Shot and Architecture Adaptive Subspace Variational Quantum Eigensolver for Microwave Simulation

TL;DR

This paper introduces an adaptive variational quantum eigensolver framework that uses reinforcement learning and adaptive measurements to efficiently simulate microwave waveguide modes on NISQ quantum hardware, reducing resource use and improving accuracy.

Contribution

It presents a novel RL-based circuit design and adaptive shot allocation method for VQE, enhancing efficiency and robustness in microwave eigenmode simulations.

Findings

Achieves eigenvalue estimation errors as low as 10^{-8}

Demonstrates accurate TE and TM mode reconstructions

Reduces quantum resource consumption compared to fixed schemes

Abstract

Quantum computing offers a promising paradigm for electromagnetic eigenmode analysis, enabling compact representations of complex field interactions and potential exponential speedup over classical numerical solvers. Recent efforts have applied variational quantum eigensolver (VQE) based methods to compute waveguide modes, demonstrating the feasibility of simulating TE and TM field distributions on noisy intermediate-scale quantum (NISQ) hardware. However, these studies typically employ manually designed, fixed-depth parameterized quantum circuits and uniform measurement-shot strategies, resulting in excessive quantum resource consumption, limited circuit expressivity, and reduced robustness under realistic noise conditions. To address these limitations, we propose an architecture and shot adaptive subspace variational quantum eigensolver for efficient microwave waveguide eigenmode simulation on NISQ devices. The proposed framework integrates a reinforcement learning (RL) based circuit design strategy and an adaptive shot allocation mechanism to jointly reduce quantum resource overhead. Specifically, the RL agent autonomously explores the quantum circuit space to generate hardware-efficient parameterized quantum circuits, while the adaptive measurement scheme allocates sampling resources according to Hamiltonian term weights. Numerical experiments on three- and five-qubit systems demonstrate that the proposed framework achieves accurate estimation of TE and TM mode eigenvalues, with a minimum absolute error down to $10^{-8}$ and reconstructed field distributions under noiseless conditions in excellent agreement with classical electromagnetic solutions.