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[2507.16641] Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
Summary
This article presents a novel reinforcement learning framework for synthesizing quantum circuits efficiently, addressing challenges in the NISQ era and future quantum computing.
Why It Matters
As quantum computing advances, efficient circuit synthesis becomes crucial for practical applications. This research introduces a hybrid reward-driven approach that improves circuit optimization, which is essential for leveraging quantum technologies effectively.
Key Takeaways
- Introduces a reinforcement learning framework for quantum circuit synthesis.
- Utilizes a hybrid reward mechanism to optimize circuit efficiency.
- Demonstrates effectiveness through benchmarking on graph-state preparation tasks.
- Achieves minimal-depth circuits with optimized gate counts.
- Adapts well to universal gate sets, showcasing robustness.
Quantum Physics arXiv:2507.16641 (quant-ph) [Submitted on 22 Jul 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis Authors:Sara Giordano, Kornikar Sen, Miguel A. Martin-Delgado View a PDF of the paper titled Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis, by Sara Giordano and 2 other authors View PDF HTML (experimental) Abstract:A reinforcement learning (RL) framework is introduced for the efficient synthesis of quantum circuits that generate specified target quantum states from a fixed initial state, addressing a central challenge in both the Noisy Intermediate-Scale Quantum (NISQ) era and future fault-tolerant quantum computing. The approach utilizes tabular Q-learning, based on action sequences, within a discretized quantum state space, to effectively manage the exponential growth of the space dimension. The framework introduces a hybrid reward mechanism, combining a static, domain-informed reward that guides the agent toward the target state with customizable dynamic penalties that discourage inefficient circuit structures such as gate congestion and redundant state revisits. This is a circuit-aware reward, in contrast to the current trend of works on this topic, which are primarily fidelity-based. By leveraging sparse matrix representations and state-space discretization, the method enables practical navigation of high-dimensional enviro...
