This document provides an overview of quantum computing and common algorithms used on different types of quantum computers. It discusses how quantum computers work using qubits or qumodes and the existing gate-based and quantum annealing-based architectures. Some examples of algorithms that could run on these quantum computers are presented, including for supervised and unsupervised machine learning tasks as well as graph and network analysis problems. Researchers can access existing quantum computers through the cloud or simulate circuits classically.
Quantum Computing: Welcome to the FutureVernBrownell
Vern Brownell, CEO at D-Wave Systems, shares his thoughts on Quantum Computing in this presentation, which he delivered at Compute Midwest in November 2014. He addresses big questions that include: What is a quantum computer? How do you build one? Why does it matter? What does the future hold for quantum computing?
Quantum computers are designed to perform tasks much more accurately and efficiently than conventional computers, providing developers with a new tool for specific applications.
It is clear in the short-term that quantum computers will not replace their traditional counterparts; instead, they will require classical computers to support their specialized abilities, such as systems optimization.
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
Quantum Computing: Welcome to the FutureVernBrownell
Vern Brownell, CEO at D-Wave Systems, shares his thoughts on Quantum Computing in this presentation, which he delivered at Compute Midwest in November 2014. He addresses big questions that include: What is a quantum computer? How do you build one? Why does it matter? What does the future hold for quantum computing?
Quantum computers are designed to perform tasks much more accurately and efficiently than conventional computers, providing developers with a new tool for specific applications.
It is clear in the short-term that quantum computers will not replace their traditional counterparts; instead, they will require classical computers to support their specialized abilities, such as systems optimization.
An introduction to quantum machine learning.pptxColleen Farrelly
Very basic introduction to quantum computing given at Indaba Malawi 2022. Overviews some basic hardware in classical and quantum computing, as well as a few quantum machine learning algorithms in use today. Resources for self-study provided.
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
A Shore Introduction to Quantum Computer and the computation of ( Quantum Mechanics),
Nowadays we work on classical computer that work with bits which is either 0s or 1s, but Quantum Computer work with qubits which is either 0s or 1s or 0 and 1 in the same time.
Quantum Computing, Quantum Machine Learning, and Recommendation SystemsSyed Falahuddin Quadri
Introductory guest lecture on Quantum Computing, Quantum Machine Learning, and Recommendation Systems delivered in Data Analysis and Data Mining (Spring 2019) course at UESTC by Dr. Li Xiaoyu, Guest Lecture delivered by Syed Falahuddin Quadri.
This is a seminar on Quantum Computing given on 9th march 2017 at CIME, Bhubaneswar by me(2nd year MCA).
Video at - https://youtu.be/vguxg0RYg7M
PDF at - http://www.slideshare.net/deepankarsandhibigraha/quantum-computing-73031375
An overview of quantum computing, with its features, capabilities and types of problems it can solve. Also covers some current and future implementations of quantum computing, and a view of the patent landscape.
Introduction to Quantum Computation. Part - 1Arunabha Saha
Introduction to quantum computation. Here the very basic maths described needed for quantum information theory as well as computation. Postulates of quantum mechanics and the Hisenberg`s Uncertainty principle. Basic operator theories are described here.
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.
Quantum Computers new Generation of Computers part 7 by prof lili saghafi Qua...Professor Lili Saghafi
Quantum algorithm
algorithm for factoring, the general number field sieve
Optimization algorithm
deterministic quantum algorithm Deutsch-Jozsa algorithm
Entanglement
Enigma
Quantum Teleportation
A Shore Introduction to Quantum Computer and the computation of ( Quantum Mechanics),
Nowadays we work on classical computer that work with bits which is either 0s or 1s, but Quantum Computer work with qubits which is either 0s or 1s or 0 and 1 in the same time.
Quantum Computing, Quantum Machine Learning, and Recommendation SystemsSyed Falahuddin Quadri
Introductory guest lecture on Quantum Computing, Quantum Machine Learning, and Recommendation Systems delivered in Data Analysis and Data Mining (Spring 2019) course at UESTC by Dr. Li Xiaoyu, Guest Lecture delivered by Syed Falahuddin Quadri.
This is a seminar on Quantum Computing given on 9th march 2017 at CIME, Bhubaneswar by me(2nd year MCA).
Video at - https://youtu.be/vguxg0RYg7M
PDF at - http://www.slideshare.net/deepankarsandhibigraha/quantum-computing-73031375
An overview of quantum computing, with its features, capabilities and types of problems it can solve. Also covers some current and future implementations of quantum computing, and a view of the patent landscape.
Introduction to Quantum Computation. Part - 1Arunabha Saha
Introduction to quantum computation. Here the very basic maths described needed for quantum information theory as well as computation. Postulates of quantum mechanics and the Hisenberg`s Uncertainty principle. Basic operator theories are described here.
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.
Call for Chapters- Edited Book: Quantum Networks and Their Applications in AI...Christo Ananth
The research on Quantum Networked Artificial Intelligence is at the intersection of Quantum Information Science (QIS), Artificial Intelligence, Soft Computing, Computational Intelligence, Machine Learning, Deep Learning, Optimization, Etc. It Touches On Many Important Parts Of Near-Term Quantum Computing And Noisy Intermediate-Scale Quantum (NISQ) Devices. The research on quantum artificial intelligence is grounded in theories, modelling, and significant studies on hybrid classical-quantum algorithms using classical simulations, IBM Q services, PennyLane, Google Cirq, D-Wave quantum annealer etc. So far, the research on quantum artificial intelligence has given us the building blocks to achieve quantum advantage to solve problems in combinatorial optimization, soft computing, deep learning, and machine learning much faster than traditional classical computing. Solving these problems is important for making quantum computing useful for noise-resistant large-scale applications. This makes it much easier to see the big picture and helps with cutting-edge research across the quantum stack, making it an important part of any QIS effort. Researchers — almost daily — are making advances in the engineering and scientific challenges to create practical quantum networks powered with artificial intelligence
Running head: QUANTUM COMPUTING
QUANTUM COMPUTING 9
Research Paper: Quantum Computing
(Student’s Name)
(Professor’s Name)
(Course Title)
(Date of Submission)
Abstract
Quantum computers are a new era of invention, and its innovation is still to come. The revolution of the quantum computers produced a lot of challenges for ethical decision-making and predictions at different levels of life; therefore, it raised new concerns such as invasion of privacy and national security. In fact, it can be used easily to access and steal private information and data, while on the other hand, quantum computers can help to eliminate these unethical intrusions and secure the information.
Quantum computers will be the most powerful computer in the world that would open the door to encrypt the information in much less time. On the contrary, the supercomputers sometimes take so many hours to encrypt, whereas quantum computers can be used for the same purpose in a shorter time period making it harder to decrypt the data and information.
Many years from now, quantum computers will become mainstays throughout the world of computing. It will serve the individual and the community, but there is a significant concern that quantum computers could be used to invade people’s privacy (Hirvensalo, 2012).
Literature Review
The study area that is aimed on the implementation of quantum theory principles to develop computer technology is called Quantum computing. The field of quantum mechanics arose from German physicist Max Planck’s attempts to describe the spectrum emitted by hot bodies and specifically he wondered the reason behind the shift in color from red to yellow to blue as the temperature of a flame increased.
https://www.stratfor.com/analysis/approaching-quantum-leap-computing
There has been tremendous development in quantum computing since then and more research is been done to realize its full potential. Generally, quantum computing depends on quantum laws of physics. Rather than store information as 0s or 1s as conventional computers do, a quantum computer uses qubits which can be a 1 or a 0 or both at the same time. The quantum superposition along with the quantum effects of entanglement and quantum tunneling enable computers to consider and manipulate all combinations of bits simultaneously. This effect will make quantum computation powerful and fast (Williams, 2014).
http://www.dwavesys.com/quantum-computing
Researchers in quantum computing have enjoyed a greater level of success. The first small 2-qubit quantum computer was developed in 1997 and in 2001 a 5-qubit quantum computer was used to successfully factor the number 15 [85].Since then, experimental progress on a number of different technologies has been steady but slow, although the practical problems facing physical realizations of quantum computers can be addressed. It is believed that a quant.
Quantum computing is the research area centered on creating computer technology that uses quantum theory concepts that explain the nature and conduct of energy and matter at the level of the quantum (atomic and subatomic). The development of a practical quantum computer would mark a step forward in computing capacity far greater than that of a modern supercomputer, with considerable increases in efficiency. According to the rules of quantum physics, a quantum computer could achieve enormous processing power through multi-state capacity and execute functions simultaneously using all possible permutations. This paper briefly discusses the basic elements of quantum computing and further explores the potential of quantum computing to improve analytical and computing capabilities in solving power system problems.
Quantum communication and quantum computingIOSR Journals
Abstract: The subject of quantum computing brings together ideas from classical information theory, computer
science, and quantum physics. This review aims to summarize not just quantum computing, but the whole
subject of quantum information theory. Information can be identified as the most general thing which must
propagate from a cause to an effect. It therefore has a fundamentally important role in the science of physics.
However, the mathematical treatment of information, especially information processing, is quite recent, dating
from the mid-20th century. This has meant that the full significance of information as a basic concept in physics
is only now being discovered. This is especially true in quantum mechanics. The theory of quantum information
and computing puts this significance on a firm footing, and has led to some profound and exciting new insights
into the natural world. Among these are the use of quantum states to permit the secure transmission of classical
information (quantum cryptography), the use of quantum entanglement to permit reliable transmission of
quantum states (teleportation), the possibility of preserving quantum coherence in the presence of irreversible
noise processes (quantum error correction), and the use of controlled quantum evolution for efficient
computation (quantum computation). The common theme of all these insights is the use of quantum
entanglement as a computational resource.
Keywords: quantum bits, quantum registers, quantum gates and quantum networks
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 Network Intrusion Detection System (NIDS) monitors a network for malicious activities or policy violations [1]. The Kernel-based Virtual Machine (KVM) is a full virtualization solution for Linux on x86 hardware virtualization extensions [2]. We design and implement a back-propagation network intrusion detection system in KVM. Compared to traditional Back Propagation (BP) NIDS, the Particle Swarm Optimization (PSO) algorithm is applied to improve efficiency. The results show an improved system in terms of recall and precision along with missing detection rates.
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.
Invited talk at workshop "Exascale Computing in Astrophysics" held in Ascona, Switzerland, 8-13 September 2013.
http://www.itp.uzh.ch/exastro2013/Home.html
Call for Chapters- Edited Book: Real World Challenges in Quantum Electronics ...Christo Ananth
Most experts would consider this the biggest challenge. Quantum computers are extremely sensitive to noise and errors caused by interactions with their environment. This can cause errors to accumulate and degrade the quality of computation. Developing reliable error correction techniques is therefore essential for building practical quantum computers. While quantum computers have shown impressive performance for some tasks, they are still relatively small compared to classical computers. Scaling up quantum computers to hundreds or thousands of qubits while maintaining high levels of coherence and low error rates remains a major challenge. Developing high-quality quantum hardware, such as qubits and control electronics, is a major challenge. There are many different qubit technologies, each with its own strengths and weaknesses, and developing a scalable, fault-tolerant qubit technology is a major focus of research. Funding agencies, such as government agencies, are rising to the occasion to invest in tackling these quantum computing challenges. Researchers — almost daily — are making advances in the engineering and scientific challenges to create practical quantum 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.
Generative AI for Social Good at Open Data Science East 2024Colleen Farrelly
A brief overview of generative AI technologies and their use for social good initiatives, including cultural training, medical image generation, drug design, and public health.
PyData Global 2023 talk overviewing case studies in network science, including stock market crash prediction, food price pattern mining, and stopping the spread of epidemics.
Overview of mathematical and machine learning models related to climate risk modeling, climate change simulations, and change point detection. Includes a hands-on session with geometry-based systems analysis of food prices related to climate change and geopolitical factors.
WiDS Workshop on natural language processing and generative AI. Details common methods that tie into coding examples. Ends with ethics discussion regarding these technologies and potential for misuse.
Link to talk YouTube: https://www.youtube.com/watch?v=byGzKm0H1-8&list=PLHAk3jHXWpxI7fHw8m5PhrpSRpR3NIjQo&index=3
ODSC-East 2023 presentation covering topics related to my book, The Shape of Data, including how geometry plays a role in text/image embeddings, network science problems, survey data analytics, image analytics, and epidemic wrangling.
This talk overviews my background as a female data scientist, introduces many types of generative AI, discusses potential use cases, highlights the need for representation in generative AI, and showcases a few tools that currently exist.
Emerging Technologies for Public Health in Remote Locations.pptxColleen Farrelly
The tools possible to leverage for public health interventions has changed significantly in the past decades. Tools from geometry, natural language processing, and generative AI allow for a quick design and implementation of interventions, even in very rural parts of the world. Case studies involve HIV, Ebola, and COVID interventions.
WoComToQC workshop lecture on Forman-Ricci curvature for applications in industry (social networks, disaster logistics, spatial data, and spatiotemporal goods pricing data).
PyData Global talk covering tools from geometry/topology and their uses in public health, public policy, and social good initiatives. Examples include food price prediction, COVID policies, public health interventions, and fair AI.
Data Science Dojo Talk on comparing time series using persistent homology. Short overview of time series data. A bit of topology. Code available. Example includes stock exchange data.
Statistical and topological algorithm piece of an Applied Machine Learning Days Morocco talk. Covers ARIMA models, SSA models, GEE models, and persistent homology. Applications include pricing data, stock data, development data, and healthcare data. Datasets and full presentation can be found on GitHub: https://github.com/gabayae/Time-Series-Applications_AMLD2022
Indaba Malawi workshop on basic approaches to time series data, including ARIMA models and SSA models. Example in R includes an agricultural example from historical Malawi data with Rssa package and base ARIMA models.
NLP: Challenges and Opportunities in Underserved AreasColleen Farrelly
This talk highlights the challenges and opportunities that exist in linguistically underserved areas. It highlights NLP initiatives in Sub-Saharan Africa, as well as financial opportunities in technology if areas neglected linguistically can produce tools in their local languages. Ethics, ownership, and other concerns are highlighted to guide development initiatives.
Geometry, Data, and One Path Into Data Science.pptxColleen Farrelly
Women in Data Science (Alexandria, Egypt) keynote address. Topics cover my journey into data science/machine learning, an overview of data science as a profession, and some case studies on topology/geometry in analytics. Example case studies include insurance, natural language processing, social network analysis, and psychometrics.
WiDS Alexandria, Egypt workshop in topological data analysis (Python and R code available on request), covering persistent homology, the Mapper algorithm, and discrete Ricci curvature. Examples include text data and social network data.
First part of a workshop looking at industry case studies in natural language processing for From Theory to Practice Workshop (AIMS, Kigali, March 2022).
SAS Global 2021 Introduction to Natural Language Processing Colleen Farrelly
Overview of text data, processing of text data, integration of text data with structured databases, and uses of text data in analytics across a variety of fields. Here's the talk link: https://www.youtube.com/watch?v=wS0X1bSsuUU
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
3. Quantum computing is a relatively new
field of computing with chips based on
quantum mechanics.
Some quantum computers exist already.
However, most extant quantum computers
are still too small of circuits to be practical.
Several different types of quantum
computers exist/are possible.
Each has its own strengths and
weaknesses on certain problems.
4. One approach replaces binary (0/1)
bits with a quantum version, the
qubit.
Qubits can take many different
values, depending on the operations
performed on them.
Superposition (quantum mechanics
property) allows a qubit to be in all
possible states at once.
This is helpful when computing
combinatorial solutions
(simultaneous search rather than
iterative).
Limited by number of qubits in the
circuit, though.
5. Practically, two types of qubit chips
exist:
Gate-based (IBM, Rigetti…)
Quantum-annealing-based (D-Wave)
Gate-based tends to be more accurate
in benchmarking.
Researchers can:
Gain access to the actual quantum
computers through the cloud
Simulate the circuits using a classical
computer and special Python
package.
6. A different type of quantum
circuit is possible using
continuous versions of qubits,
called qumodes.
These are photonic circuits, upon
which continuous transformations
can be made on the photon through
the circuit.
Information is stored in qubits.
Qumodes retrieve the information
and operate on it.
A functioning qumodes computer
doesn’t exist yet, but simulation
software is available in Python.
7. A short overview of common target algorithms on different types of
quantum computers
8. Supervised learning
Given a set of predictors, how can we
predict an outcome?
Which predictors are most important?
Unsupervised learning
Given a set of data, what relationships
can we find?
What clusters exist?
Network analysis
How are people connected to each other?
How is information passed among people
in the same social group?
9. Many machine learning algorithms focus on
supervised learning.
Algorithms learn the relationship between a set of
possible predictors and an outcome of interest.
Some examples include deep learning, random forest,
and logistic regression.
Most of these algorithms are rooted in generalized
linear models.
Qumodes applications (Xanadu) abound these
days, including quantum generalized linear
modeling, quantum deep learning, and quantum
boosted regression.
10. Unsupervised learning aims to either:
Learn groupings of data (by combining
individuals)
Learn reductions of the data (by combining
predictors)
Clustering algorithms are quite important
in unsupervised learning, including k-
means clustering.
Many qubit clustering-type algorithms
exist, including Rigetti’s quantum
clustering algorithm, qubit-based
persistent homology, and D-Wave’s semi-
supervised classification algorithm.
11. Graphs and network data are ubiquitous
today:
Social networks connecting people
Gene networks connecting genes/proteins
Epidemic networks
Ranking of individuals and ties between
individuals in the network is a key problem
in the study of graphs.
Stopping of epidemic spread in disease
networks
Disintegration of links between terror cells
Many quantum graph-based/network
analysis algorithms exist, particularly on
qubit systems:
Quantum max flow/min cut algorithms
Quantum coloring problems
Quantum clique-finding
12. Biamonte, J., Wittek, P., Pancotti, N., Rebentrost, P., Wiebe, N., & Lloyd, S. (2017).
Quantum machine learning. Nature, 549(7671), 195.
Farhi, E., & Harrow, A. W. (2016). Quantum supremacy through the quantum
approximate optimization algorithm. arXiv preprint arXiv:1602.07674.
Farrelly, C. M., & Chukwu, U. (2019). Benchmarking in Quantum Algorithms. Digitale
Welt, 3(2), 38-41.
Izaac, J., Quesada, N., Bergholm, V., Amy, M., &Weedbrook, C. (2018). Strawberry
Fields: A Software Platform for Photonic Quantum Computing. arXiv preprint
arXiv:1804.03159.
Killoran, N., Bromley, T. R., Arrazola, J. M., Schuld, M., Quesada, N., & Lloyd, S.
(2018). Continuous-variable quantum neural networks. arXiv preprint
arXiv:1806.06871.
Lloyd, S., Garnerone, S., &Zanardi, P. (2016). Quantum algorithms for topological and
geometric analysis of data. Nature communications, 7, 10138.
Pakin, S., & Reinhardt, S. P. (2018, June). A Survey of Programming Tools for D-Wave
Quantum-Annealing Processors. In International Conference on High Performance
Computing (pp. 103-122). Springer, Cham.
Zhang, D. B., Xue, Z. Y., Zhu, S. L., & Wang, Z. D. (2019). Realizing quantum linear
regression with auxiliary qumodes. Physical Review A, 99(1), 012331.