用神经网络搜索思想自动设计量子电路，提升效率与性能。
Neural Architecture Search Algorithms for Quantum Autoencoders
- 借鉴神经网络架构搜索，自动寻找高效量子电路。
- 在三种任务上均超越基线，实现更优压缩与去噪效果。
- 适合量子算法设计者快速构建高性能量子模型。
当前量子电路设计依赖于特定量子算法的目标，需大量人工干预，难以扩展至复杂任务且易引入偏差。为此，我们受神经架构搜索（NAS）启发，提出两种量子-神经架构搜索（Quantum-NAS）算法，旨在自动为给定量子任务寻找高效电路。以量子数据压缩为驱动任务，实验表明所提算法在三项任务——量子数据去噪、经典数据压缩和纯量子数据压缩中，均能生成优于基线的自动编码器设计。结果表明，量子NAS可显著减少人工设计负担，同时生成高性能量子电路，适用于各类量子任务。
原文摘要 · Abstract (English)
The design of quantum circuits is currently driven by the specific objectives of the quantum algorithm in question. This approach thus relies on a significant manual effort by the quantum algorithm designer to design an appropriate circuit for the task. However this approach cannot scale to more complex quantum algorithms in the future without exponentially increasing the circuit design effort and introducing unwanted inductive biases. Motivated by this observation, we propose to automate the process of cicuit design by drawing inspiration from Neural Architecture Search (NAS). In this work, we propose two Quantum-NAS algorithms that aim to find efficient circuits given a particular quantum task. We choose quantum data compression as our driver quantum task and demonstrate the performance of our algorithms by finding efficient autoencoder designs that outperform baselines on three different tasks - quantum data denoising, classical data compression and pure quantum data compression. Our results indicate that quantum NAS algorithms can significantly alleviate the manual effort while delivering performant quantum circuits for any given task.
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