Problem
Research questions and friction points this paper is trying to address.
Quantum Circuit Optimization
Generative Model
Schrödinger Bridges
Bridge of $Ψ$'s: Quantum Circuit Optimization with Schrödinger Bridges
🤖 AI Summary
该研究使用基于Schrödinger桥梁的生成模型BOPS，从示例中学习量子电路优化，有效减少了门数和深度。
📝 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 $Ψ$'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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Schrödinger bridges
generative model
quantum circuit optimization
denoiser architecture
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