Recent advances in quantum computing have drawn considerable attention to
building realistic application for and using quantum computers. However,
designing a suitable quantum circuit architecture requires expert knowledge.
For example, it is non-trivial to design a quantum gate sequence for generating
a particular quantum state with as fewer gates as possible. We propose a
quantum architecture search framework with the power of deep reinforcement
learning (DRL) to address this challenge. In the proposed framework, the DRL
agent can only access the Pauli-$X$, $Y$, $Z$ expectation values and a
predefined set of quantum operations for learning the target quantum state, and
is optimized by the advantage actor-critic (A2C) and proximal policy
optimization (PPO) algorithms. We demonstrate a successful generation of
quantum gate sequences for multi-qubit GHZ states without encoding any
knowledge of quantum physics in the agent. The design of our framework is
rather general and can be employed with other DRL architectures or optimization
methods to study gate synthesis and compilation for many quantum states.

使用增强学习算法为量子电路架构设计提供了一个通用架构，可以在不编码任何关于量子物理的知识下，成功地生成多比特 GHZ 态的量子门序列。

深度强化学习量子体系结构搜索

Quantum Architecture Search via Deep Reinforcement Learning

We present a novel semi-analytical methodology to construct optimal linear
optical circuits for heralded production of 3-photon GHZ and 2-photon Bell
states. We provide a detailed description and analysis of the resulting optical
schemes, which deliver success probabilities of 1/54 and 2/27 for dual-rail
encoded 3-GHZ and Bell states generation, respectively. Our results improve the
known constructive bounds on the success probabilities for 3-GHZ states and are
of particular importance for a ballistic quantum computing model, for which
these states provide an essential resource.

本文提出了一种新的半解析方法，用于构建最优线性光学电路，以产生 3 光子 GHZ 态和 2 光子 Bell 态，并对光学方案进行了详细描述和分析，成功概率为 1/54 和 2/27，这些结论对于球形量子计算模型非常重要。