Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning

2026/04/09 by Ryo Suzuki, Shohei Watabe · 1 voice
Computer Science · Physics and Astronomy · #cs.AI #cs.LG #quant-ph

paper · pdf · doi:10.48550/arxiv.2604.07951

Abstract

Efficient ground state search is fundamental to advancing combinatorial optimization problems and quantum chemistry. While the Variational Imaginary Time Evolution (VITE) method offers a useful alternative to Variational Quantum Eigensolver (VQE), and Quantum Approximate Optimization Algorithm (QAOA), its implementation on Noisy Intermediate-Scale Quantum (NISQ) devices is severely limited by the gate counts and depth of manually designed ansatz. Here, we present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN). Our approach treats circuit construction as a multi-objective optimization problem, simultaneously minimizing energy expectation values and optimizing circuit complexity. By introducing adoptive thresholds, we demonstrate significant hardware overhead reductions. In Max-Cut problems, our agent autonomously discovered circuits with approximately 37% fewer gates and 43% less depth than standard hardware-efficient ansatz on average. For molecular hydrogen (H₂), the DDQN also achieved the Full-CI limit, with maintaining a significantly shallower circuit. These results suggest that deep reinforcement learning can be helpful to find non-intuitive, optimal circuit structures, providing a pathway toward efficient, hardware-aware quantum algorithm design.

Citations

• A Reinforcement Learning Environment for Directed Quantum Circuit Synthesis
• Reinforcement learning for optimization of variational quantum circuit architectures
• Quantum circuit optimization with deep reinforcement learning
• Variational Quantum Algorithms
• Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution
• Theory of variational quantum simulation
• Quantum Computing in the NISQ era and beyond
• Proximal Policy Optimization Algorithms
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Deep Reinforcement Learning with Double Q-learning
• A Quantum Approximate Optimization Algorithm
• The Bravyi-Kitaev transformation for quantum computation of electronic structure
• Improved approximation algorithms for maximum cut and satisfiability problems using semidefinite programming

Discussions

• Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning This article represents a compelling convergence of AI and quantum computin [bsky, 0 points, 0 comments]

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