浅深度变分量子假设检验
Shallow-Depth Variational Quantum Hypothesis Testing
AI总结：
本文提出浅深度变分量子算法，以单次鉴别成功概率联合优化态制备与测量，在资源受限量子照明中达到最优双模探针性能，并扩展至多假设鉴别。
AI中文摘要：
我们提出一种变分量子算法，用于区分编码为量子信道的若干假设。态制备和测量同时被优化，采用单次鉴别成功概率作为目标函数，该函数可通过局域化测量计算。在受约束信号模式光子数量子照明下，我们通过模拟玻色子电路，达到了已知最优双模探针的性能。结果表明，变分算法能够在资源受限条件下为二元假设检验制备最优态。除二元假设检验场景外，我们还证明该变分算法能够学习并鉴别多个假设。
英文摘要：
We present a variational quantum algorithm for differentiating several hypotheses encoded as quantum channels. Both state preparation and measurement are simultaneously optimized using success probability of single-shot discrimination as an objective function which can be calculated using localized measurements. Under constrained signal mode photon number quantum illumination we match the performance of known optimal 2-mode probes by simulating a bosonic circuit. Our results show that variational algorithms can prepare optimal states for binary hypothesis testing with resource constraints. Going beyond the binary hypothesis testing scenario, we also demonstrate that our variational algorithm can learn and discriminate between multiple hypotheses.
