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An introduction to quantum computing for high energy physics

Davide Provasoli

Published Jan 1, 2020• 1 citation
OpenAlex

Abstract

Applications of quantum computing to high energy physics (HEP) is a relatively new field of research. As such, the relevant literature is not well organized in one place and a clear road-map for people approaching the field is not currently available. Addressing this issue was the main motivation for this article, which is intended a pedagogical introduction to the field and specifically to our research direction, i.e. application of quantum computing to parton shower event generators, with the hope of more people becoming interested in exploring this path. Our paper on the subject, “A quantum algorithm for high energy physics simulations” [8], is the subject of chapter 4. I hope this article can provide a direct path for people familiar with quantum mechanics and quantum field theory to start reading papers or conducting research in this area of quantum computing applications in HEP. In this regard, the quantum algorithm we present in Chapter 4, can serve as an example of development of a novel efficient quantum algorithm to solve a problem in a particle physics model.

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Applications of quantum computing to high energy physics (HEP) is a relatively new field of research. As such, the relevant literature is not well organized in one place and a clear road-map for people approaching the field is not currently available. Addressing this issue was the main motivation for this article, which is intended a pedagogical introduction to the field and specifically to our research direction, i.e. application of quantum computing to parton shower event generators, with the hope of more people becoming interested in exploring this path. Our paper on the subject, “A quantum algorithm for high energy physics simulations” [8], is the subject of chapter 4. I hope this article can provide a direct path for people familiar with quantum mechanics and quantum field theory to start reading papers or conducting research in this area of quantum computing applications in HEP. In this regard, the quantum algorithm we present in Chapter 4, can serve as an example of development of a novel efficient quantum algorithm to solve a problem in a particle physics model.
We discuss general criteria that could guide us in applying quantum algorithms/computers to problems in high-energy physics. We then discuss the particular example of parton showers with quantum interference. We summarize the basic ideas behind the classical and quantum parton shower Monte Carlo algorithms and highlight the importance of quantum/classical hybrid algorithms. Our finding in quantum parton showers could serve as a useful case study for further exploration of quantum algorithms/computers in high-energy physics.
This PhD thesis explores the potential of quantum computing to address computational challenges in high-energy physics (HEP). As the Standard Model (SM) leaves key questions unanswered and no signs of new physics have emerged since the Higgs boson discovery, advanced experimental and computational tools become necessary. Quantum computing offers a promising alternative to classical methods, and this work investigates three main avenues where quantum algorithms can be useful in HEP research. First, we develop quantum subroutines for computing Minkowski distances and identifying maximum values in unsorted data, inserting them into clustering algorithms such as kkk-means, Affinity Propagation, and kTk_TkT​-jet clustering. These quantum algorithms match classical performance while offering theoretical advantages when implemented on quantum hardware with qRAM. Then, we introduce a novel quantum Monte Carlo integrator, Quantum Fourier Iterative Amplitude Estimation (QFIAE), which combines a quantum neural network with amplitude estimation to integrate multivariate functions. QFIAE is executed on simulators and real quantum computers to evaluate Feynman loop integrals via Loop-Tree Duality, and is extended to compute (partially on hardware) a physical observable at NLO in perturbative quantum field theory. Finally, we present Quantum Chebyshev Probabilistic Models (QCPMs) for modeling multivariate distributions, applying them to fragmentation functions of partons fragmenting into kaons and pions. These models demonstrate accurate generative and interpolation capabilities. We also show how entanglement between variables plays a key role in training, enhancing model accuracy. Overall, these results show how quantum algorithms can already tackle relevant HEP problems on current hardware, while paving the way for future fault-tolerant applications that fully exploit quantum computational advantages.
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena.Realizing these ideals will require the development of novel computational tools for modeling and simulation, detection and classification, data analysis, and forecasting of high-energy physics (HEP) experiments.While the emerging hardware, software, and applications of quantum computing are exciting opportunities, significant gaps remain in integrating such techniques into the HEP community research programs.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.
Quantum computing has emerged as a promising framework for addressing computationally demanding problems in collider physics. In recent years, a growing number of quantum algorithms have been proposed for applications ranging from event generation and parton shower simulation to the evaluation of scattering amplitudes, loop and phase-space integration, and optimization problems relevant to experimental analysis. We provide a concise overview of the main ideas behind these developments, with emphasis on the potential advantages of quantum approaches in comparison with classical methods, as well as on the current limitations imposed by noisy intermediate-scale quantum hardware.
Simulating quantum field theories is a flagship application of quantum computing. However, calculating experimentally relevant high energy scattering amplitudes entirely on a quantum computer is prohibitively difficult. It is well known that such high energy scattering processes can be factored into pieces that can be computed using well established perturbative techniques, and pieces which currently have to be simulated using classical Markov chain algorithms. These classical Markov chain simulation approaches work well to capture many of the salient features, but cannot capture all quantum effects. To exploit quantum resources in the most efficient way, we introduce a new paradigm for quantum algorithms in field theories. This approach uses quantum computers only for those parts of the problem which are not computable using existing techniques. In particular, we develop a polynomial time quantum final state shower that accurately models the effects of intermediate spin states similar to those present in high energy electroweak showers with a global evolution variable. The algorithm is explicitly demonstrated for a simplified quantum field theory on a quantum computer.
With the dramatic growth of experimental data in high-energy physics and the increasing demand for accuracy of physical analysis results, the current classical computing model and computing power can no longer meet the needs of data processing and analysis. As a new computing device with great potential, quantum computers have become one of the possible ways to solve the bottleneck of high-energy physics data processing and computation, though quantum computers are still far away from general computing. At such a stage that quantum computers have so many shortcomings like few physical qubits, short coherent time, and poor fidelity to solve most of practical problems, it's necessary to build a development and simulation platform using classical computers to accelerate research on quantum algorithms and applications. We've set up an interactive development platform for quantum algorithms research and quantum computing simulation, and are preparing one web portal for public education and training on quantum computing. In the future, we will pay more attention to quantum computing applications in HEP and QCD simulation.