Artificial-Intelligence-Driven Shot Reduction in Quantum Measurement

TL;DR

This paper introduces a reinforcement learning approach to optimize shot allocation in Variational Quantum Eigensolver (VQE), significantly reducing measurement costs while maintaining accuracy, and demonstrating transferability across different quantum systems.

Contribution

It presents a novel RL-based method for automatic shot assignment in VQE, reducing reliance on heuristics and human expertise, and enabling scalable quantum optimization.

Findings

RL policy reduces measurement shots in VQE

Policy transfers across different molecules and ansatzes

Potential of RL for scalable quantum optimization

Abstract

Variational Quantum Eigensolver (VQE) provides a powerful solution for approximating molecular ground state energies by combining quantum circuits and classical computers. However, estimating probabilistic outcomes on quantum hardware requires repeated measurements (shots), incurring significant costs as accuracy increases. Optimizing shot allocation is thus critical for improving the efficiency of VQE. Current strategies rely heavily on hand-crafted heuristics requiring extensive expert knowledge. This paper proposes a reinforcement learning (RL) based approach that automatically learns shot assignment policies to minimize total measurement shots while achieving convergence to the minimum of the energy expectation in VQE. The RL agent assigns measurement shots across VQE optimization iterations based on the progress of the optimization. This approach reduces VQE's dependence on static heuristics and human expertise. When the RL-enabled VQE is applied to a small molecule, a shot reduction policy is learned. The policy demonstrates transferability across systems and compatibility with other wavefunction ansatzes. In addition to these specific findings, this work highlights the potential of RL for automatically discovering efficient and scalable quantum optimization strategies.