The document discusses the application of the Bravyi-Kitaev transformation to quantum chemistry calculations on a quantum computer. It notes that while quantum computers could perform quantum chemistry simulations much faster than classical computers, actually implementing the calculations requires many unitary circuits. The Bravyi-Kitaev transformation reduces the number of circuits needed by encoding qubits in a different way, making the calculations more efficient for a real quantum computer.
QIQB(大阪大学先導的学際研究機構量子情報・量子生命研究部門)セミナー でのスライドを加筆したもの。量子コンピュータを用いた量子化学計算の現在の状況と展望を述べた.
伝統的なゲート式位相推定による方法とvariational eigen solverによるものと2つ。ごく最近虚時間発展法の実装もされており、それは別スライドで概観した。
QIQB(大阪大学先導的学際研究機構量子情報・量子生命研究部門)セミナー でのスライドを加筆したもの。量子コンピュータを用いた量子化学計算の現在の状況と展望を述べた.
伝統的なゲート式位相推定による方法とvariational eigen solverによるものと2つ。ごく最近虚時間発展法の実装もされており、それは別スライドで概観した。
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)Maho Nakata
The document describes the Hamiltonian operator (H) and its application to the Hartree-Fock wavefunction (|ΦHF⟩) to obtain energy eigenvalues (E0, E1, etc.). The Hartree-Fock wavefunction can be expressed as a linear combination of Slater determinants (|Ψ0⟩, |Ψ1⟩, etc.). Applying the exponential of the Hamiltonian operator over time (eiHt) to |ΦHF⟩ yields the time-dependent Hartree-Fock wavefunction.
This document contains contact information for several researchers from the Machine Perception and Robotics Group at Chubu University in Japan, including professors, lecturers, and research assistants. It lists their names, titles, contact details such as phone numbers and email addresses, and web links for the group's website. The group is part of the Department of Robotics Science and Technology or Department of Computer Science within the College of Engineering at Chubu University.
The document provides an overview of quantum computing basics, including:
- Types of quantum computers such as quantum annealers, analog quantum computers, and universal quantum computers.
- Key concepts such as qubits, the smallest unit of quantum information that can be in a superposition of states, and common physical implementations like ions and photons.
- Challenges like errors that can occur and approaches to error correction using techniques like Shor's code and topological quantum codes.
- An example of Schrodinger's cat thought experiment that illustrates the strange nature of quantum superposition.
This document provides an overview of quantum computing trends and directions. It introduces Francisco Gálvez as the presenter and covers the following topics: IBM's quantum computers including the IBM Quantum Experience platform, basic concepts in quantum computing, quantum architecture focusing on superconducting qubits, quantum algorithms like Shor's and Grover's algorithms, applications of quantum computing, and the IBM Quantum Experience platform which allows users to design and run quantum circuits on real quantum processors.
quantum chemistry on quantum computer handson by Q# (2019/8/4@MDR Hongo, Tokyo)Maho Nakata
The document describes the Hamiltonian operator (H) and its application to the Hartree-Fock wavefunction (|ΦHF⟩) to obtain energy eigenvalues (E0, E1, etc.). The Hartree-Fock wavefunction can be expressed as a linear combination of Slater determinants (|Ψ0⟩, |Ψ1⟩, etc.). Applying the exponential of the Hamiltonian operator over time (eiHt) to |ΦHF⟩ yields the time-dependent Hartree-Fock wavefunction.
This document contains contact information for several researchers from the Machine Perception and Robotics Group at Chubu University in Japan, including professors, lecturers, and research assistants. It lists their names, titles, contact details such as phone numbers and email addresses, and web links for the group's website. The group is part of the Department of Robotics Science and Technology or Department of Computer Science within the College of Engineering at Chubu University.
The document provides an overview of quantum computing basics, including:
- Types of quantum computers such as quantum annealers, analog quantum computers, and universal quantum computers.
- Key concepts such as qubits, the smallest unit of quantum information that can be in a superposition of states, and common physical implementations like ions and photons.
- Challenges like errors that can occur and approaches to error correction using techniques like Shor's code and topological quantum codes.
- An example of Schrodinger's cat thought experiment that illustrates the strange nature of quantum superposition.
This document provides an overview of quantum computing trends and directions. It introduces Francisco Gálvez as the presenter and covers the following topics: IBM's quantum computers including the IBM Quantum Experience platform, basic concepts in quantum computing, quantum architecture focusing on superconducting qubits, quantum algorithms like Shor's and Grover's algorithms, applications of quantum computing, and the IBM Quantum Experience platform which allows users to design and run quantum circuits on real quantum processors.
Quantum computing has the potential to solve certain problems exponentially faster than classical computers by exploiting principles like superposition, entanglement, and interference. Current quantum computers with 50-100 qubits operate in the Noisy Intermediate-Scale Quantum (NISQ) era and use algorithms like the Variational Quantum Eigensolver (VQE) that are hybrid quantum-classical and incorporate techniques like quantum error mitigation. Major players in the field include IBM, Google, and Rigetti who are developing quantum hardware and software for applications in optimization, simulation, and machine learning.
Quantum algorithms like VQE and QAOA were used to analyze the impact of COVID-19 on optimal portfolio selection across different industries. Three time periods were considered - pre-COVID, during COVID, and post-COVID. Results found that COVID disrupted optimal portfolios, with sectors like retail, technology and automotive favored more pre-COVID, while oil/gas and airlines/hospitality favored post-COVID. Quantum algorithms provided comparable results to classical methods like Markowitz for portfolio optimization under changing market conditions from the pandemic.
This document provides an overview of quantum computing, including:
- The current state of quantum computing technology, which involves noisy intermediate-scale quantum computers with 10s to 100s of qubits and moderate error rates.
- The difference between quantum and classical information, noting that quantum information uses superposition and entanglement, exponentially increasing computational power.
- An example quantum algorithm, Bernstein-Vazirani, which can solve a problem in one query that classical computers require n queries to solve, demonstrating quantum computing's potential computational advantages.
ABSTRACT: Once introduced the fundamental ideas of quantum computing, we will discuss the possibilities offered by quantum computers in machine learning.
BIO: Davide Pastorello obtained an M.Sc. in Physics (2011) and a Ph.D. in Mathematics (2014) from Trento University. After serving at the Dept. of Mathematics and DISI in Trento, he is currently an assistant professor at the Dept. of Mathematics, University of Bologna. His main research interests concern the mathematical aspects of quantum information theory, quantum computing, and quantum machine learning.
Quantum computing provides an alternative computational model based on quantum mechanics. It utilizes quantum phenomena such as superposition and entanglement to perform computations using quantum logic gates on qubits. This allows quantum computers to potentially solve certain problems exponentially faster than classical computers. However, building large-scale quantum computers remains a challenge. In the meantime, smaller quantum systems are being developed and quantum algorithms are being experimentally tested on these devices. Researchers are also working on methods to efficiently simulate quantum computations on classical computers.
Strengths and limitations of quantum computingVinayak Sharma
Quantum computing as a research field has been around for about 30 years. It seems like a way to overcome the challenges that classical (boolean based) computers are facing due to “quantum tunneling” effect. Although, there are various theoretical and practical challenges that are needed to be dealt with if we want quantum computes to perform better that classical computers (i.e achieving “quantum supremacy”). This seminar will aim to shed light on basics of quantum computing and its strengths and weaknesses.
Video Links
Part 1: https://www.youtube.com/watch?v=-WLD_HnUvy0
Part 2: https://www.youtube.com/watch?v=xXzUmpk8ztU
Quantum computing is a type of computation that harnesses the collective properties of quantum states, such as superposition, interference, and entanglement, to perform calculations.
This presentation is designed to elucidate about the Quantum Computing - History - Principles - QUBITS - Quantum Computing Models - Applications - Advantages and Disadvantages.
Pulse Compression Sequence (PCS) are widely used in radar to increase the range resolution. Binary sequence has the limitation that the compression ratio is small. Ternary code is suggested as an alternative. The design of ternary sequence with good Discriminating Factor (DF) and merit factor can be considered as a nonlinear multivariable optimization problem which is difficult to solve. In this paper, we proposed a new method for designing ternary sequence by using Modified Simulated Annealing Algorithm (MSAA). The general features such as global convergence and robustness of the statistical algorithm are revealed.
This document provides an overview of quantum computing presented by Dr Marcus Doherty. It discusses how quantum computing works, important concepts in quantum physics, applications of quantum computing, and opportunities for software professionals. Some key points include:
- Quantum computing offers potential solutions to problems that are intractable for classical computers by exploiting properties of quantum mechanics like superposition and entanglement.
- It works by initializing qubit states, applying quantum gates to encode data and algorithms, then reading out and repeating to build statistics.
- Challenges include errors, limited connectivity, and lack of a quantum random access memory. Software plays a key role in error correction, compilation, and developing applications.
- Potential applications include optimization, machine learning
This presentation is about quantum computing.which going to be new technological concept for computer operating system.In this subject the research is going on.
A quantum computer harnesses the power of atoms and molecules to perform calculations billions of times faster than silicon-based computers. Unlike classical bits that are either 0 or 1, quantum bits or qubits can be in a superposition of both states simultaneously. While current quantum computers have only manipulated a few qubits, their potential applications include efficiently solving problems like integer factorization that are intractable for classical computers. Significant challenges remain to controlling quantum phenomena necessary for building useful quantum computers.
1. The document provides an overview of quantum computation, discussing its history and advantages over classical computing.
2. Quantum computers can perform certain tasks like factoring large numbers and simulating quantum systems much faster than classical computers by taking advantage of quantum mechanics principles like superposition and parallelism.
3. One of the major advantages is that a quantum computer with just a few hundred qubits could theoretically operate on more states simultaneously than there are atoms in the observable universe, massively increasing its computational power over classical computers.
Em computação quântica, um algoritmo quântico é um algoritmo que funciona em um modelo realístico de computação quântica. O modelo mais utilizado é o modelo do circuito de computação quântica.
Quantum Computer is a machine that is used for Quantum Computation with the help of using Quantum Physics properties. Where classical computers encode information in binary “bits” that can either 0s or 1s but quantum computer use Qubits. Like the classical computer, the Quantum computer also uses 0 and 1, but qubits have a third state that allows them to represent one or zero at the same time and it’s called “Superposition”. This research paper has presented the Basics of Quantum Computer and The Future of Quantum Computer. So why Quantum Computer can be Future Computer, Because Quantum Computer is faster than any other computer, as an example, IBM’s Computer Deep Blue examined 200 million possible chess moves each second. Quantum Computer would be able to examine 1 trillion possible chess moves per second. It can be 100 million times faster than a classical computer. The computer makes human life easier and also focuses on increasing performance to make technology better. One such way is to reduce the size of the transistor and another way is to use Quantum Computer. The main aim of this paper is to know that how Quantum Computers can become the future computer.
- The document discusses quantum computing and its potential applications. It notes that while quantum computers may be able to efficiently simulate physical processes, quantum error correction is needed for scalability.
- Current quantum hardware has around 50-100 qubits but higher qubit numbers and lower error rates are needed. Quantum computers in the "Noisy Intermediate-Scale Quantum" era may be able to explore physics and have some commercial uses, but more powerful quantum technologies will likely require decades more of work.
Quantum computing harnesses the principles of quantum mechanics to perform calculations in parallel using quantum bits (qubits) that can exist in superposition. The first proposals for quantum computing date back to the 1980s. Current quantum computers have around 50-100 qubits and are being developed by companies like IBM, Google, and Intel to potentially solve problems like cryptography and AI that are intractable on classical computers. However, building reliable quantum computers remains challenging due to issues like qubit decoherence. Potential applications include optimization, simulation, and machine learning.
Quantum computing is a rapidly emerging technology that uses principles of quantum mechanics like superposition and entanglement to perform operations on quantum bits (qubits) and solve complex problems. It has the potential to vastly outperform classical computers for certain problems. The document discusses key aspects of quantum computing including how it differs from classical computing, what qubits are, how quantum computers work using elements like superconductors and Josephson junctions, and potential applications in areas like artificial intelligence, drug development, weather forecasting, and cybersecurity. It also covers advantages like speed and ability to solve complex problems, as well as current disadvantages like difficulty to build and susceptibility to errors.
Quantum Computation for Predicting Electron and Phonon Properties of SolidsKAMAL CHOUDHARY
This document outlines a workflow for using quantum computing to simulate electron and phonon properties of solids. It discusses the motivation for using quantum bits to simulate quantum systems more easily. It provides background on band theory of solids, quantum algorithms like VQE and circuit models. The workflow is then applied to calculate properties of aluminum metal and over 1000 other materials using classical and quantum solvers. Future opportunities and challenges are also discussed.
Similar to 20180723 量子コンピュータの量子化学への応用; Bravyi-Kitaev基底の実装 (20)
PubChem QC project. In this project we calculate all molecules in the PubChem Project. Currently 1,100,000 molecules are available at http://pubchemqc.riken.jp/ . Results are in public domain.
Direct variational calculation of second-order reduced density matrix : appli...Maho Nakata
Presented at GCOE interdisciplinary workshop on numerical methods for many-body correlations, https://sites.google.com/a/cns.s.u-tokyo.ac.jp/shimizu/gcoe
The MPACK : Multiple precision version of BLAS and LAPACKMaho Nakata
We are interested in the accuracy of linear algebra operations; accuracy of the solution of linear equation, eigenvalue and eigenvectors of some matrices, etc. This is a reason for we have been developing the MPACK. The MPACK consists of MBLAS and MLAPACK, multiple precision version of BLAS and LAPACK, respectively. Features of MPACK are: (i) based on LAPACK 3.x, (ii) to provide a reference implementation and or API (iii) written in C++, rewrite from FORTRAN77 (iv) supports GMP, MPFR, DD/QD and binary128 as multiple precision arithmetic library and (v) portable. Current version of MPACK is 0.7.0 and it supports 76 MBLAS routines and 100 MLAPACK routines. Matrix-matrix multiplication routine has been accelerated using NVIDIA C2050 GPU. All source codes are available at: http://mplapack.sourceforge.net/
Recent progresses in the variational reduced-density-matrix methodMaho Nakata
Recent progresses in the variational reduced-density-matrix method
N-representability, second-order reduced density matrix, 2-RDM, many body qunantum mechanics
Immersive Learning That Works: Research Grounding and Paths ForwardLeonel Morgado
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
Mending Clothing to Support Sustainable Fashion_CIMaR 2024.pdfSelcen Ozturkcan
Ozturkcan, S., Berndt, A., & Angelakis, A. (2024). Mending clothing to support sustainable fashion. Presented at the 31st Annual Conference by the Consortium for International Marketing Research (CIMaR), 10-13 Jun 2024, University of Gävle, Sweden.
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
(June 12, 2024) Webinar: Development of PET theranostics targeting the molecu...Scintica Instrumentation
Targeting Hsp90 and its pathogen Orthologs with Tethered Inhibitors as a Diagnostic and Therapeutic Strategy for cancer and infectious diseases with Dr. Timothy Haystead.
Current Ms word generated power point presentation covers major details about the micronuclei test. It's significance and assays to conduct it. It is used to detect the micronuclei formation inside the cells of nearly every multicellular organism. It's formation takes place during chromosomal sepration at metaphase.