Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics
Abstract
The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time t, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths.
Versions
Related
- Machine Learning-Assisted Automated Parametric Modeling, Simulation, and Structural Optimization of On-Chip Inductors
- Artificial Intelligence-Assisted Engineering of Nanocomposite Corrosion Protection Materials: A Critical Review of Machine Learning, Molecular Simulation, and Experimental Verification
- A Molecular Generation–Validation Workflow Combining Reversible Junction Tree Reinforcement Learning and Semi-empirical Quantum Chemistry: A Case Study on Molecular Solar Thermal Fuels
- Revealing the H2O/Na environments in Hydrated Manganese Iron Prussian White: a D/H exchange and machine-learning-assisted MD simulation study
- Cross-Linking Simulation at Metal Oxide-Thermoset Interfaces Using Quantum Chemical and Molecular Dynamics Methods
BibTeX
@misc{zhang2026reinforcement,
title = {{Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics}},
author = {Jiaji Zhang and Lipeng Chen and Carlos L. Benavides-Riveros},
howpublished = {arXiv},
year = {2026},
month = may,
doi = {10.48550/arxiv.2605.18569},
eprint = {2605.18569},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.18569},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Reinforcement Learning Assisted Quantum Simulation of Many-Body Excited States and Real-Time Dynamics
AU - Jiaji Zhang
AU - Lipeng Chen
AU - Carlos L. Benavides-Riveros
PY - 2026
DA - 2026/05/18
PB - arXiv
DO - 10.48550/arxiv.2605.18569
UR - https://arxiv.org/abs/2605.18569
AB - The computation of electronic excited states and real-time quantum dynamics of many-fermion systems is among the most promising applications of near-term quantum computing. In this work, we generalize the reinforcement learning contracted quantum eigensolver (RL-CQE), previously developed for ground-state problems, to electronic excited states and real-time quantum dynamics, in which a deep Q-network agent adaptively selects the two-body operators at each iteration, yielding more compact ansätze and improved robustness with respect to critical hyperparameters. A key feature of the algorithm is a scalable state representation based on the ACSE residuals, whose dimension grows with the one-particle basis but remains independent of the number of targeted excited states. We also verify the equivalence of sign-free qubit operators in the excited-state setting, extending a result previously established for ground-state problems. Our RL-CQE for time evolution derives from a constant-scaling ansatz that represents the wave function with a fixed number of unitary transformations independent of simulation time t, enabled by the shared unitary structure of the purified ensemble treatment of excited states. Benchmarks on chemical systems demonstrate chemical accuracy with minimal operator counts across a range of bond lengths.
ER -