论文标题
通过Levenberg Marquardt机器学习编程量子硬件
Programming Quantum Hardware via Levenberg Marquardt Machine Learning
论文作者
论文摘要
宏观量子计算，噪声，反矫正和缩放的硬件问题，误差校正问题以及最重要的是算法构建的软件问题，仍然存在重大挑战。找到真正的量子算法是非常困难的，许多量子算法（例如shor Prime保理或相位估计）都需要任何实际应用的电路深度，因此需要进行误差校正。机器学习可以用作非Algorithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmothmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmithmitmithmithmithmithmitmithmentmentment的。量子机学习使我们能够执行计算，而无需将算法分解到其门构建块中，从而消除了这一困难的步骤并可能降低了不必要的复杂性。此外，我们已经证明了我们的机器学习方法对噪声和腐蚀性都是可靠的，这是在固有嘈杂的NISQ设备上运行的理想选择，该设备在可用于校正的量子数中受到限制。我们使用基本非经典计算证明了这一点，从实验上估算了未知量子状态的纠缠。该结果已成功移植到IBM硬件，并使用强大的混合增强学习技术进行了培训，这是一种修改后的Levenberg Marquardt LM方法。 LM方法非常适合量子机学习，因为它仅需要了解量子计算的最终测量输出，而不是通常无法访问的中间量子状态。由于它同时处理所有学习数据，因此量子硬件的命中率也大大减少。通过模拟的结果证明了机器学习，并在IBM Qiskit硬件接口上运行。
Significant challenges remain with the development of macroscopic quantum computing, hardware problems of noise, decoherence, and scaling, software problems of error correction, and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many quantum algorithms, like Shor prime factoring or phase estimation, require extremely long circuit depth for any practical application, necessitating error correction. Machine learning can be used as a systematic method to nonalgorithmically program quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate building blocks, eliminating that difficult step and potentially reducing unnecessary complexity. In addition, we have shown that our machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrated this using a fundamentally non classical calculation, experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a powerful hybrid reinforcement learning technique which is a modified Levenberg Marquardt LM method. The LM method is ideally suited to quantum machine learning as it only requires knowledge of the final measured output of the quantum computation, not intermediate quantum states which are generally not accessible. Since it processes all the learning data simultaneously, it also requires significantly fewer hits on the quantum hardware. Machine learning is demonstrated with results from simulations and runs on the IBM Qiskit hardware interface.
