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Bridge of $\Psi$'s introduces a generative model leveraging Schrödinger bridges to learn direct transformations between quantum circuits, bypassing traditional rule-based rewrite libraries. By training on adversarial datasets designed to challenge existing optimizers, the method achieves significant reductions in gate count and circuit depth compared to nine baseline approaches. This work marks a pivotal shift toward integrating frontier machine learning techniques directly into the quantum compilation stack for more efficient execution.
Bridge of $\Psi$'s: Quantum Circuit Optimization with Schr\"odinger Bridges
arXiv:2609.25947v1 Announce Type: cross Abstract: Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask whether a generative model can learn this transformation directly from examples, rather than selecting from a fixed rewrite library or rigid algebraic routines. We present Bridge of $\Psi$'s (BOPS), a generative model based on Schr\"odinger bridges, using a custom denoiser architecture, that learns a transformation from a source circuit into an equivalent optimized circuit. We train it on data constructed to be hard for existing optimizers, by applying rewrite rules backwards so that each input has a known lower-cost target. On held-out 8 qubits $\times$ 64 depth Clifford+$T$ circuits, BOPS reduces gate count by $2.46\times$ and depth by $2.45\times$ in geometric mean, outperforming all nine baseline optimizers. This constitutes the first generative model bridging quantum circuits and frontier machine learning methods, opening up the quantum compilation stack to learned optimization along multiple axes.
Abstract:Quantum circuit optimization replaces a circuit with an equivalent one of fewer gates and lower depth, reducing execution cost and error rate. We ask whether a generative model can learn this transformation directly from examples, rather than selecting from a fixed rewrite library or rigid algebraic routines. We present Bridge of $\Psi$'s (BOPS), a generative model based on Schrödinger bridges, using a custom denoiser architecture, that learns a transformation from a source circuit into an equivalent optimized circuit. We train it on data constructed to be hard for existing optimizers, by applying rewrite rules backwards so that each input has a known lower-cost target. On held-out 8 qubits $\times$ 64 depth Clifford+$T$ circuits, BOPS reduces gate count by $2.46\times$ and depth by $2.45\times$ in geometric mean, outperforming all nine baseline optimizers. This constitutes the first generative model bridging quantum circuits and frontier machine learning methods, opening up the quantum compilation stack to learned optimization along multiple axes.
Quantum Physics (quant-ph); Machine Learning (cs.LG)
arXiv:2609.25947 [quant-ph]
(or arXiv:2609.25947v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2609.25947 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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