Hey there….it’s been a while, hasn’t it? At the risk of sounding like I’m making excuses, I’ve been pretty busy these past few weeks. And while I do have drafts in progress for more posts on quantum-enhanced machine learning (inaugural post here), my upcoming schedule for the next few months suggests to me that I’ll be able to do only about 1 of those posts a month. My apologies for keeping you hanging. I’ll continue to work on those (in preparation: quantum neural networks, quantum generative models, and a final “Whither quantum-enhanced ML?” post).
In the meantime, I thought it might be nice to do a post which can act as a kind of literature review. Given that the number of papers published in quantum computing is quite large, an exhaustive survey would be impossible. But, collecting a bunch of review articles and/or tutorials which summarize the literature seems much more doable.
I’ve organized the references below based on loose topic areas: chemistry/physics/materials, finance, healthcare & life sciences, machine learning, and climate/energy. Of course, there might be cross-pollination amongst them. For that reason, I’ve excerpted parts of the abstracts, to make it easier to search and find papers which touch on a given topic. (Substack might need some kind of text collapse option!) Papers are ordered in reverse chronological order (newest at the top).
Any omissions are my own, and feel free to leave a comment highlighting a work you think should be considered for inclusion. Though the focus in this post is review articles and/or tutorials, not individual pieces of literature.
Before diving into the literature, a few consultancy reports which might be of interest:
From BCG, What Happens When ‘If’ Turns to ‘When’ in Quantum Computing? provides a pretty comprehensive overview of applications of quantum computers, key players, and value creation.
McKinsey has a similar report, called Quantum computing use cases are getting real—what you need to know.
Quantum Computing for Chemistry/Physics/Materials
2023 July Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
We present here three specific examples where the use of quantum computing and quantum machine learning provides, or could provide in the future, a possible computational advantage: i) the determination of the phase/shape in schematic nuclear models, ii) the calculation of the ground state energy of a nuclear shell model-type Hamiltonian and iii) the identification of particles or the determination of trajectories in nuclear physics experiments
In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future.
2023 March Quantum computing with and for many-body physics
In this review, we explain how quantum many-body systems are used to build quantum processors, and how, in turn, current and future quantum processors can be used to describe large many-body systems of fermions such as electrons and nucleons. The review includes an introduction to analog and digital quantum devices, the mapping of Fermi systems and their Hamiltonians onto qubit registers, as well as an overview of methods to access their static and dynamical properties.
2022 September Snowmass Computational Frontier: Topical Group Report on Quantum Computing
This report outlines how Quantum Information Science (QIS) and HEP are deeply intertwined endeavors that benefit enormously from a strong engagement together. Quantum computers do not represent a detour for HEP, rather they are set to become an integral part of our discovery toolkit. Problems ranging from simulating quantum field theories, to fully leveraging the most sensitive sensor suites for new particle searches, and even data analysis will run into limiting bottlenecks if constrained to our current computing paradigms. Easy access to quantum computers is needed to build a deeper understanding of these opportunities.
2022 August The basics of quantum computing for chemists
It is in this context that here we review and illustrate the basic aspects of quantum information and their relation to quantum computing insofar as enabling simulations of quantum chemistry. We consider some of the most relevant developments in light of these aspects and discuss the current landscape when of relevance to quantum chemical simulations in quantum computers.
2022 March Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research
Here we identify both the challenges and opportunities for developing quantum computing systems and software to advance HEP discovery science. We describe opportunities for the focused development of algorithms, applications, software, hardware, and infrastructure to support both practical and theoretical applications of quantum computing to HEP problems within the next 10 years.
2020 October Quantum Algorithms for Quantum Chemistry and Quantum Materials Science
In this review, we briefly describe central problems in chemistry and materials science, in areas of electronic structure, quantum statistical mechanics, and quantum dynamics that are of potential interest for solution on a quantum computer. We then take a detailed snapshot of current progress in quantum algorithms for ground-state, dynamics, and thermal-state simulation and analyze their strengths and weaknesses for future developments.
2020 March Quantum computational chemistry
This review provides a comprehensive introduction to both computational chemistry and quantum computing, bridging the current knowledge gap. Major developments in this area are reviewed, with a particular focus on near-term quantum computation. Illustrations of key methods are provided, explicitly demonstrating how to map chemical problems onto a quantum computer, and how to solve them. The review concludes with an outlook on this nascent field.
2019 August Quantum Chemistry in the Age of Quantum Computing
This Review provides an overview of the algorithms and results that are relevant for quantum chemistry. The intended audience is both quantum chemists who seek to learn more about quantum computing and quantum computing researchers who would like to explore applications in quantum chemistry.
Quantum Computing for Finance
2023 July Quantum computing for finance
We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques.
In this paper, we provide an overview of the recent work in the quantum finance realm from various perspectives. The applications in consideration are Portfolio Optimization, Fraud Detection, and Monte Carlo methods for derivative pricing and risk calculation. Furthermore, we give a comprehensive overview of the applications of quantum computing in the field of blockchain technology which is a main concept in fintech.
2022 June A Survey of Quantum Computing for Finance
This survey paper presents a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning, describing how these solutions, adapted to work on a quantum computer, can potentially help to solve financial problems, such as derivative pricing, risk modeling, portfolio optimization, natural language processing, and fraud detection, more efficiently and accurately.
2021 September Quantum Machine Learning for Finance
This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.
2021 January Quantum Computing for Finance: State of the Art and Future Prospects
This article outlines our point of view regarding the applicability, state-of-the-art, and potential of quantum computing for problems in finance. We provide an introduction to quantum computing as well as a survey on problem classes in finance that are computationally challenging classically and for which quantum computing algorithms are promising. In the main part, we describe in detail quantum algorithms for specific applications arising in financial services, such as those involving simulation, optimization, and machine learning problems.
2020 November Prospects and challenges of quantum finance
In this article we describe such potential applications of quantum computing to finance, starting with the state-of-the-art and focusing in particular on recent works by the QC Ware team. We consider quantum speedups for Monte Carlo methods, portfolio optimization, and machine learning. For each application we describe the extent of quantum speedup possible and estimate the quantum resources required to achieve a practical speedup. The near-term relevance of these quantum finance algorithms varies widely across applications - some of them are heuristic algorithms designed to be amenable to near-term prototype quantum computers, while others are proven speedups which require larger-scale quantum computers to implement.
Quantum Computing for Healthcare and Life Sciences
2023 August Towards quantum-enabled cell-centric therapeutics
Herein, we discuss the transformational changes we expect from the use of quantum computation for HCLS research, more specifically in the field of cell-centric therapeutics. Moreover, we identify and elaborate open problems in cell engineering, tissue modeling, perturbation modeling, and bio-topology while discussing candidate quantum algorithms for research on these topics and their potential advantages over classical computational approaches.
2023 August The state of quantum computing applications in health and medicine
In this review, clinical and medical proof-of-concept quantum computing applications are outlined and put into perspective. These consist of over 40 experimental and theoretical studies. The use case areas span genomics, clinical research and discovery, diagnostics, and treatments and interventions. Quantum machine learning (QML) in particular has rapidly evolved and shown to be competitive with classical benchmarks in recent medical research. Near-term QML algorithms have been trained with diverse clinical and real-world data sets. This includes studies in generating new molecular entities as drug candidates, diagnosing based on medical image classification, predicting patient persistence, forecasting treatment effectiveness, and tailoring radiotherapy. The use cases and algorithms are summarized and an outlook on medicine in the quantum era, including technical and ethical challenges, is provided.
2022 November Biology and medicine in the landscape of quantum advantages
Here, we distill the concept of a quantum advantage into a simple framework to aid researchers in biology and medicine pursuing the development of quantum applications. We then apply this framework to a wide variety of computational problems relevant to these domains in an effort to (i) assess the potential of practical advantages in specific application areas and (ii) identify gaps that may be addressed with novel quantum approaches. In doing so, we provide an extensive survey of the intersection of biology and medicine with the current landscape of quantum algorithms and their potential advantages. While we endeavour to identify specific computational problems that may admit practical advantages throughout this work, the rapid pace of change in the fields of quantum computing, classical algorithms and biological research implies that this intersection will remain highly dynamic for the foreseeable future.
2022 June Quantum network medicine: rethinking medicine with network science and quantum algorithms
As we will discuss, quantum computing may be a key ingredient in enabling the full potential of network medicine. We propose to combine network medicine and quantum algorithms in a novel research field, quantum network medicine, to lay the foundations of a new era of disease prevention and drug design.
2020 May The prospects of quantum computing in computational molecular biology
In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.
2019 November Quantum Computing at the Frontiers of Biological Sciences
Accordingly, we articulate a view towards quantum computation and quantum information science, where algorithms have demonstrated potential polynomial and exponential computational speedups in certain applications, such as machine learning. The maturation of the field of quantum computing, in hardware and algorithm development, also coincides with the growth of several collaborative efforts to address questions across length and time scales, and scientific disciplines. We use this coincidence to explore the potential for quantum computing to aid in one such endeavor: the merging of insights from genetics, genomics, neuroimaging and behavioral phenotyping. By examining joint opportunities for computational innovation across fields, we highlight the need for a common language between biological data analysis and quantum computing. Ultimately, we consider current and future prospects for the employment of quantum computing algorithms in the biological sciences.
Quantum Computing for Machine Learning
2023 March Quantum Machine Learning: from physics to software engineering
This review provides a two-fold overview of several key approaches that can offer advancements in both the development of quantum technologies and the power of artificial intelligence. Among these approaches are quantum-enhanced algorithms, which apply quantum software engineering to classical information processing to improve keystone machine learning solutions. In this context, we explore the capability of hybrid quantum-classical neural networks to improve model generalization and increase accuracy while reducing computational resources.
2018 January Quantum machine learning: a classical perspective
Here we review the literature in quantum ML and discuss perspectives for a mixed readership of classical ML and quantum computation experts. Particular emphasis will be placed on clarifying the limitations of quantum algorithms, how they compare with their best classical counterparts and why quantum resources are expected to provide advantages for learning problems. Learning in the presence of noise and certain computationally hard problems in ML are identified as promising directions for the field. Practical questions, such as how to upload classical data into quantum form, will also be addressed.
2017 September Machine learning & artificial intelligence in the quantum domain
Aside from quantum speed-up in data analysis, or classical ML optimization used in quantum experiments, quantum enhancements have also been demonstrated for interactive learning, highlighting the potential of quantum-enhanced learning agents. Finally, works exploring the use of AI for the very design of quantum experiments, and for performing parts of genuine research autonomously, have reported their first successes. Beyond the topics of mutual enhancement, researchers have also broached the fundamental issue of quantum generalizations of ML/AI concepts. This deals with questions of the very meaning of learning and intelligence in a world that is described by quantum mechanics. In this review, we describe the main ideas, recent developments, and progress in a broad spectrum of research investigating machine learning and artificial intelligence in the quantum domain.
2014 September An introduction to quantum machine learning
In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
2014 August The quest for a Quantum Neural Network
This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. It establishes requirements for a meaningful QNN and reviews existing literature against these requirements. It is found that none of the proposals for a potential QNN model fully exploits both the advantages of quantum physics and computing in neural networks.
Quantum Computing for Climate/Energy
2022 November QuEnergy: Exploring the role of quantum computing for the electric grid
Applications of quantum computing for the electric sector reported here are informed by analysis of perspectives from the quantum computing industry and electric sector. Applications of quantum computing to optimization, machine learning, and simulation may impact all segments of the electric sector. The most prevalent idea is the application of quantum simulation techniques to aid development of battery technologies.
2022 September Quantum computing for smart grid applications
Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications.
2022 June Quantum computing in power systems
This paper reviews the newly emerging application of quantum computing techniques in power systems. We present a comprehensive overview of existing quantum-engineered power analytics from different operation perspectives, including static analysis, transient analysis, stochastic analysis, optimization, stability, and control. We thoroughly discuss the related quantum algorithms, their benefits and limitations, hardware implementations, and recommended practices.
2021 July Quantum Computing Opportunities in Renewable Energy
We identify a few places where quantum computing is most likely to contribute to renewable energy problems: in simulation, in scheduling and dispatch, and in reliability analyses. The problems have the common theme that there are potential future issues concerning scalability of current approaches that quantum computing may address. We then recommend potentially fruitful areas of crossover research to advance applications of quantum computing and renewable energy.
2021 June Quantum technologies for climate change: Preliminary assessment
This preliminary report aims to identify potential high-impact use-cases of quantum technologies for climate change with a focus on four main areas: simulating physical systems, combinatorial optimization, sensing, and energy efficiency.
Wrap-Up
Congratulations on getting this far! I hope the above-referenced articles are useful to you. As you can see, there are quite a few ideas in the literature about how to use quantum computing acrosss a wide variety of applications. Whether they will pan out in practice is to be determined, of course, and there are no doubt many other problems for which quantum computing might be applied.
As noted at the start of this article, any omissions are my own. Feel free to suggest additional papers in the comments below.