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

• URL: http://arxiv.org/abs/2604.07951v1
• Date: Thu, 09 Apr 2026 08:17:11 GMT
• Title: Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
• Abstract summary: We present an automated framework for VITE circuit design using Double Deep-Q Networks (DDQN)<n>Our approach treats circuit construction as a multi-objective problem, simultaneously minimizing energy expectation values and optimizing circuit complexity.<n>In Max-Cut problems, our agent autonomously discovered circuits with approximately 37% fewer gates and 43% less depth than standard hardware-efficient ansatz.
• Score: 6.1990010478895
• License: http://creativecommons.org/licenses/by/4.0/
• 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 ($H_2$), 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.

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