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

• URL: http://arxiv.org/abs/2405.12866v2
• Date: Mon, 19 Aug 2024 18:13:01 GMT
• Title: Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation
• Authors: Alon Kukliansky, Lukasz Cincio, Ed Younis, Costin Iancu,
• Abstract summary: QFactor-Sample replaces matrix-matrix operations with simpler $mathcalO(2n)$ circuit simulations. We demonstrate improved scalability and a reduction in compile time, achieving an average speedup factor of 69 for circuits with more than 8 qubits.
• Score: 0.7049738935364297
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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