Quantum computing has become a noteworthy topic in academia and industry. The multinational companies in the world have been obtaining impressive advances in all areas of quantum technology during the last two decades. These companies try to construct real quantum computers in order to exploit their theoretical preferences over today’s classical computers in practical applications. However, they are challenging to build a full-scale quantum computer because of their increased susceptibility to errors due to decoherence and other quantum noise. Therefore, quantum error correction (QEC) and fault-tolerance protocol will be essential for running quantum algorithms on large-scale quantum computers.
The overall effect of noise is modeled in terms of a set of Pauli operators and the identity acting on the physical qubits (bit flip, phase flip and a combination of bit and phase flips). In addition to Pauli errors, there is another error named leakage errors that occur when a qubit leaves the defined computational subspace. As the location of leakage errors is unknown, these can damage even more the quantum computations. Thus, this talk will briefly provide quantum error models.
Shor's algorithm is for quantum computer. Using this algorithm any arbitrarily large number can be factored in polynomial time. which is not possible in classical computer
Quantum Cryptography and Possible Attacks-slideArinto Murdopo
Quantum cryptography uses principles of quantum mechanics to securely distribute encryption keys. The BB84 protocol is a seminal quantum key distribution protocol that works as follows: Alice sends Bob polarized photons encoded with random key bits and basis choices. Bob measures the photons randomly in different bases. They communicate to discard mismatched bases, leaving a shared raw key. They test for errors from eavesdropping and apply privacy amplification to distill a final secure key. However, quantum cryptography is vulnerable to attacks like the faked-state attack, where an eavesdropper Eve blind's Bob's detectors and forces measurement outcomes to match her own. If successful, Eve can learn almost all the raw key without introducing errors.
This document discusses quantum cryptography and its advantages over traditional cryptography. It provides background on traditional public and private key cryptography and explains that quantum cryptography uses the principles of quantum mechanics to securely transmit encryption keys. The document outlines the basics of how quantum cryptography works, including using photon polarization to represent bits, and describes an example of how it could be implemented using MATLAB. It also lists some of the key companies working in the field of quantum cryptography.
This document discusses quantum error correction. It explains that while quantum states and operators are theoretically perfect, in reality approximations must be made which can cause errors. Quantum error correction deals with these imperfections. It describes different types of quantum errors and discusses barriers to quantum error correction, such as the no-cloning theorem. The document introduces classical error correction techniques and explains how similar techniques can be applied to encode quantum states to correct bit flip and phase flip errors by measuring the parity of qubits without collapsing their superpositions. Specific quantum error correcting codes are presented, including Shor's code which can correct both types of errors.
This document provides an overview of concepts from linear algebra that are necessary for understanding quantum mechanics. It reviews vectors, vector spaces, linear independence, bases, linear operators, and complex numbers. It then introduces key concepts for quantum mechanics, including Dirac notation, inner products, outer products, eigenvalues and eigenvectors, unitary and Hermitian operators, and tensor products. The goal is to cover the necessary mathematical foundations and notations systematically to enable the study of quantum mechanics postulates.
This document discusses quantum cryptography and its advantages over classical cryptography. It introduces the key distribution problem in classical cryptography. Quantum cryptography uses principles of quantum mechanics like quantum bits that cannot be copied and photon polarization to securely distribute keys. The document describes the BB84 protocol for quantum key distribution where Alice and Bob use different polarization bases to generate a random key and detect eavesdropping. While promising, challenges remain in scaling the technology to longer distances and developing affordable devices.
Shor's algorithm is for quantum computer. Using this algorithm any arbitrarily large number can be factored in polynomial time. which is not possible in classical computer
Quantum Cryptography and Possible Attacks-slideArinto Murdopo
Quantum cryptography uses principles of quantum mechanics to securely distribute encryption keys. The BB84 protocol is a seminal quantum key distribution protocol that works as follows: Alice sends Bob polarized photons encoded with random key bits and basis choices. Bob measures the photons randomly in different bases. They communicate to discard mismatched bases, leaving a shared raw key. They test for errors from eavesdropping and apply privacy amplification to distill a final secure key. However, quantum cryptography is vulnerable to attacks like the faked-state attack, where an eavesdropper Eve blind's Bob's detectors and forces measurement outcomes to match her own. If successful, Eve can learn almost all the raw key without introducing errors.
This document discusses quantum cryptography and its advantages over traditional cryptography. It provides background on traditional public and private key cryptography and explains that quantum cryptography uses the principles of quantum mechanics to securely transmit encryption keys. The document outlines the basics of how quantum cryptography works, including using photon polarization to represent bits, and describes an example of how it could be implemented using MATLAB. It also lists some of the key companies working in the field of quantum cryptography.
This document discusses quantum error correction. It explains that while quantum states and operators are theoretically perfect, in reality approximations must be made which can cause errors. Quantum error correction deals with these imperfections. It describes different types of quantum errors and discusses barriers to quantum error correction, such as the no-cloning theorem. The document introduces classical error correction techniques and explains how similar techniques can be applied to encode quantum states to correct bit flip and phase flip errors by measuring the parity of qubits without collapsing their superpositions. Specific quantum error correcting codes are presented, including Shor's code which can correct both types of errors.
This document provides an overview of concepts from linear algebra that are necessary for understanding quantum mechanics. It reviews vectors, vector spaces, linear independence, bases, linear operators, and complex numbers. It then introduces key concepts for quantum mechanics, including Dirac notation, inner products, outer products, eigenvalues and eigenvectors, unitary and Hermitian operators, and tensor products. The goal is to cover the necessary mathematical foundations and notations systematically to enable the study of quantum mechanics postulates.
This document discusses quantum cryptography and its advantages over classical cryptography. It introduces the key distribution problem in classical cryptography. Quantum cryptography uses principles of quantum mechanics like quantum bits that cannot be copied and photon polarization to securely distribute keys. The document describes the BB84 protocol for quantum key distribution where Alice and Bob use different polarization bases to generate a random key and detect eavesdropping. While promising, challenges remain in scaling the technology to longer distances and developing affordable devices.
This document provides an overview of quantum cryptography. It introduces key concepts like the Heisenberg uncertainty principle, photon polarization, and the need for quantum cryptography due to potential threats from quantum computers. The document describes how quantum key distribution works using protocols like BB84 to generate and test secure encryption keys between two parties by detecting any eavesdropping. It notes that working prototypes have been implemented over fiber optic cables and open air.
This document provides an overview of continuous variable quantum cryptography (CVQKD). It discusses how CVQKD works at a medium range of ~25km with medium rates of a few kbit/s, offering less security than single-photon QKD but more potential for improvement. The document reviews the theoretical basis of CVQKD in terms of field quadratures, homodyne detection, information theory, and how the protocol encodes a secret key. It also summarizes the progress made in improving CVQKD protocols and increasing their security over the last 10 years based on theoretical work. Finally, it mentions the development of 1st generation CVQKD experimental demonstrators.
Quantum key distribution allows Alice and Bob to securely share a secret key using quantum properties of photons. In the BB84 protocol, Alice randomly encodes photons in one of two bases and sends them to Bob. Bob measures in a randomly chosen basis. They discard mismatched results and use the remaining bits as a secure key. An eavesdropper like Eve cannot intercept the photons without introducing errors, revealing her presence. This allows Alice and Bob to detect eavesdropping and ensure the secrecy of their shared key.
This presentation provides an overview of quantum cryptography. It begins by defining classical cryptography and introducing the idea of public key cryptography. It then explains how quantum cryptography works using polarized photons to securely distribute a key between two parties. The method described is BB84, which uses randomly polarized photons and basis sets to encode information and detect eavesdropping based on error rates. Real-world implementations of quantum cryptography over fiber optic cables up to 150km are mentioned. In summary, quantum cryptography provides unconditional secure key distribution through properties of quantum mechanics such as photon polarization.
[01] Quantum Error Correction for Beginners Shin Nishio
This document provides an introduction to quantum error correction. It discusses the types of quantum errors including coherent errors and environmental decoherence. It then describes the 3-qubit error correction code, which can correct one bit flip error by using syndrome measurements. Finally, it covers the 9-qubit code developed by Shor, which can correct both one bit flip and one phase flip error by combining 3-qubit codes and independently correcting for bit flip and phase flip errors.
QUANTUM ERROR CORRECTING CODES-Interaction between a quantum system with environment cause undesirable changes in the state of the quantum system. In the case qubits, they appears bit-flip and phase flip errors. To reduce such errors, we must build in some sort of error correcting mechanism in the algorithm
This document provides an introduction to quantum cryptography. It discusses how quantum cryptography solves the key distribution problem faced by conventional cryptography through the use of polarized photons and quantum properties like the Heisenberg uncertainty principle. The document summarizes the BB84 quantum key distribution protocol developed by Bennett and Brassard, in which Alice and Bob use randomly polarized photons to generate an encryption key. It also discusses some challenges for practical quantum cryptography implementations, like developing single photon sources and detectors and transmitting photons over long distances.
Quantum Key Distribution Meetup Slides (Updated)Kirby Linvill
Quantum key distribution (QKD) uses quantum mechanics to establish secure encryption keys between two parties. The BB84 protocol is an example of how it works: Alice sends Bob polarized photons encoded in random bases. Bob measures in a random basis, and they later disclose their bases to keep the results where they matched. This allows detection of eavesdropping, since an eavesdropper would introduce errors. While providing security against future computers, current QKD has limitations like vulnerability to attacks on the classical channel and practical difficulties generating single photons. Overall it demonstrates how quantum effects can offer information-theoretic security for encryption.
A brief presentation on Position-Based, Device-Independent and Post Quantum Cryptographies. Detailing Position-Based QC, defining Device-Independent QC and discussing Post Device-Independent.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states rather than just 1s and 0s. This allows quantum computers to perform exponentially more calculations in parallel than classical computers. Some of the main challenges to building quantum computers are preventing qubit decoherence from environmental interference, developing effective error correction methods, and observing outputs without corrupting data. Quantum computers may one day be able to break current encryption methods and solve optimization problems much faster than classical computers.
Grover's algorithm - Introduction to quantum computingMeir TOLEDANO
This document provides an introduction to Grover's algorithm, a quantum algorithm that allows for faster searching of an unstructured database. It explains some key concepts in quantum computing like superposition, entanglement, and quantum gates. Grover's algorithm uses amplitude amplification to find a target item in a list of N items using only O(sqrt(N)) operations, providing a speedup over classical algorithms. The document analyzes how Grover's algorithm works and its complexity, and discusses some physical implementations of quantum computers.
Quantum computing is a new paradigm that utilizes quantum mechanics phenomena like superposition and entanglement. It has the potential to solve certain problems exponentially faster than classical computers by using qubits that can be in superposition of states. Some key applications are factoring, simulation, and optimization problems. However, building large-scale quantum computers faces challenges like preventing decoherence of qubits and developing error correction techniques. While still in development, quantum computing could revolutionize fields like encryption, communication, and material science in the future through a hybrid model combining classical and quantum processing.
I will explain why quantum computing is interesting, how it works and what you actually need to build a working quantum computer. I will use the superconducting two-qubit quantum processor I built during my PhD as an example to explain its basic building blocks. I will show how we used this processor to achieve so-called quantum speed-up for a search algorithm that we ran on it. Finally, I will give a short overview of the current state of superconducting quantum computing and Google's recently announced effort to build a working quantum computer in cooperation with one of the leading research groups in this field.
Quantum Cryptography is the one of the most successul application of quantum computing/information theory.
cryptography is the coding and decoding of secret messages.
Quantum Key Distribution uses the laws of quantum mechanics, we can distribute keys in perfect secrecy.
Quantum cryptography uses principles of quantum mechanics like quantum entanglement and the Heisenberg uncertainty principle to securely distribute encryption keys. It works by having Alice send individual photons encoded with bits to Bob, who measures them. They later communicate to discard any bits where their bases did not align. This prevents eavesdropping by Eve without introducing errors, allowing detection. After error correction and privacy amplification, the key can be used for encryption with perfect security. Quantum cryptography thus provides a secure way to transmit encryption keys.
Quantum cryptography uses principles of quantum mechanics to guarantee secure communication. It allows two parties to generate a shared random key that can be used to encrypt and decrypt messages. There are two main approaches - using polarized photons or entangled photons. Information reconciliation and privacy amplification protocols are used to ensure the keys between the two parties are identical and an eavesdropper gains no information. While traditional man-in-the-middle attacks are impossible, future work aims to increase transmission distances including to satellites. Several research groups and companies are conducting research on quantum cryptography.
Seminar Report on Quantum Key DistributionShahrikh Khan
This document is a seminar report submitted by Shahrukh A. Khan to the Department of Computer Engineering at SSBT's College of Engineering and Technology in partial fulfillment of the requirements for a Bachelor of Engineering degree. The report discusses quantum key distribution, with sections introducing classical cryptography, reviewing related work, describing the methodology used, discussing implementation and applications, and concluding. The report was completed under the guidance of Mr. M.E. Patil in 2015.
This document presents an overview of uncertainty in artificial intelligence, specifically focusing on fuzzy logic. It defines fuzzy logic as a way to deal with imprecise and vague information using degrees of truth rather than binary logic. The key concepts discussed include fuzzy sets and their operations, fuzzy rules and inferences, and applications of fuzzy logic systems. Examples are provided to illustrate fuzzy sets for describing tall men and an air conditioning system controlled by a fuzzy logic controller.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states and become entangled. Shor's algorithm shows how a quantum computer could factor large numbers much faster than a classical computer by using quantum parallelism and the quantum Fourier transform. It works by first preparing the input in a superposition, applying a modular exponentiation operation, measuring the output qubit to partially collapse the input, applying a quantum Fourier transform to reveal periodicity, and using the period to determine the factors of the original number. This algorithm demonstrates the power of quantum computing for certain problems.
This document provides an overview of quantum cryptography. It introduces key concepts like the Heisenberg uncertainty principle, photon polarization, and the need for quantum cryptography due to potential threats from quantum computers. The document describes how quantum key distribution works using protocols like BB84 to generate and test secure encryption keys between two parties by detecting any eavesdropping. It notes that working prototypes have been implemented over fiber optic cables and open air.
This document provides an overview of continuous variable quantum cryptography (CVQKD). It discusses how CVQKD works at a medium range of ~25km with medium rates of a few kbit/s, offering less security than single-photon QKD but more potential for improvement. The document reviews the theoretical basis of CVQKD in terms of field quadratures, homodyne detection, information theory, and how the protocol encodes a secret key. It also summarizes the progress made in improving CVQKD protocols and increasing their security over the last 10 years based on theoretical work. Finally, it mentions the development of 1st generation CVQKD experimental demonstrators.
Quantum key distribution allows Alice and Bob to securely share a secret key using quantum properties of photons. In the BB84 protocol, Alice randomly encodes photons in one of two bases and sends them to Bob. Bob measures in a randomly chosen basis. They discard mismatched results and use the remaining bits as a secure key. An eavesdropper like Eve cannot intercept the photons without introducing errors, revealing her presence. This allows Alice and Bob to detect eavesdropping and ensure the secrecy of their shared key.
This presentation provides an overview of quantum cryptography. It begins by defining classical cryptography and introducing the idea of public key cryptography. It then explains how quantum cryptography works using polarized photons to securely distribute a key between two parties. The method described is BB84, which uses randomly polarized photons and basis sets to encode information and detect eavesdropping based on error rates. Real-world implementations of quantum cryptography over fiber optic cables up to 150km are mentioned. In summary, quantum cryptography provides unconditional secure key distribution through properties of quantum mechanics such as photon polarization.
[01] Quantum Error Correction for Beginners Shin Nishio
This document provides an introduction to quantum error correction. It discusses the types of quantum errors including coherent errors and environmental decoherence. It then describes the 3-qubit error correction code, which can correct one bit flip error by using syndrome measurements. Finally, it covers the 9-qubit code developed by Shor, which can correct both one bit flip and one phase flip error by combining 3-qubit codes and independently correcting for bit flip and phase flip errors.
QUANTUM ERROR CORRECTING CODES-Interaction between a quantum system with environment cause undesirable changes in the state of the quantum system. In the case qubits, they appears bit-flip and phase flip errors. To reduce such errors, we must build in some sort of error correcting mechanism in the algorithm
This document provides an introduction to quantum cryptography. It discusses how quantum cryptography solves the key distribution problem faced by conventional cryptography through the use of polarized photons and quantum properties like the Heisenberg uncertainty principle. The document summarizes the BB84 quantum key distribution protocol developed by Bennett and Brassard, in which Alice and Bob use randomly polarized photons to generate an encryption key. It also discusses some challenges for practical quantum cryptography implementations, like developing single photon sources and detectors and transmitting photons over long distances.
Quantum Key Distribution Meetup Slides (Updated)Kirby Linvill
Quantum key distribution (QKD) uses quantum mechanics to establish secure encryption keys between two parties. The BB84 protocol is an example of how it works: Alice sends Bob polarized photons encoded in random bases. Bob measures in a random basis, and they later disclose their bases to keep the results where they matched. This allows detection of eavesdropping, since an eavesdropper would introduce errors. While providing security against future computers, current QKD has limitations like vulnerability to attacks on the classical channel and practical difficulties generating single photons. Overall it demonstrates how quantum effects can offer information-theoretic security for encryption.
A brief presentation on Position-Based, Device-Independent and Post Quantum Cryptographies. Detailing Position-Based QC, defining Device-Independent QC and discussing Post Device-Independent.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states rather than just 1s and 0s. This allows quantum computers to perform exponentially more calculations in parallel than classical computers. Some of the main challenges to building quantum computers are preventing qubit decoherence from environmental interference, developing effective error correction methods, and observing outputs without corrupting data. Quantum computers may one day be able to break current encryption methods and solve optimization problems much faster than classical computers.
Grover's algorithm - Introduction to quantum computingMeir TOLEDANO
This document provides an introduction to Grover's algorithm, a quantum algorithm that allows for faster searching of an unstructured database. It explains some key concepts in quantum computing like superposition, entanglement, and quantum gates. Grover's algorithm uses amplitude amplification to find a target item in a list of N items using only O(sqrt(N)) operations, providing a speedup over classical algorithms. The document analyzes how Grover's algorithm works and its complexity, and discusses some physical implementations of quantum computers.
Quantum computing is a new paradigm that utilizes quantum mechanics phenomena like superposition and entanglement. It has the potential to solve certain problems exponentially faster than classical computers by using qubits that can be in superposition of states. Some key applications are factoring, simulation, and optimization problems. However, building large-scale quantum computers faces challenges like preventing decoherence of qubits and developing error correction techniques. While still in development, quantum computing could revolutionize fields like encryption, communication, and material science in the future through a hybrid model combining classical and quantum processing.
I will explain why quantum computing is interesting, how it works and what you actually need to build a working quantum computer. I will use the superconducting two-qubit quantum processor I built during my PhD as an example to explain its basic building blocks. I will show how we used this processor to achieve so-called quantum speed-up for a search algorithm that we ran on it. Finally, I will give a short overview of the current state of superconducting quantum computing and Google's recently announced effort to build a working quantum computer in cooperation with one of the leading research groups in this field.
Quantum Cryptography is the one of the most successul application of quantum computing/information theory.
cryptography is the coding and decoding of secret messages.
Quantum Key Distribution uses the laws of quantum mechanics, we can distribute keys in perfect secrecy.
Quantum cryptography uses principles of quantum mechanics like quantum entanglement and the Heisenberg uncertainty principle to securely distribute encryption keys. It works by having Alice send individual photons encoded with bits to Bob, who measures them. They later communicate to discard any bits where their bases did not align. This prevents eavesdropping by Eve without introducing errors, allowing detection. After error correction and privacy amplification, the key can be used for encryption with perfect security. Quantum cryptography thus provides a secure way to transmit encryption keys.
Quantum cryptography uses principles of quantum mechanics to guarantee secure communication. It allows two parties to generate a shared random key that can be used to encrypt and decrypt messages. There are two main approaches - using polarized photons or entangled photons. Information reconciliation and privacy amplification protocols are used to ensure the keys between the two parties are identical and an eavesdropper gains no information. While traditional man-in-the-middle attacks are impossible, future work aims to increase transmission distances including to satellites. Several research groups and companies are conducting research on quantum cryptography.
Seminar Report on Quantum Key DistributionShahrikh Khan
This document is a seminar report submitted by Shahrukh A. Khan to the Department of Computer Engineering at SSBT's College of Engineering and Technology in partial fulfillment of the requirements for a Bachelor of Engineering degree. The report discusses quantum key distribution, with sections introducing classical cryptography, reviewing related work, describing the methodology used, discussing implementation and applications, and concluding. The report was completed under the guidance of Mr. M.E. Patil in 2015.
This document presents an overview of uncertainty in artificial intelligence, specifically focusing on fuzzy logic. It defines fuzzy logic as a way to deal with imprecise and vague information using degrees of truth rather than binary logic. The key concepts discussed include fuzzy sets and their operations, fuzzy rules and inferences, and applications of fuzzy logic systems. Examples are provided to illustrate fuzzy sets for describing tall men and an air conditioning system controlled by a fuzzy logic controller.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states and become entangled. Shor's algorithm shows how a quantum computer could factor large numbers much faster than a classical computer by using quantum parallelism and the quantum Fourier transform. It works by first preparing the input in a superposition, applying a modular exponentiation operation, measuring the output qubit to partially collapse the input, applying a quantum Fourier transform to reveal periodicity, and using the period to determine the factors of the original number. This algorithm demonstrates the power of quantum computing for certain problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states and become entangled. Shor's algorithm shows how a quantum computer could factor large numbers much faster than a classical computer by using quantum parallelism and the quantum Fourier transform. It works by first preparing the input in a superposition, applying a modular exponentiation operation, measuring the output qubit to partially collapse the input, applying a quantum Fourier transform to reveal periodicity, and using the period to determine the factors of the original number. This algorithm demonstrates the power of quantum computing for certain problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
Quantum computing uses quantum mechanics principles to perform calculations. A qubit can represent a 1, 0, or superposition of both simultaneously. Operations are performed by reversible logic gates like CNOT. Shor's algorithm shows quantum computers can factor large numbers faster by using quantum parallelism and Fourier transforms to find the period of a function, revealing the factors. While progress is being made, challenges remain in building larger quantum computers and developing new algorithms to solve other hard problems.
The basics of quantum computing, associated mathematics, DJ algorithms and coding details are covered.
These slides are used in my videos https://youtu.be/6o2jh25lrmI, https://youtu.be/Wj73E4pObRk, https://youtu.be/OkFkSXfGawQ and https://youtu.be/OkFkSXfGawQ
This document discusses quantum error correction. It begins by explaining the need for quantum error correction due to noise and imperfections in real-world quantum systems. It then discusses barriers to quantum error correction like the no-cloning theorem. Different types of quantum errors like bit flips, phase flips, and more complex errors are described. Classical error correction techniques are compared. Finally, specific quantum error correcting codes like the repetition code, phase flip code, and Shor's code are explained as ways to protect quantum information against noise by encoding quantum states.
The document discusses different techniques for clipping lines and polygons to a viewing window or clipping region.
It describes line clipping algorithms like Cohen-Sutherland that use outcodes to quickly reject lines outside the clipping region or clip lines intersecting the boundary. It also discusses the midpoint subdivision algorithm for line clipping.
For polygon clipping, it explains the Sutherland-Hodgeman algorithm which clips polygons against each window edge one by one, dividing the polygon into smaller clipped polygons inside the viewing region.
Classic computing uses bits that have values of 0 and 1 and computations are performed by modifying sequences of bits. Quantum computing uses qubits that can be in superposition of states represented by complex numbers, allowing multiple computations to occur concurrently. Qubits can become entangled, linking their probabilities, and their phases can be shifted to amplify or dampen interference during measurement. Quantum algorithms harness these quantum effects like superposition and entanglement to solve certain problems significantly faster than classical algorithms.
Descripcion about IBM quantum experience. In this presentation I introduce the IBM Tools for quantum programming. Also it serves as a general introduction to Quantum Computing
QX Simulator and quantum programming - 2020-04-28Aritra Sarkar
This document discusses quantum computing simulation and quantum programming. It notes that directly simulating large quantum systems requires exponential resources, but that smart simulation techniques can reduce these requirements. It introduces the QX quantum computing simulator, including its syntax, functionality for noisy circuits, classical control, and parallelism. The document provides examples of simulating simple circuits and algorithms to demonstrate the QX simulator's capabilities.
This document discusses best practices for conducting and reporting on computational fluid dynamics (CFD) analyses to achieve credible and confident results. It emphasizes the importance of verification and validation to demonstrate acceptable levels of error and uncertainty. It provides guidance on quantifying various sources of error in CFD simulations and outlines recommended steps for grid convergence studies, reporting results, and validating simulations against experimental data.
Quantum computing uses quantum bits (qubits) that can exist in superpositions of states. A controlled-NOT (CN) gate inverts the target qubit if the control qubit is 1. A controlled-controlled-NOT (CCN) gate inverts the target qubit if both control qubits are 1. Shor's algorithm uses quantum Fourier transforms and modular exponentiation to factor integers into prime factors exponentially faster than classical computers. It finds the period of the function x raised to a power (mod N), from which the factors can be derived.
Artificial Intelligence (AI), specifically deep learning, is revolutionizing industries, products, and core capabilities by delivering dramatically enhanced experiences. However, the deep neural networks of today use too much memory, compute, and energy. Plus, to make AI truly ubiquitous, networks need to run on the end device within a tight power and thermal budget. One approach to help address these issues is quantization, which attempts to reduce the number of bits used for weight parameters and activation calculations without sacrificing model accuracy. This presentation covers: why quantization is important, existing quantization challenges, Qualcomm AI Research's existing quantization research, and how developers and researchers can take advantage of quantization on Qualcomm Snapdragon.
Similar to Fault-tolerance Quantum computation and Quantum Error Correction (20)
"El álgebra lineal es una herramienta fundamental en muchos campos de la ciencia y la tecnología. Es particularmente importante en la física, la ingeniería, la informática y la estadística. La capacidad de manipular eficientemente grandes cantidades de datos y matrices complejas es esencial en estas áreas para la resolución de problemas y la toma de decisiones.
A priori, puede dar la sensación de que estamos muy lejos del uso del álgebra lineal en nuestro día a día. Sin embargo, algunas técnicas como la descomposición en valores singulares y la regresión lineal para entrenar modelos y hacer predicciones precisas están detrás de la inteligencia artificial y el aprendizaje automático. ¿Te suena ChatGPT? Puede no parecerlo, pero el álgebra lineal también está detrás en algunos de sus procesos. Por este motivo, debemos seguir trabajando en este campo, ya que su importancia seguirá creciendo a medida que se generen y analicen grandes cantidades de datos en el mundo actual.
"
La pandemia de COVID-19 ha supuesto una proliferación de mapas y contramapas. Por ello, organizaciones de la sociedad civil y movimientos sociales han generado sus propias interpretaciones y representaciones de los datos sobre la crisis. Estos también han contribuido a visibilizar aspectos, sujetos y temas que han sido desatendidos o infrarrepresentados en las visualizaciones hegemónicas y dominantes. En este contexto, la presente ponencia se centra en el análisis de los imaginarios sociales relacionados con la elaboración de mapas durante la pandemia. Es decir, trata de indagar en la importancia de los mapas para el activismo digital, las potencialidades que se extraen de esta tecnología y los valores asociados a las visualizaciones creadas con ellos. El objetivo último es reflexionar sobre la vía emergente del activismo de datos, así como sobre la intersección entre los imaginarios sociales y la geografía digital.
Designing RISC-V-based Accelerators for next generation Computers (DRAC) is a 3-year project (2019-2022) funded by the ERDF Operational Program of Catalonia 2014-2020. DRAC will design, verify, implement and fabricate a high performance general purpose processor that will incorporate different accelerators based on the RISC-V technology, with specific applications in the field of post-quantum security, genomics and autonomous navigation. In this talk, we will provide an overview of the main achievements in the DRAC project, including the fabrication of Lagarto, the first RISC-V processor developed in Spain.
This talk will begin introducing the uElectronics section of ESA at ESTEC and the general activities the group is responsible for. Then, it will go through some of the R+D on-going activities that the group is involved with, hand in hand with universities and/or companies. One of the major ones is related to the European rad-hard FPGAs that have been partially founded by ESA for several years and that will be playing a major role in the sector in the upcoming years. It´s also worth talking about the RTL soft IPs that are currently under development and that will allow us to keep on providing the European ecosystem with some key capabilities. The latter will be an overview of RISC-V space hardened on-going activities that might be replacing the current SPARC based processors available for our missions.
El objetivo de esta charla es presentar las últimas novedades incorporadas en la arquitectura ARM y describir las tendencias en la microarquitectura de los procesadores con arquitectura ARM. ARM es una empresa relativamente pequeña en comparación con otros gigantes del sector tecnológico. Sin embargo, la amplia implantación de su arquitectura, siendo ampliamente dominante en algunos sectores, y sus microarquitecturas, hacen que la tecnología ARM ocupe un lugar central en el desarrollo tecnológico del mundo actual. La tecnología ARM está presente prácticamente en todo el espectro tecnológico, desde los dispositivos más sencillos hasta el HPC y Cloud computing, pasando por los smartphones, automoción electrónica de consumo, etc
"Formal verification has been used by computer scientists for decades to prevent
software bugs. However, with a few exceptions, it has not been used by researchers
working in most areas of mathematics (geometry, algebra, analysis, etc.). In this
talk, we will discuss how this has changed in the past few years, and the possible
implications to the future of mathematical research, teaching and communication.
We will focus on the theorem prover Lean and its mathematical library
mathlib, since this is currently the system most widely used by mathematicians.
Lean is a functional programming language and interactive theorem prover based
on dependent type theory, with proof irrelevance and non-cumulative universes.
The mathlib library, open-source and designed as a basis for research level
mathematics, is one of the largest collections of formalized mathematics. It allows
classical reasoning, uses large- and small-scale automation, and is characterized
by its decentralized nature with over 200 contributors, including both computer
scientists and mathematicians."
"Part of the research community thinks that it is still early to tackle the development of quantum software engineering techniques. The reason is that how the quantum computers of the future will look like is still unknown. However, there are some facts that we can affirm today: 1) quantum and classical computers will coexist, each dedicated to the tasks at which they are most efficient. 2) quantum computers will be part of the cloud infrastructure and will be accessible through the Internet. 3) complex software systems will be made up of smaller pieces that will collaborate with each other. 4) some of those pieces will be quantum, therefore the systems of the future will be hybrid. 5) the coexistence and interaction between the components of said hybrid systems will be supported by service composition: quantum services.
This talk analyzes the challenges that the integration of quantum services poses to Service Oriented Computing."
In this talk, after a brief overview of AI concepts in particular Machine Learning (ML) techniques, some of the well-known computer design concepts for high performance and power efficiency are presented. Subsequently, those techniques that have had a promising impact for computing ML algorithms are discussed. Deep learning has emerged as a game changer for many applications in various fields of engineering and medical sciences. Although the primary computation function is matrix vector multiplication, many competing efficient implementations of this primary function have been proposed and put into practice. This talk will review and compare some of those techniques that are used for ML computer design.
Tras una breve introducción a la informática médica y unas pinceladas sobre conceptos prácticos de Inteligencia Artificial (posible definición consensuada, strong VS weak AI y técnicas y métodos comúnmente empleados), el bloque central de la charla muestra ejemplos prácticos (en forma de casos de éxito) de distintos desarrollos llevados a cabo por el grupo de Sistemas Informáticos de Nueva Generación (SING: http//sing-group.org/) en los ámbitos de (i) Informática clínica (InNoCBR, PolyDeep), (ii) Informática para investigación clínica (PathJam, WhichGenes), (iii) bioinformática traslacional (Genómica: ALTER, Proteómica: DPD, BI, BS, Mlibrary, Mass-Up, e integración de datos ÓMICOS: PunDrugs) y (iv) Informática en salud pública (CURMIS4th). Finalmente, se comenta brevemente la importancia que se espera tenga en un futuro inmediato la IA interpretable (XAI, Explainable Artificial Intelligence) y la participación humana (HITL. Human-In-The-Loop). La charla termina con una breve reflexión sobre las lecciones aprendidas por el ponente después de más de 16 años de desarrollo de sistemas inteligentes en el ámbito de la informática médica.
Many emerging applications require methods tailored towards high-speed data acquisition and filtering of streaming data followed by offline event reconstruction and analysis. In this case, the main objective is to relieve the immense pressure on the storage and communication resources within the experimental infrastructure. In other applications, ultra low latency real time analysis is required for autonomous experimental systems and anomaly detection in acquired scientific data in the absence of any prior data model for unknown events. At these data rates, traditional computing approaches cannot carry out even cursory analyses in a time frame necessary to guide experimentation. In this talk, Prof. Ogrenci will present some examples of AI hardware architectures. She will discuss the concept of co-design, which makes the unique needs of an application domain transparent to the hardware design process and present examples from three applications: (1) An in-pixel AI chip built using the HLS methodology; (2) A radiation hardened ASIC chip for quantum systems; (3) An FPGA-based edge computing controller for real-time control of a High Energy Physics experiment.
En esta conferencia se presentará una revisión del concepto de autonomía para robots móviles de campo y la identificación de desafíos para lograr un verdadero sistema autónomo, además de sugerir posibles direcciones de investigación. Los sistemas robóticos inteligentes, por lo general, obtienen conocimiento de sus funciones y del entorno de trabajo en etapa de diseño y desarrollo. Este enfoque no siempre es eficiente, especialmente en entornos semiestructurados y complejos como puede ser el campo de cultivo. Un sistema robótico verdaderamente autónomo debería desarrollar habilidades que le permitan tener éxito en tales entornos sin la necesidad de tener a-priori un conocimiento ontológico del área de trabajo y la definición de un conjunto de tareas o comportamientos predefinidos. Por lo que en esta conferencia se presentarán posibles estrategias basadas en Inteligencia Artificial que permitan perfeccionar las capacidades de navegación de robots móviles y que sean capaces de ofrecer un nivel de autonomía lo suficientemente elevado para poder ejecutar todas las tareas dentro de una misión casa-a-casa (home-to-home).
Los chatbots son un elemento clave en la transformación digital de nuestra sociedad. Están por todas partes: eCommerce, salud digital, asistencia a clientes, turismo,... Pero si habéis usado alguno, probablemente os habrá decepcionado. Lo confieso, la mayoría de los chatbots que existen son muy malos. Y es que no es nada fácil hacer un chatbot que sea realmente útil e inteligente. Un chatbot combina toda la complejidad de la ingeniería de software con la del procesamiento de lenguaje natural. Pensad que muchos chatbots hay que desplegarlos en varios canales (web, telegram, slack,...) y a menudo tienen que utilizar APIs y servicios externos, acceder a bases de datos internas o integrar modelos de lenguaje preentrenados (por ej. detectores de toxicidad), etc. Y el problema no es sólo crear el bot, si no también probarlo y evolucionarlo. En esta charla veremos los mayores desafíos a los que hay que enfrentarse cuando nos encargan un proyecto de desarrollo que incluye un chatbot y qué técnicas y estrategias podemos ir aplicando en función de las necesidades del proyecto, para conseguir, esta vez sí un chatbot que sepa de lo que habla.
Many HPC applications are massively parallel and can benefit from the spatial parallelism offered by reconfigurable logic. While modern memory technologies can offer high bandwidth, designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. Addressing these challenges requires to combine compiler optimizations, high-level synthesis, and hardware design.
In this talk, I will present challenges, solutions, and trends for generating massively parallel accelerators on FPGA for high-performance computing. These architectures can provide performance comparable to software implementations on high-end processors, and much higher energy efficiency thanks to logic customization.
The main challenge of concurrent software verification has always been in achieving modularity, i.e., the ability to divide and conquer the correctness proofs with the goal of scaling the verification effort. Types are a formal method well-known for its ability to modularize programs, and in the case of dependent types, the ability to modularize and scale complex mathematical proofs.
In this talk I will present our recent work towards reconciling dependent types with shared memory concurrency, with the goal of achieving modular proofs for the latter. Applying the type-theoretic paradigm to concurrency has lead us to view separation logic as a type theory of state, and has motivated novel abstractions for expressing concurrency proofs based on the algebraic structure of a resource and on structure-preserving functions (i.e., morphisms) between resources.
Microarchitectural attacks, such as Spectre and Meltdown, are a class of
security threats that affect almost all modern processors. These attacks exploit the side-effects resulting from processor optimizations to leak sensitive information and compromise a system’s security.
Over the years, a large number of hardware and software mechanisms for
preventing microarchitectural leaks have been proposed. Intuitively, more
defensive mechanisms are less efficient, while more permissive mechanisms may offer more performance but require more defensive programming. Unfortunately, there are no
hardware-software contracts that would turn this intuition into a basis for
principled co-design.
In this talk, we present a framework for specifying hardware/software security
contracts, an abstraction that captures a processor’s security guarantees in a
simple, mechanism-independent manner by specifying which program executions a
microarchitectural attacker can distinguish.
La aparición de vulnerabilidades por la falta de controles de seguridad es una de las causas por las que se demandan nuevos marcos de trabajo que produzcan software seguro de forma predeterminada. En la conferencia se abordará cómo transformar el proceso de desarrollo de software dando la importancia que merece la seguridad desde el inicio del ciclo de vida. Para ello se propone un nuevo modelo de desarrollo – modelo Viewnext-UEx – que incorpora prácticas de seguridad de forma preventiva y sistemática en todas las fases del proceso de ciclo de vida del software. El propósito de este nuevo modelo es anticipar la detección de vulnerabilidades aplicando la seguridad desde las fases más tempranas, a la vez que se optimizan los procesos de construcción del software. Se exponen los resultados de un escenario preventivo, tras la aplicación del modelo Viewnext-UEx, frente al escenario reactivo tradicional de aplicar la seguridad a partir de la fase de testing.
This document discusses trusting artificial intelligence systems. It begins with an overview of trust in social and computing contexts. It then discusses artificial intelligence, including machine learning, deep learning, and natural language processing. It details how AI systems can be attacked, including adversarial inputs, data poisoning, and model stealing. It raises important discussions around using AI in contexts like cybersecurity, medicine, transportation, and sentiment analysis, and the challenges of ensuring systems can be trusted.
El uso de energías renovables es clave para cumplir los objetivos de desarrollo sostenible de la Agenda 2030. Entre estas energías, la eólica es la segunda más utilizada debido a su alta eficiencia. Algunos estudios sugieren que la energía eólica será la principal fuente de generación en 2050. Por ello es conveniente seguir investigando en la aplicación de técnicas de control avanzadas en estos sistemas.
Entre estas técnicas avanzadas cabe destacar las redes neuronales y el aprendizaje por refuerzo combinadas con estrategias clásicas de control. Estas técnicas ya se han empleado con éxito en el modelado y el control de sistemas complejos.
Esta conferencia presentará la aplicación de redes neuronales y aprendizaje por refuerzo al control de aerogeneradores, centrándolo especialmente en el control de pitch. Se detallarán diferentes configuraciones con redes neuronales y otras técnicas aplicadas al control de pitch. Finalmente se propondrán algunas técnicas híbridas que combinen lógica difusa, tablas de búsqueda y redes neuronales, mostrando resultados que han permitido probar su utilidad para mejorar la eficiencia de las turbinas eólicas.
As the world's energy demand rises, so does the amount of renewable energy, particularly wind energy, in the supply. The life cycle of wind farms starting from manufacturing the components to decommission stage involve significant involvement of cost and the application of AI and data analytics are on reducing these costs are limited. With this conference talk, the audience expected to know some of the interesting applications of AI and data analytics on offshore wind. And, also highlight the future challenges and opportunities. This conference could be useful for students, academics and researcher who want to make next career in offshore wind but yet know where to start.
This document discusses the evolution of edge AI systems and architectures for the Internet of Things (IoT) era. It describes how IoT has transitioned from simple wireless sensor networks to complex systems that converge digitized enterprise data with edge AI sensors and deep learning analytics. Edge AI moves intelligence closer to IoT devices by enabling real-time data processing and filtering at the network edge. This reduces data transmission costs and latency. The document outlines several examples of edge AI applications in healthcare, smart homes, and industry that analyze sensor data in real-time to provide personalized and energy efficient services. It also discusses how new edge AI hardware platforms and open-source systems are enabling more customized and affordable IoT solutions.
AI for Legal Research with applications, toolsmahaffeycheryld
AI applications in legal research include rapid document analysis, case law review, and statute interpretation. AI-powered tools can sift through vast legal databases to find relevant precedents and citations, enhancing research accuracy and speed. They assist in legal writing by drafting and proofreading documents. Predictive analytics help foresee case outcomes based on historical data, aiding in strategic decision-making. AI also automates routine tasks like contract review and due diligence, freeing up lawyers to focus on complex legal issues. These applications make legal research more efficient, cost-effective, and accessible.
Prediction of Electrical Energy Efficiency Using Information on Consumer's Ac...PriyankaKilaniya
Energy efficiency has been important since the latter part of the last century. The main object of this survey is to determine the energy efficiency knowledge among consumers. Two separate districts in Bangladesh are selected to conduct the survey on households and showrooms about the energy and seller also. The survey uses the data to find some regression equations from which it is easy to predict energy efficiency knowledge. The data is analyzed and calculated based on five important criteria. The initial target was to find some factors that help predict a person's energy efficiency knowledge. From the survey, it is found that the energy efficiency awareness among the people of our country is very low. Relationships between household energy use behaviors are estimated using a unique dataset of about 40 households and 20 showrooms in Bangladesh's Chapainawabganj and Bagerhat districts. Knowledge of energy consumption and energy efficiency technology options is found to be associated with household use of energy conservation practices. Household characteristics also influence household energy use behavior. Younger household cohorts are more likely to adopt energy-efficient technologies and energy conservation practices and place primary importance on energy saving for environmental reasons. Education also influences attitudes toward energy conservation in Bangladesh. Low-education households indicate they primarily save electricity for the environment while high-education households indicate they are motivated by environmental concerns.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Software Engineering and Project Management - Introduction, Modeling Concepts...Prakhyath Rai
Introduction, Modeling Concepts and Class Modeling: What is Object orientation? What is OO development? OO Themes; Evidence for usefulness of OO development; OO modeling history. Modeling
as Design technique: Modeling, abstraction, The Three models. Class Modeling: Object and Class Concept, Link and associations concepts, Generalization and Inheritance, A sample class model, Navigation of class models, and UML diagrams
Building the Analysis Models: Requirement Analysis, Analysis Model Approaches, Data modeling Concepts, Object Oriented Analysis, Scenario-Based Modeling, Flow-Oriented Modeling, class Based Modeling, Creating a Behavioral Model.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Use PyCharm for remote debugging of WSL on a Windo cf5c162d672e4e58b4dde5d797...shadow0702a
This document serves as a comprehensive step-by-step guide on how to effectively use PyCharm for remote debugging of the Windows Subsystem for Linux (WSL) on a local Windows machine. It meticulously outlines several critical steps in the process, starting with the crucial task of enabling permissions, followed by the installation and configuration of WSL.
The guide then proceeds to explain how to set up the SSH service within the WSL environment, an integral part of the process. Alongside this, it also provides detailed instructions on how to modify the inbound rules of the Windows firewall to facilitate the process, ensuring that there are no connectivity issues that could potentially hinder the debugging process.
The document further emphasizes on the importance of checking the connection between the Windows and WSL environments, providing instructions on how to ensure that the connection is optimal and ready for remote debugging.
It also offers an in-depth guide on how to configure the WSL interpreter and files within the PyCharm environment. This is essential for ensuring that the debugging process is set up correctly and that the program can be run effectively within the WSL terminal.
Additionally, the document provides guidance on how to set up breakpoints for debugging, a fundamental aspect of the debugging process which allows the developer to stop the execution of their code at certain points and inspect their program at those stages.
Finally, the document concludes by providing a link to a reference blog. This blog offers additional information and guidance on configuring the remote Python interpreter in PyCharm, providing the reader with a well-rounded understanding of the process.
3. What is quantum computer?
• A quantum computer is a machine that relies on
quantum phenomena like superposition, quantum
uncertainty, and entanglement.
5/3/2022
Samira Sayedsalehi
3
4. The power of quantum computer
• Google announced it has a quantum computer that is
100 million times faster than any classical computer
in its lab.
• Not only is this expected to make quantum computers
faster and more efficient than even the most powerful
current supercomputer, it’s thought they could have
potential uses and solve problems that we can’t even
comprehend yet.
5/3/2022
Samira Sayedsalehi
4
5. History of quantum computing
• Yuri Manin(1980) and Richard Feynman (1981)
proposed independently the concept of Quantum
Computer.
• David Deutsch (1985) developed the quantum Turing
machine, showing that quantum circuits are universal.
• Peter Shor (1994) came up with a quantum algorithm
to factor very large numbers in polynomial time.
• Lov Grover (1996) invents quantum database search
algorithm.
5/3/2022
Samira Sayedsalehi
5
8. Quantum bit (Qubit)
• In quantum computing the information is encoded in Quantum bits
▫ Two basic states
▫ Superposition
▫ Where α and β are complex numbers and are called quantum amplitudes,
▫ A qubit in superposition is in both of the states at the same time with probabilities
2 2
1
a b
+ =
2 2
,
a b
1 0
0 , 1
0 1
0 1
5/3/2022
Samira Sayedsalehi
8
10. Quantum error
• In reality, a quantum system that is not closed system is always in
interaction with its environment.
• This interaction inevitably alters the state of the quantum system, which
causes loss of information encoded in the system.
• This process is called decoherence whereby a pure state is turned into a
mixed state via interactions with the environment.
• The effect of noise on a single qubit is described by saying that quantum
noise acts on qubits via the application of one of the operators I , X, Y , Z .
Samira Sayedsalehi
10
5/3/2022
11. Quantum error
• Quantum Pauli errors
▫ Bit-flip error (X Pauli operator (𝜎𝑥)):
▫ Phase-flip error (Z Pauli operator (𝜎𝑧)):
▫ Y error (Y Pauli operator (𝜎𝑦)): Y=ZX
Samira Sayedsalehi
11
5/3/2022
12. Leakage error
• Leakage error occurs when a qubit leaves the defined computational
subspace.
• Leakage errors not only spread additional errors to other qubits, but also
lead to measurement errors and will accumulate unless removed.
5/3/2022
Samira Sayedsalehi
12
Phase Qubit
Flux Qubit
13. Leakage error
• Leakage error occurs when a qubit leaves the defined computational
subspace.
• Leakage errors not only spread additional errors to other qubits, but also
lead to measurement errors and will accumulate unless removed.
5/3/2022
Samira Sayedsalehi
13
Charge Qubit
14. The problems for Quantum error correction code
• There are some limitations in quantum:
▫ Measurement of error destroys quantum data.
▫ No-cloning theorem prevents repetition.
▫ There are some type errors in quantum computing like
phase and bit flip errors
▫ How continuous errors are corrected in quantum
computing?
Samira Sayedsalehi
14
5/3/2022
15. Why it is impossible to copy qubits?
Samira Sayedsalehi
15
• Controlled-Not (CNOT)
5/3/2022
16. Why it is impossible to copy qubits?
Samira Sayedsalehi
16
a
0
a
a⊕0=a
If there is a bit information, it can be copied
5/3/2022
18. Bit-flip Quantum Error Correction Code (QECC)
• Encoding
▫ Let us recall that the action of a CNOT gate is
▫ Therefore, it duplicates the control bit j ∈ {0, 1} when the initial target bit is set
to |0⟩. We use this fact to triplicate the basis vectors as
▫ Where |ψ⟩L denotes the encoded state. The state |ψ⟩L is called the logical qubit, while each
constituent qubit is called the physical qubit. We borrow terminologies from classical
error correcting code (ECC) and call the set
▫ The code and call each member of C a codeword.
Samira Sayedsalehi
18
5/3/2022
21. Error syndrome correction Phase in bit-flip QECC
• If we received the following logical qubit
• Both of the ancillary qubits are flipped for both |100⟩ and |011⟩. The set of
two bits is called the syndrome, and it tells us in which physical qubit the
error occurred during transmission.
• We have detected an error without measuring the received state. These
features are common to all QECC.
Samira Sayedsalehi
21
5/3/2022
24. Correction Phase in bit-flip QECC
• Ignoring multiple error states with small probabilities, we immediately find
that the following action must be taken:
Error syndrome Correction to be made
(00) identity operation (nothing is required)
(01) apply X3
(10) apply X2
(11) apply X1
Samira Sayedsalehi
24
5/3/2022
25. Phase-Flip QECC
• Let us consider a phase-flip channel. Phase flip
• occurs with probability p for each qubit independently when it is sent
through a channel.
• To correct phase flip errors, we can again use a three-qubit code to encode
logical states. This is done using the |±⟩ basis instead of the computational
basis.
• The encoded code
Samira Sayedsalehi
25
5/3/2022
27. Shor’s Nine-Qubit Code
• Encoding circuit for Shor’s nine-qubit QECC, which maps
• We encode |0⟩ and |1⟩ as
• then
Samira Sayedsalehi
27
5/3/2022
28. QEC code
• In general, most of the QEC codes work as
• A quantum data register |ψ⟩D is entangled with redundancy qubits |0⟩R via
an encoding operation to create a logical |ψ⟩L.
• The set of auxillary qubits |0⟩A is called the syndrome, and it tells us in
which physical qubit the error occurred during transmission
Samira Sayedsalehi
28
Encoder
N
N
Stabilizer
H H
Decoder
Error Syndrome
Correction
Noise
Encoding Decoding
Correction
5/3/2022
29. Stabilizer
• Stabilizer is a subgroup S of the Pauli group 𝒢n on n qubits with the following
two properties:
▫ The subgroup is Abelian (i.e., all operators in the subgroup commute);
▫ The subgroup does not contain the element -I.
• We can say
• Stabilizer code can be defined as
• An important property of the Pauli group is that any two Pauli operators either
commute or anticommute.
• A valid code word will be a +1 eigenvalue of all the stabilizer generators.
i
S
{ , }
s
C H M M S
Samira Sayedsalehi
29
5/3/2022
30. The stabilizer of bit-flip repetition code
Samira Sayedsalehi
30
• The error syndrome for this code is determined by measuring the two
stabilizer generators Z1Z2 ≡ ZZ𝐼 and Z2Z3 ≡ 𝐼ZZ.
1
2 1
{ 000 , 111 }, {100 , 011 },
{ 010 , 101 }, { 001 , 110 }
C span E span
E span E span
5/3/2022
31. Coherent parity check (CPC)
Samira Sayedsalehi
31
• Encode stage: The data register is entangled with the parity
qubit .
• Decode stage: The register is disentangled from the parity qubit
via the application of the unitary inverse of the first parity
check. The final syndrome measurement of qubit tells us
whether the results of the two parity checks differ.
D
0 P
N
N
H H
Noise
Encoding Decoding Correction
H H
5/3/2022
35. Surface code
• One of the topological QEC codes is surface code that is a stabilizer code
arranged on a 2-D lattice with nearest-neighbor interactions. It encodes a
single logical qubit in a number of physical qubits that is determined by the
code distance d.
Samira Sayedsalehi
35
5/3/2022
36. Surface code
• The eigenvectors of 𝑍 are {|0⟩, |1⟩} with eigenvalues ±1. A measurement
𝑀𝑧 of the qubit will return only one of two possible measurement
outcomes, +1 with the qubit state projected to |0⟩, or −1 with the qubit state
projected to |1⟩.
• A subsequent measurement 𝑀𝑥 of X will project the qubit state onto the X
eigenstates |+⟩ or |-⟩, with +1 and −1 measurement outcomes, respectively.
, 1
, 1
a b c d abcd abcd
a b c d abcd abcd
Z Z Z Z Z Z
X X X X X X
Samira Sayedsalehi
36
5/3/2022
37. Surface code
• A single error on data qubit will be indicated by changes in the
measurement outcomes.
• Because of 𝑋, 𝑍 ≠ 0, operators X and Z anticommute.
( ) ( )
= X ( )
a b c d a a a b c d
abcd a
X X X X Z Z X X X X
Z
( ) ( )
= Z ( )
a b c d a a a b c d
abcd a
Z Z Z Z X X Z Z Z Z
X
Samira Sayedsalehi
37
5/3/2022
43. Noise model with Qiskit
Samira Sayedsalehi
43
• The function below creates a simple noise model in order to
see the effects of imperfect qubits in Qiskit.
• First, we need to import all the tools we will need
▫ from qiskit.providers.aer.noise import NoiseModel
▫ from qiskit.providers.aer.noise.errors import pauli_error,
depolarizing_error
▫ from qiskit import QuantumRegister, ClassicalRegister
▫ from qiskit import QuantumCircuit, Aer, transpile, assemble
▫ from qiskit.visualization import plot_histogram
▫ aer_sim = Aer.get_backend('aer_simulator')
5/3/2022
44. Noise model
Samira Sayedsalehi
44
▫ def get_noise(p_meas,p_gate):
error_meas = pauli_error([('X',p_meas), ('I', 1 - p_meas)])
error_gate1 = depolarizing_error(p_gate, 1)
error_gate2 = error_gate1.tensor(error_gate1)
noise_model = NoiseModel()
noise_model.add_all_qubit_quantum_error(error_meas, "measure") # measurement
error is applied to measurements
noise_model.add_all_qubit_quantum_error(error_gate1, ["x"]) # single qubit gate
error is applied to x gates
noise_model.add_all_qubit_quantum_error(error_gate2, ["cx"]) # two qubit gate error
is applied to cx gates return noise_model
return noise_model
return noise_model
5/3/2022
45. Quantum repetition code with Qiskit
Samira Sayedsalehi
45
• In this code, |000⟩ is built and also a noise model is created with a
probability of 1% for each type of error.
▫ noise_model = get_noise(0.01,0.01)
▫ qc0 = QuantumCircuit(3) # initialize circuit with three qubits in the 0 state
▫ qc0.measure_all() # measure the qubits
▫ # run the circuit with the noise model and extract the counts
▫ qobj = assemble(qc0)
▫ counts = aer_sim.run(qobj, noise_model=noise_model).result().get_counts()
▫ plot_histogram(counts)
5/3/2022
46. Quantum repetition code with Qiskit
Samira Sayedsalehi
46
• Here we see that almost all results still come out '000', as they would if there was
no noise. Of the remaining possibilities, those with a majority of 0s are most likely.
In total, much less than 10 samples come out with a majority of 1s.
5/3/2022
47. Quantum repetition code with Qiskit
Samira Sayedsalehi
47
• In this code, |111⟩ is built and also a noise model is created with a
probability of 1% for each type of error.
▫ noise_model = get_noise(0.01,0.01)
▫ qc0 = QuantumCircuit(3) # initialize circuit with three qubits in the 0 state
▫ qc0.measure_all() # measure the qubits
▫ # run the circuit with the noise model and extract the counts
▫ qobj = assemble(qc0)
▫ counts = aer_sim.run(qobj, noise_model=noise_model).result().get_counts()
▫ plot_histogram(counts)
5/3/2022
48. Quantum repetition code with Qiskit
Samira Sayedsalehi
48
• The number of samples that come out with a majority in the wrong state (0 in this
case) is again much less than 10, so P<1%.
5/3/2022
49. Quantum repetition code with Qiskit
Samira Sayedsalehi
49
• As we increase pmeas and pgate, the higher the probability P will be. The
extreme case of this is for either of them to have a 50/50 chance of
applying the bit flip error, x. For example, let's run the same circuit as
before but with pmeas = 0.5 and pgate = 0.5.
▫ noise_model = get_noise(0.5,0.0)
▫ qobj = assemble(qc1)
▫ counts = aer_sim.run(qobj, noise_model=noise_model).result().get_counts()
▫ plot_histogram(counts)
5/3/2022
51. Quantum repetition code with Qiskit
Samira Sayedsalehi
51
• We can use it in Qiskit by importing the required tools from Ignis.
from qiskit.ignis.verification.topological_codes import RepetitionCode
• In the version 0.7.0 Qiskit Ignis is deprecated has been supersceded by Qiskit
Experiments project
▫ Import qiskit_experiments.library
• The circuits for the repetition code can then be created automatically from
using the RepetitionCode object from Qiskit-Ignis.
n = 3
T = 1 #one syndrome measurement round
code = RepetitionCode(n, T)
5/3/2022
52. Quantum repetition code
• The RepetitionCode contains two quantum circuits that implement the code: One
for each of the two possible logical bit values. Here are those for logical 0 and 1,
respectively.
▫ code.circuit['0'].draw(‘mpl’)
Samira Sayedsalehi
52
5/3/2022
54. Quantum repetition code
• Running these circuits on a simulator without any noise leads to very
simple results.
def get_raw_results(code,noise_model=None):
circuits = code.get_circuit_list()
raw_results = {}
for log in range(2):
qobj = assemble(circuits[log])
job = qasm_sim.run(qobj, noise_model=noise_model)
raw_results[str(log)] = job.result().get_counts(str(log))
return raw_results raw_results = get_raw_results(code)
for log in raw_results:
print('Logical', log, ':', raw_results[log], 'n')
Samira Sayedsalehi
54
5/3/2022
55. Quantum repetition code
• Repetition code with some noise:
code = RepetitionCode(3,1)
noise_model = get_noise(0.05,0.05)
raw_results = get_raw_results(code,noise_model)
for log in raw_results:
print('Logical', log,':', raw_results[log],'n')
• Result :
Samira Sayedsalehi
55
5/3/2022
56. References
• https://qiskit.org/textbook/preface.html
• Fowler, A.G., Mariantoni, M., Martinis, J.M. and Cleland,
A.N., 2012. Surface codes: Towards practical large-scale
quantum computation. Physical Review A, 86(3), p.032324.
• Tomita, Y. and Svore, K.M., 2014. Low-distance surface codes
under realistic quantum noise. Physical Review A, 90(6),
p.062320.
• Nakahara, M. and Ohmi, T., 2008. Quantum computing: from
linear algebra to physical realizations. CRC press.
5/3/2022
Samira Sayedsalehi
56
57. References
• Roffe, J., Headley, D., Chancellor, N., Horsman, D. and
Kendon, V., 2018. Protecting quantum memories using
coherent parity check codes. Quantum Science and
Technology, 3(3), p.035010.
• Roffe, J., 2019. Quantum error correction: an
introductory guide. Contemporary Physics, 60(3),
pp.226-245.
• https://www.nextbigfuture.com/2018/04/improved-
quantum-error-correction-could-enable-universal-
quantum-computing.html
5/3/2022
Samira Sayedsalehi
57
Editor's Notes
A quantum computer is a machine that relies on quantum phenomena like superposition, quantum uncertainty, and entanglement.
SUPERPOSITION: there is an equal probability that something is either in one state (1) or another (0). Thus, something is in both states, or between both states at the same time until observedQuantum Uncertainty: states that the position and velocity of a particle are unknown until observed.
Quantum Nonlocality—Entanglement: When two particles share the same quantum state they are entangled. This means that two or more particles will share the same properties: for example, their spins are related. Even when removed from each other, these particles will continue to share the same properties.
We can say:
Information is stored in a physical medium, and manipulated by physical processes. Therefore, the laws of physics dictate the capabilities of any information processing device.
As we know, classical physics is incomplete to explain some physical events especially on the atomic and subatomic level and has been replaced by a more powerful framework: quantum mechanics. Consequently, a computer that operates on quantum states can perform tasks that are beyond the capability of any conceivable classical computer.
Google announced it has a quantum computer that is 100 million times faster than any classical computer in its lab.
For example, a quantum computer with 30 qubits equals the processing power of conventional computer that running at 10 teraflop (trillions of floating-point operations per second).
Not only is this expected to make quantum computers faster and more efficient than even the most powerful current supercomputer, it’s thought they could have potential uses and solve problems that we can’t even comprehend yet because of quantum phenomena like quantum superposition and entanglement.
Quantum computer are good at solving certain classes of problems like factoring large primes, random walks, unordered data retrieval, etc.
Not good at things like: exact answers, logic, pattern recognition, unconstrained control problems.
In 2016, IBM was the first company to put a quantum computer on the cloud. The company has since built up an active community of more than 260,000 registered users, who run more than one billion every day on real hardware and simulators.
In 2017, IBM was the first company to offer universal quantum computing systems via the IBM Q Network.
Intel designed and fabricated the new 49-qubit superconducting quantum chip in 2018.
Founded in 1999, D-Wave claims to be the first company to sell a commercial quantum computer, in 2011, and the first to give developers real-time cloud access to quantum processors with Leap, its quantum cloud service.
D-Wave's approach to quantum computing, known as quantum annealing, is best suited to optimization tasks.
In nature, physical systems tend to evolve toward their lowest energy state: objects slide down hills, hot things cool down, and so on. This behavior also applies to quantum systems. To imagine this, think of a traveler looking for the best solution by finding the lowest valley in the energy landscape that represents the problem.
Google built a quantum computer with 53 qubits.
This picture presents IBM’s roadmap to advance quantum computers from today’s noisy, small-scale devices to larger, more advance quantum systems of the future.
Currently the company's researchers are developing a suite of scalable, increasingly larger and better processors, with a 1,000-plus qubit device - called IBM Quantum Condor - targeted for the end of 2023.
In quantum computing the information is encoded in Quantum bits (or qubits), which are somewhat analogous to the bit in classical computation. This information is described by a state in a 2-level quantum mechanical system which is formally equivalent to a two-dimentional vector space over the complex numbers. The two basis states are conventionally written as ∣0⟩ and ∣1⟩ (pronounced: 'ket 0' and 'ket 1'), this notation is called Dirac notation.
A qubit can be in ∣0⟩, ∣1⟩ or (unlike a classical bit) in a linear combination of both states. The name of this phenomenon is superposition.
Superposition: there is an equal probability that something is either in one state (1) or another (0). Thus, something is in both states, or between both states at the same time until observed.
Where α and β are complex numbers and are called quantum amplitudes.
A qubit in superposition is in both of the states at the same time with probabilities.
In other words, 𝛼 2 : tells us the probability of finding ∣ψ⟩ in state ∣0⟩ and 𝛽 2 : tells us the probability of finding ∣ψ⟩ in state ∣1⟩.
It turns out that we can describe the interaction of a single qubit with the environment and hence single-qubit errors with the familiar operators I,X,Y,Z.
The identity operator, of course, represents no error at all.
In the case of a classical bit, which can be 0 or 1, engineers are concerned with bit flip errors. The physical details aren’t important for us, but we can imagine some stray electromagnetic field causing a bit to change from 1 to 0, for example. A similar type of error can affect qubits, where we have |0>→|1> and |1>→|0>. This type of error is described by the X operator,
In quantum systems, bit flip errors are not the only problem that we can encounter. We can also have phase flip errors. we readily see that a phase flip error is described by the Z Pauli operator. We recall that Z acts on a qubit in the following way:
The Y operator is related to a phase flip followed by a bit flip.
Later we will see how quantum error-correcting codes work on bit flip and phase flip errors
The repetition code works in a classical channel, because classical bits are easy to measure and to repeat. Repetition code in quantum is not possible because of no-cloning theorem. A different method, such as the so-called three-qubit bit flip code, has to be used. This technique uses entanglement and syndrome measurements and is comparable in performance with the repetition code.
It is important to note that the state |ψ> is not triplicated but only the basis vectors are triplicated.
This redundancy makes it possible to detect errors in |ψL> and correct them as we see in the next figure.
The gate NX stands for the bit-flip noise
Now the state |ψL> is sent through a quantum channel which introduces bitflip error with a rate p for each qubit independently. We assume p is sufficiently small so that not many errors occur during the qubit transmission. The received state depends on in which physical qubit(s) the bit-flip occurred.
Two ancillary qubits are perpared in the state |00> as depicted in Fig and applies four CNOT operations whose control bits are the encoded qubits while the target qubits are two ancillary qubits. Let |x1x2x3 > be a basis vector has received and let A (B) be the output state of the first (second) ancilla qubit. (A = x1 ⊕ x2 and B = x1 ⊕ x3)
The gate NX stands for the bit-flip noise
Now the state |ψL> is sent through a quantum channel which introduces bitflip error with a rate p for each qubit independently. We assume p is sufficiently small so that not many errors occur during the qubit transmission. The received state depends on in which physical qubit(s) the bit-flip occurred.
Two ancillary qubits are perpared in the state |00> as depicted in Fig and applies four CNOT operations whose control bits are the encoded qubits while the target qubits are two ancillary qubits. Let |x1x2x3 > be a basis vector has received and let A (B) be the output state of the first (second) ancilla qubit. (A = x1 ⊕ x2 and B = x1 ⊕ x3)
The gate NX stands for the bit-flip noise
Now the state |ψL> is sent through a quantum channel which introduces bitflip error with a rate p for each qubit independently. We assume p is sufficiently small so that not many errors occur during the qubit transmission. The received state depends on in which physical qubit(s) the bit-flip occurred.
Two ancillary qubits are perpared in the state |00> as depicted in Fig and applies four CNOT operations whose control bits are the encoded qubits while the target qubits are two ancillary qubits. Let |x1x2x3 > be a basis vector has received and let A (B) be the output state of the first (second) ancilla qubit. (A = x1 ⊕ x2 and B = x1 ⊕ x3)
The gate NX stands for the bit-flip noise
Now the state |ψL> is sent through a quantum channel which introduces bitflip error with a rate p for each qubit independently. We assume p is sufficiently small so that not many errors occur during the qubit transmission. The received state depends on in which physical qubit(s) the bit-flip occurred.
Two ancillary qubits are perpared in the state |00> as depicted in Fig and applies four CNOT operations whose control bits are the encoded qubits while the target qubits are two ancillary qubits. Let |x1x2x3 > be a basis vector has received and let A (B) be the output state of the first (second) ancilla qubit. (A = x1 ⊕ x2 and B = x1 ⊕ x3)
We consider how to correct phase-flip error mimicking the prescription given for a bit-flip error. We generate a logical qubit that takes α|000>+β|111>, and then apply Hadamard gates to each qubit.
An n–qubit quantum state is called a stabilizer state if it is stabilized by nontrivial subgroup of Pauli group.
An important property of the Pauli group is that any two Pauli operators either commute or anticommute Based on this property we can see how a stabilizer code detects and corrects errors.
A valid code word will be a +1 eigenvalue of all the stabilizer generators.
Suppose that an error operator E will anticommute with some of the stabilizer generators, and commute with others. The error will be detected that it anticommute with some of the stabilizer generators.
As errors occur from the random and unpredictable appearance of X and Z operations, they must be detected by repeatedly measuring each qubit, which can be done with combined X and Z measurements.
A error gate, is called depolarizing noise, is an imperfection in any operation we perform. The effect of this will be, with probability Pgate ,to replace the state of any qubit with a completely random state.
The other form of noise is that for measurement. This simply flips between a 0 to a 1 and vice-versa immediately before measurement with probability Pmeas.
def get_noise(p_meas,p_gate):
A error gate, is called depolarizing noise, is an imperfection in any operation we perform. The effect of this will be, with probability Pgate ,to replace the state of any qubit with a completely random state.
The other form of noise is that for measurement. This simply flips between a 0 to a 1 and vice-versa immediately before measurement with probability Pmeas.
def get_noise(p_meas,p_gate):
A error gate, is called depolarizing noise, is an imperfection in any operation we perform. The effect of this will be, with probability Pgate ,to replace the state of any qubit with a completely random state.
The other form of noise is that for measurement. This simply flips between a 0 to a 1 and vice-versa immediately before measurement with probability Pmeas.
def get_noise(p_meas,p_gate):
A error gate, is called depolarizing noise, is an imperfection in any operation we perform. The effect of this will be, with probability Pgate ,to replace the state of any qubit with a completely random state.
The other form of noise is that for measurement. This simply flips between a 0 to a 1 and vice-versa immediately before measurement with probability Pmeas.
def get_noise(p_meas,p_gate):
With this noise, all outcomes occur with equal probability, with differences in results being due only to statistical noise. No trace of the encoded state remains. This is an important point to consider for error correction: sometimes the noise is too strong to be corrected. The optimal approach is to combine a good way of encoding the information you require, with hardware whose noise is not too strong.
In these circuits, we have two types of physical qubits. There are the 'code qubits', which are the three physical qubits across which the logical state is encoded. There are also the 'link qubits', which serve as the auxiliary qubits for the syndrome measurements.
Our single round of syndrome measurements in these circuits consist of just two syndrome measurements. One compares code qubits 0 and 1, and the other compares code qubits 1 and 2. One might expect that a further measurement, comparing code qubits 0 and 2, should be required to create a full set. However, these two are sufficient. This is because of the information on whether 0 and 2 have the same z basis state can be inferred from the same information about 0 and 1 with that for 1 and 2. Indeed, for n qubits, we can get the required information from just n−1 syndrome measurements of neighbouring pairs of qubits.