arXiv 2405.128662024-05quant-ph

利用量子机器学习泛化能力显著加速量子编译

Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation

Alon Kukliansky, Lukasz Cincio, Ed Younis, Costin Iancu

备注：: 15 pages, 8 figures, and 4 appendices

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AI总结：

针对量子编译器中现有数值优化器运算成本高的问题，提出QFactor-Sample方法，以低复杂度线路模拟替代矩阵运算，集成到BQSKit后对8比特以上线路平均提速69倍，还分析了其对分块编译方案动态特性的影响。

摘要：

当前部署在量子编译器中的数值优化器采用计算成本高昂的$\u0001D4AA(4^n)$矩阵-矩阵运算。受量子机器学习（QML）领域最新进展启发，QFactor-Sample（量子因子采样法）用一组采样输入上更简单的$\u0001D4AA(2^n)$线路模拟替代了矩阵-矩阵运算。量子线路越简单，所需的输入采样数量就越少。我们在大规模线路集合上验证了QFactor-Sample的性能，并讨论了其超参数调优方法。将该方法集成到BQSKit量子编译器中后，与当前最先进的领域专用优化器相比，我们证明其可扩展性得到提升，编译时间有所缩短，对于量子比特数超过8的线路，平均加速因子可达69。我们还探讨了改进的数值优化如何影响基于划分的编译方案的动态特性，这类方案可在编译速度与求解质量之间实现权衡。

英文摘要：

Existing numerical optimizers deployed in quantum compilers use expensive $\mathcal{O}(4^n)$ matrix-matrix operations. Inspired by recent advances in quantum machine learning (QML), QFactor-Sample replaces matrix-matrix operations with simpler $\mathcal{O}(2^n)$ circuit simulations on a set of sample inputs. The simpler the circuit, the lower the number of required input samples. We validate QFactor-Sample on a large set of circuits and discuss its hyperparameter tuning. When incorporated in the BQSKit quantum compiler and compared against a state-of-the-art domain-specific optimizer, We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits. We also discuss how improved numerical optimization affects the dynamics of partitioning-based compilation schemes, which allow a trade-off between compilation speed and solution quality.

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