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

TL;DR

This paper presents an automated deep reinforcement learning framework for designing quantum circuits for imaginary time evolution, reducing gate counts and circuit depth on NISQ devices.

Contribution

It introduces a novel DDQN-based method that optimizes quantum circuit design as a multi-objective problem, improving efficiency and hardware compatibility.

Findings

Achieved 37% fewer gates and 43% less depth in Max-Cut circuits.

Successfully reached the Full-CI limit for molecular hydrogen.

Demonstrated the method's ability to discover non-intuitive, optimized circuit structures.

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.