Compile-Time Simplification of Classically Controlled Operations in Dynamic Circuits

• URL: http://arxiv.org/abs/2605.28439v1
• Date: Wed, 27 May 2026 13:05:41 GMT
• Title: Compile-Time Simplification of Classically Controlled Operations in Dynamic Circuits
• Abstract summary: We propose a compile-time optimization framework that reduces the use of classical controls in dynamic circuits.<n>At its core, the framework uses a static analysis that symbolically executes the circuit by propagating classical information alongside the quantum state.<n>Compared to existing compile-time optimizations that target only mid-circuit measurements, our method applies to a broader class of dynamic circuits expressible in modern quantum programming languages.
• Score: 0.24689994775563415
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dynamic circuits use real-time outcomes of mid-circuit measurements, processed by a classical controller, to adapt subsequent operations during circuit execution. This additional flexibility over static circuits comes at a price. Mid-circuit measurements are typically slower and noisier than unitary gates. Furthermore, classical feedforward requires exchanging information between the quantum processor (QPU) and the classical controller, introducing latency that erodes the practical performance of dynamic circuits. We propose a compile-time optimization framework that reduces the use of classical controls in dynamic circuits while preserving their semantics. At its core, the framework uses a static analysis that symbolically executes the circuit by propagating classical information alongside the quantum state. By combining this classical-quantum information with the Probabilistic Circuit Model extended with probabilistic controls that emulate classical feedforward, we obtain an intermediate probabilistic representation of the dynamic circuit. In this representation, mid-circuit measurements and classically controlled operations can be removed or rewritten as purely unitary operations and probabilistic components. Compared to existing compile-time optimizations that target only mid-circuit measurements, our method applies to a broader class of dynamic circuits expressible in modern quantum programming languages. We evaluated our framework on randomly generated dynamic circuits, achieving about 50% classical feedforward reduction and even higher reductions in favorable settings.

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