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.
The emergence of quantum reinforcement learning (QRL) is propelled by advancements in quantum computing (QC) and machine learning (ML), particularly through quantum neural networks (QNN) built on variational quantum circuits (VQC). These advancements have proven successful in addressing sequential decision-making tasks. However, constructing effective QRL models demands significant expertise due to challenges in designing quantum circuit architectures, including data encoding and parameterized circuits, which profoundly influence model performance. In this paper, we propose addressing this challenge with differentiable quantum architecture search (DiffQAS), enabling trainable circuit parameters and structure weights using gradient-based optimization. Furthermore, we enhance training efficiency through asynchronous reinforcement learning (RL) methods facilitating parallel training. Through numerical simulations, we demonstrate that our proposed DiffQAS-QRL approach achieves performance comparable to manually-crafted circuit architectures across considered environments, showcasing stability across diverse scenarios. This methodology offers a pathway for designing QRL models without extensive quantum knowledge, ensuring robust performance and fostering broader application of QRL.
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.
Many challenges arising in Quantum Technology can be successfully addressed using a set of machine learning algorithms collectively known as reinforcement learning (RL), based on adaptive decision-making through interaction with the quantum device. After a concise and intuitive introduction to RL aimed at a broad physics readership, we discuss the key ideas and core concepts in reinforcement learning with a particular focus on quantum systems. We then survey recent progress in RL in all relevant areas. We discuss state preparation in few- and many-body quantum systems, the design and optimization of high-fidelity quantum gates, and the automated construction of quantum circuits, including applications to variational quantum eigensolvers and architecture search. We further highlight the interactive capabilities of RL agents, emphasizing recent progress in quantum feedback control and quantum error correction, and briefly discuss quantum reinforcement learning as well as applications to quantum metrology. The review concludes with a discussion of open challenges -- such as scalability, interpretability, and integration with experimental platforms -- and outlines promising directions for future research. Throughout, we highlight experimental implementations that exemplify the increasing role of reinforcement learning in shaping the development of quantum technologies.