Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
2026-04-09 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors created a computer program that uses a type of artificial intelligence to automatically design quantum circuits for solving optimization and chemistry problems more efficiently. Their method aims to reduce the number of operations and complexity in the circuits while still finding good solutions. They tested this on Max-Cut problems and molecular hydrogen, achieving simpler circuits compared to traditional designs. This approach helps make quantum algorithms more practical for current quantum computers with limited capabilities.
Variational Imaginary Time Evolution (VITE)Variational Quantum Eigensolver (VQE)Quantum Approximate Optimization Algorithm (QAOA)Noisy Intermediate-Scale Quantum (NISQ)Deep Reinforcement LearningDouble Deep-Q Networks (DDQN)Max-Cut ProblemQuantum CircuitMolecular Hydrogen (H2)Full Configuration Interaction (Full-CI)
Authors
Ryo Suzuki, Shohei Watabe
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.