Matrix-Model Simulations Using Quantum Computing, Deep Learning, and Lattice Monte Carlo
[1];
[3];
[4];
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[6]
- Univ. of Michigan, Ann Arbor, MI (United States); RIKEN, Saitama (Japan)
- Stanford Univ., CA (United States)
- City College of New York, NY (United States)
- Pasadena City College, CA (United States)
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Univ. of Surrey, Guildford (United Kingdom)
Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes, and it underpins the only practical numerical approach to the study of complex high-dimensional supergravity theories. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the realization of a quantum theory of gravity. Moreover, a complete numerical understanding of the holographic duality and the emergence of geometric space-time features from microscopic degrees of freedom could pave the way for new discoveries in quantum information science. Euclidean lattice Monte Carlo simulations are the de facto numerical tool for understanding the spectrum of large matrix models and have been used to test the holographic duality. However, they are not tailored to extract dynamical properties or even the quantum wave function of the ground state of matrix models. Quantum computing and deep learning provide potentially useful approaches to study the dynamics of matrix quantum mechanics. If successful in the context of matrix models, these rapidly improving numerical techniques could become the new Swiss army knife of quantum gravity practitioners. In this paper, we perform the first systematic survey for quantum computing and deep-learning approaches to matrix quantum mechanics, comparing them to lattice Monte Carlo simulations. These provide baseline benchmarks before addressing more complicated problems. In particular, we test the performance of each method by calculating the low-energy spectrum.
- Research Organization:
- Brookhaven National Laboratory (BNL), Upton, NY (United States); National Quantum Information Science (QIS) Research Centers (United States). Co-design Center for Quantum Advantage (C2QA)
- Sponsoring Organization:
- Asian Office of Aerospace Research and Development (AOARD); Centers of Research Excellence in Science and Technology (CREST); Foundational Questions Institute (FQXi); Japan Science and Technology Agency (JST); Japan Society for the Promotion of Science (JSPS); Moonshot Research and Development; Nippon Telegraph and Telephone Corporation (NTT) Research; US Army Research Office (ARO); USDOE Office of Science (SC); USDOE Office of Science (SC), Office of Workforce Development for Teachers & Scientists (WDTS)
- Grant/Contract Number:
- SC0012704
- OSTI ID:
- 1982852
- Journal Information:
- PRX Quantum, Journal Name: PRX Quantum Journal Issue: 1 Vol. 3; ISSN 2691-3399
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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