Contextualized Quantum Circuit Optimization via Reinforcement Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum circuit optimization remains a significant challenge in the development of practical quantum computing. Traditional methods often rely on heuristics or exhaustive searches, which are computationally expensive and may not effectively adapt to the specific characteristics of the quantum data being processed. This paper proposes a novel approach leveraging reinforcement learning (RL) to achieve contextualized quantum circuit optimization. The core idea is to train an RL agent to dynamically modify quantum circuits in real-time, guided by a reward function that balances circuit fidelity and execution time. Crucially, the agent incorporates a learned contextual representation of the input data, allowing it to tailor the circuit design to the specific context. We demonstrate the potential of this approach by outlining the key components, the reward function design, and the overall architecture. The potential benefits include improved circuit fidelity, reduced execution time, and the ability to adapt to diverse quantum datasets. This represents a shift from static circuit design to adaptive, context-aware optimization, potentially unlocking the full potential of quantum computation.

DOI: 10.5281/zenodo.22202058

Related papers

Adaptive Quantum Circuit Compilation with Reinforcement Learning

Jincheng Zhang

This paper presents a novel approach to quantum circuit compilation leveraging reinforcement learning (RL). The core idea is to train an RL agent to dynamically optimize quantum circuit structures, aiming to minimize gate count and enhance overall circuit performance. Traditional quantum circuit compilation methods often rely on static heuristics and pre-defined optimization strategies, which may not be optimal for all quantum algorithms or hardware platforms. Our approach addresses this limitation by allowing the RL agent to learn a policy that adapts to the specific characteristics of the algorithm and the target quantum hardware. The agent learns by interacting with a simulated quantum circuit environment, receiving rewards based on circuit fidelity, depth, and gate count. The results demonstrate the potential of RL to significantly improve the efficiency and robustness of quantum computations. The key components of the system are: a high-level quantum algorithm representation, an RL agent, a quantum circuit simulator, and a reward function. The agent's policy is represented as a neural network, enabling it to generalize across different algorithms and hardware. We demonstrate the effectiveness of this approach through simulations, showcasing its ability to outperform conventional compilation methods in terms of reducing circuit depth and gate count while maintaining high fidelity.

Sep 5, 20260 citations

Dynamic Quantum Circuit Compilation via Reinforcement Learning

Jincheng Zhang

Quantum circuit compilation is a crucial step in realizing quantum algorithms on real quantum hardware. Traditionally, compilation methods are largely static, relying on pre-defined mappings and optimizations that fail to account for the inherent noise and variability present in quantum devices. This paper proposes a novel approach leveraging Reinforcement Learning (RL) to dynamically generate and optimize quantum circuits. An RL agent is trained to learn a policy for circuit design, adapting to specific hardware characteristics and real-time measurement data. The agent's goal is to minimize the overall error rate of the compiled circuit. This dynamic compilation framework offers a feedback loop between hardware performance and circuit design, resulting in adaptive and robust quantum computation. The core claim of this work is that traditional static compilation methods are insufficient, and dynamic compilation driven by RL offers a superior solution. The approach presented here represents a significant advancement towards realizing the full potential of quantum computing by mitigating the effects of hardware imperfections. The framework includes a detailed description of the RL agent architecture, the reward function design, and the exploration strategy employed. The effectiveness of the proposed method is demonstrated through simulations, highlighting its ability to outperform static compilation strategies in noisy environments.

Sep 5, 20260 citations

Adaptive Quantum Circuit Compilation via Reinforcement Learning

Jincheng Zhang

Quantum circuit compilation is a critical step in realizing quantum algorithms on near-term quantum hardware. The process of translating high-level quantum algorithms into low-level gate sequences, while minimizing gate count and circuit depth, is often performed using deterministic algorithms that struggle to adapt to the diverse and often imperfect characteristics of available quantum devices. This paper proposes a novel approach to quantum circuit compilation utilizing reinforcement learning (RL). An RL agent is trained to dynamically adapt to hardware constraints, such as qubit connectivity and gate decomposition costs, optimizing circuit fidelity and execution time. The core claim is that RL can significantly improve quantum circuit compilation by introducing a dynamic and adaptive compilation process. The agent learns a policy that maps high-level quantum algorithms to optimized low-level gate sequences, effectively bridging the gap between theoretical algorithms and practical hardware execution. This work demonstrates the potential of RL to overcome the limitations of traditional deterministic compilation methods and paves the way for more efficient and robust quantum algorithm implementation.

Aug 28, 20260 citations

Adaptive Quantum Circuit Design – Optimizing for Circuit Length and Fidelity

Jincheng Zhang

Quantum circuit design presents a significant challenge due to the inherent complexity of quantum systems and the need to balance circuit length with fidelity. Traditional methods often rely on fixed parameter settings, limiting the potential for substantial improvements. This paper introduces an adaptive quantum circuit design algorithm leveraging reinforcement learning to dynamically adjust circuit parameters during synthesis, optimizing for both length and fidelity. We demonstrate the effectiveness of this approach through a comprehensive analysis of circuit designs and performance metrics, highlighting its potential to significantly enhance both circuit length and overall quality. The core mechanism centers around a reinforcement learning agent that learns to optimize circuit parameters based on the current state of the circuit, leading to more efficient and robust designs.

Aug 31, 20260 citations

Hybrid reward-driven reinforcement learning for efficient quantum circuit synthesis

Sara Giordano, Kornikar Sen, Miguel Angel Martin-Delgado

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 environments while minimizing computational overhead. Benchmarking on graph-state preparation tasks for up to seven qubits, we demonstrate that the algorithm consistently discovers minimal-depth circuits with optimized gate counts. Moreover, extending the framework to a universal gate set still yields low depth circuits, highlighting the algorithm’s robustness and adaptability. The results confirm that this RL-driven approach, with our completely circuit-aware method, efficiently explores the complex quantum state space and synthesizes near-optimal quantum circuits, providing a resource-efficient foundation for quantum circuit optimization.

arXiv:2507.16641Quantum Machine IntelligenceFeb 3, 20260 citations

Quantum-Inspired Optimization for Reinforcement Learning Reward Shaping

Jincheng Zhang

Reward shaping is a critical component in reinforcement learning (RL), aiming to guide agents towards desired behaviors by modifying the reward function. However, designing effective reward shaping functions can be a challenging and time-consuming process, often requiring significant domain expertise. This paper proposes a novel approach leveraging quantum-inspired optimization techniques to automate and improve the reward shaping process for RL agents. Specifically, we utilize amplitude estimation, a technique inspired by quantum computing, to iteratively refine reward functions based on agent interactions. The core idea is to treat the reward function as a parameter to be optimized using a quantum-inspired algorithm, effectively learning a reward function that best aligns with the agent's learning objective. We demonstrate, through theoretical analysis and a conceptual framework, the potential benefits of this approach in accelerating learning and improving the efficiency of RL algorithms. The proposed method offers a promising alternative to traditional reward shaping techniques, particularly in complex environments where designing hand-crafted reward functions is difficult. The evaluation of this approach is left for future work, focusing on practical implementation and comparative analysis with existing reward shaping strategies.

Aug 29, 20260 citations