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

2026-04-09

AI Summary

This work addresses the challenge of efficiently preparing ground states on noisy intermediate-scale quantum (NISQ) devices, where manually designed variational imaginary time evolution (VITE) circuits are often limited by circuit depth and gate count. The authors propose the first framework that integrates deep reinforcement learning into the automated design of VITE circuits, formulating a multi-objective optimization problem that simultaneously minimizes energy expectation and circuit complexity. By combining double deep Q-networks (DDQN) with an adaptive thresholding mechanism, the method discovers non-intuitive yet hardware-aware circuit structures. On Max-Cut problems, it reduces gate count and circuit depth by 37% and 43% on average, respectively; for the hydrogen molecule (H₂), it achieves full configuration interaction (Full-CI) accuracy while maintaining significantly shallower circuits.

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.

Problem

• Quantum Circuit Design
• Imaginary Time Evolution
• NISQ Devices
• Ansatz Optimization
• Ground State Search

Innovation

• Deep Reinforcement Learning
• Variational Imaginary Time Evolution
• Automated Quantum Circuit Design
• NISQ
• Multi-objective Optimization