[2507.16001] Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design

Quantum Physics arXiv:2507.16001 (quant-ph) [Submitted on 21 Jul 2025 (v1), last revised 1 Mar 2026 (this version, v2)] Title:Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design Authors:Gloria Turati, Simone Foderà, Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi View a PDF of the paper titled Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design, by Gloria Turati and 4 other authors View PDF Abstract:As quantum computing continues to gain attention, there is growing interest in how classical machine learning can assist quantum workflows in practice. Automated circuit design, sometimes referred to as Quantum Architecture Search (QAS), is a natural application but relies on the ability to model the quantum system to support learning as the number of qubits grows. This challenge is central to QAS, and much of the current literature that proposes new ways to model the ansatz focuses on small systems, often around ten qubits. In this work, we propose a complementary approach that separates a small-scale structure discovery phase, where a reusable modular circuit block is learned on small instances where classical learning is feasible, from a deployment phase, where the blocks are used to create the ansatz required for larger problems. To this end, we introduce Reinforcement Learning for Variational Quantum Circuits (RLVQC), formulating QAS as a...