arXiv:2607.02292v1 Announce Type: new Abstract: Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods. Yet their optimization remains comparatively underexplored: Adam is a scalable method but ignores function space geometry, while stochastic reconfiguration is principled but costly and numerically fragile i
This paper reframes the optimization of Neural Quantum States (NQS) through a Reinforcement Learning lens, addressing current limitations in their practical application.
Improved optimization techniques for NQS could accelerate the development of quantum algorithms and simulations, impacting computational chemistry, materials science, and quantum computing hardware design.
The shift in perspective to reinforcement learning for NQS optimization offers new avenues to overcome current computational bottlenecks and enhance the efficiency of quantum state preparation.
- · Quantum computing researchers
- · Materials science
- · Computational chemists
- · AI/ML in scientific discovery
- · Traditional quantum simulation methods
- · Compute-intensive experimental verification
More accurate and scalable quantum simulations become feasible with better NQS optimization.
This could lead to breakthroughs in designing novel materials with tailored properties or discovering new drug candidates.
Ultimately, a more robust NQS framework could accelerate the timeline for fault-tolerant quantum computers by improving error correction or state preparation.
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Read at arXiv cs.LG