论文标题
使用量子计算机的量子最佳控制：具有机器学习优化的混合算法
Quantum optimal control with quantum computers: an hybrid algorithm featuring machine learning optimization
论文作者
论文摘要
我们开发了一种杂化量子古典算法，以解决受激光脉冲的分子的最佳种群转移问题。在量子计算机上模拟了激光脉冲下分子波函数的演变，而最佳脉冲是通过机器学习（进化）算法迭代形状的。一种在量子计算机上编码的方法，讨论了N-电机波函数，得出其量子模拟的电路，并讨论了操作数量的可扩展性。提供了嘈杂的中间量子量子设备（IBM Q X2）的性能，以评估当前的技术差距。此外，在量子模拟器上测试了混合算法，以将进化算法的性能与标准算法进行比较。我们的结果表明，这种算法能够使用下坡单纯形方法优于优化，并提供与Rabitz等更高级（但量子不友好）算法相当的性能。
We develop an hybrid quantum-classical algorithm to solve an optimal population transfer problem for a molecule subject to a laser pulse. The evolution of the molecular wavefunction under the laser pulse is simulated on a quantum computer, while the optimal pulse is iteratively shaped via a machine learning (evolutionary) algorithm. A method to encode on the quantum computer the n-electrons wavefunction is discussed, the circuits accomplishing its quantum simulation are derived and the scalability in terms of number of operations is discussed. Performance on Noisy Intermediate-Scale Quantum devices (IBM Q X2) is provided to assess the current technological gap. Furthermore the hybrid algorithm is tested on a quantum emulator to compare performance of the evolutionary algorithm with standard ones. Our results show that such algorithms are able to outperform the optimization with a downhill simplex method and provide performance comparable to more advanced (but quantum-computer unfriendly) algorithms such as Rabitz's.
