量子生成对抗自编码器可高效生成纠缠态与分子基态。
Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation
- 用量子自编码器压缩数据，再用对抗网络学习潜在表示。
- 生成氢分子和锂氢分子基态，能量误差分别低至0.02和0.06哈特里。
- 适合量子化学模拟与近中期量子机器学习研究者。
本文提出量子生成对抗自编码器（QGAA），用于生成量子数据。QGAA由两部分构成：(a) 量子自编码器（QAE）用于压缩量子态，(b) 量子生成对抗网络（QGAN）用于学习训练后QAE的潜在空间，赋予其生成能力。该方法在两类典型场景中得到验证：(a) 纯纠缠态生成，(b) 参数化分子基态生成（针对H₂和LiH）。在最多6个量子比特的仿真中，所训练的QGAA估算的能量平均误差分别为0.02哈特里（H₂）和0.06哈特里（LiH）。结果表明，QGAA在量子态生成、量子化学计算及近中期量子机器学习应用方面具有潜力。
原文摘要 · Abstract (English)
In this work, we introduce the Quantum Generative Adversarial Autoencoder (QGAA), a quantum model for generation of quantum data. The QGAA consists of two components: (a) Quantum Autoencoder (QAE) to compress quantum states, and (b) Quantum Generative Adversarial Network (QGAN) to learn the latent space of the trained QAE. This approach imparts the QAE with generative capabilities. The utility of QGAA is demonstrated in two representative scenarios: (a) generation of pure entangled states, and (b) generation of parameterized molecular ground states for H$_2$ and LiH. The average errors in the energies estimated by the trained QGAA are 0.02 Ha for H$_2$ and 0.06 Ha for LiH in simulations upto 6 qubits. These results illustrate the potential of QGAA for quantum state generation, quantum chemistry, and near-term quantum machine learning applications.
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