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Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo

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arxiv 2108.02942 v1 pith:YLMTBX4C submitted 2021-08-06 quant-ph hep-lathep-th

Enrico Rinaldi , Xizhi Han , Mohammad Hassan , Yuan Feng , Franco Nori , Michael McGuigan , Masanori Hanada This is my paper
classification quant-phhep-lathep-th
keywords quantummatrixcomputingdeeplearningmechanicsapproachesblack
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Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the development of better quantum algorithms (quantum error correction codes) and for the realization of a quantum theory of gravity. Quantum computing and deep learning offer us potentially useful approaches to study the dynamics of matrix quantum mechanics. In this paper we perform a systematic survey for quantum computing and deep learning approaches to matrix quantum mechanics, comparing them to Lattice Monte Carlo simulations. In particular, we test the performance of each method by calculating the low-energy spectrum.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer

    quant-ph 2026-04 conditional novelty 8.0 of 10

    First digital quantum simulation of SU(2) matrix model real-time dynamics on Quantinuum H2 using Loschmidt echo, with systematic error breakdown and modest post-selection gains.

  2. Simulating the dynamics of an SU(2) matrix model on a trapped-ion quantum computer

    quant-ph 2026-04 unverdicted novelty 6.5 of 10

    First digital quantum simulation of an SU(2) bosonic matrix model on Quantinuum H2, with Loschmidt-echo dynamics and a three-way error budget showing post-selection and ZNE give only modest, non-scaling gains.

  3. High-Precision Bootstrap of Multimatrix Quantum Mechanics

    hep-th 2025-07 conditional novelty 6.0 of 10

    Semidefinite bootstrap bounds fix the large-N ground-state energy and ⟨trX²⟩ of bosonic matrix quantum mechanics to up to eight significant digits.

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