The content discusses the use of reinforcement learning (RL) for quantum circuit design, which comprises two main objectives: quantum architecture search (QAS) and quantum circuit optimization (QCO).
QAS involves finding a sequence of quantum gates to achieve a certain objective, such as preparing arbitrary quantum states or composing unitary operations. The authors formalize core RL objectives for QAS, including state preparation (SP) and unitary composition (UC), and propose a generic quantum circuit designer (QCD) environment as an MDP-compliant framework for RL.
QCO aims to optimize the circuit structure itself to reduce depth and gate count, while also accounting for hardware constraints like limited qubit connectivity and error-prone operations. The authors argue that RL can be effectively applied to address these QCO challenges.
The authors benchmark several state-of-the-art RL algorithms (A2C, PPO, SAC, TD3) on the proposed QCD environment, evaluating their performance on the SP and UC tasks, as well as more advanced challenges like random state preparation and Toffoli composition. The results reveal significant challenges for current RL approaches in effectively exploring the complex, high-dimensional action spaces and reward landscapes inherent to quantum circuit design.
The authors conclude that the QCD framework provides a solid foundation for future research on applying RL to quantum computing, while also highlighting key areas that need to be addressed, such as improving exploration mechanisms, incorporating multi-objective optimization, and developing more efficient state representations.
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