Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation
- URL: http://arxiv.org/abs/2405.12866v1
- Date: Tue, 21 May 2024 15:32:16 GMT
- Title: Leveraging Quantum Machine Learning Generalization to Significantly Speed-up Quantum Compilation
- Authors: Alon Kukliansky, Lukasz Cincio, Ed Younis, Costin Iancu,
- Abstract summary: Existing numerical compilers use expensive $mathcalO(4n)$ matrix-matrix operations.
Inspired by recent advances in quantum machine learning (QML), 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 bf 69 for circuits with more than 8bits.
- 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 {\bf 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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