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.
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.
Compile-time optimization is important for improving the efficiency and reliability of quantum circuits on current noisy hardware. While many existing methods simplify circuits using structural patterns or quantum-state information, most of them target only unitary circuits and do not support dynamic circuits with mid-circuit measurements and classical feedforward. In this work, we present Branch-Aware Quantum Constant Propagation (BQCP), a compile-time analysis for dynamic circuits. BQCP extends Quantum Constant Propagation (QCP) by tracking the classical information produced by mid-circuit measurements together with the corresponding post-measurement quantum states across different execution branches. This enables path-sensitive reasoning inside conditional blocks and more precise information propagation than QCP. To keep the analysis scalable, we bound both the size of the quantum-state representation and the number of tracked branches. Using the information inferred by the analysis, we apply semantics-preserving simplifications to circuit operations. We prove the soundness of both the analysis and the simplifications. Experimental results on both application-driven and synthetic benchmarks show that, on dynamic circuits, our method consistently achieves larger reductions than other existing passes including QCP.
Quantum compilers typically follow the circuit model, representing programs as fixed sequences of gates. This static view breaks down in hybrid quantum-classical applications, where gate choices depend on runtime data or measurement results. We introduce a new Intermediate Representation (IR) that elevates gates to first-class values, enabling their dynamic creation, composition, and control. This unified representation allows classical computation to steer quantum behaviour, capturing phenomena including stochastic gate selection, adaptive error correction, and measurement-driven computation within a single framework. Case studies in noise modelling, randomised compilation, error correction, and measurement-based quantum computing show that our IR expresses these programs compactly and supports optimisations that were not possible in the circuit model. Evaluation on a benchmark suite of hybrid quantum-classical programs indicates that our IR represents programs compactly and facilitates compiler analysis and transformation.
Dynamic quantum circuits incorporate mid-circuit measurements (MCMs) and feed-forward operations are crucial for manipulating quantum information. They have been broadly used in quantum error correction and quantum teleportation. Recently, they are utilized to prepare certain states and long-range entangling gates as well as reduce resource overhead in quantum algorithms. In this paper, we present AC/DC, a novel A utomated C ompilation framework for generating D ynamic quantum C ircuits that prepare any unitary operators or states, leveraging numerical optimization-based circuit synthesis methods. The first contribution is introducing optimization objective functions incorporating MCMs and feed-forward operations. The second contribution is embedding these into a popular open-source quantum circuit synthesis framework. We demonstrate generating dynamic circuits for long-range entangling gates, circuit optimization, lattice simulations, and state preparation, with validation through simulation and quantum hardware. Furthermore, we perform a noise analysis to assess the impact of MCM and gate errors, identifying scenarios where dynamic circuits provide significant benefits. The dynamic circuits generated by our framework show substantial improvements in reducing circuit depth and, in some cases, the number of gates. To our knowledge, this is the first practical procedure to generate dynamic quantum circuits, paving the way for enhanced circuit generation and optimization methods for near-term quantum computers.
Mid-circuit measurements and measurement-controlled gates are supported by an increasing number of quantum hardware platforms and will become more relevant as an essential building block for quantum error correction. However, mid-circuit measurements impose significant demands on the quantum hardware due to the required signal analysis and classical feedback loop. This work presents a static circuit optimization algorithm that can substitute some of these measurements with an equivalent circuit with stochastic applications of gates. Our method uses ideas from constant propagation to classically precompute measurement outcome probabilities. Our proposed optimization is efficient, as its runtime scales polynomially on the number of qubits and gates of the circuit.
In this poster, we present our recent exploration of the novel probabilistic circuit model (PCM). Compared to dynamic circuits (i.e., circuits with mid-circuit measurements), probabilistic circuits (i.e., circuits containing stochastic applications of gates) suggest a way to model non-deterministic circuit behavior without the need for mid-circuit measurements. This poster first demonstrates how the PCM can reduce midcircuit measurements of dynamic circuits. By static analysis of quantum constant propagation (QCP), we replace mid-circuit measurements with probabilistic circuit snippets. This replacement can significantly reduce runtime overhead for dynamic circuits because mid-circuit measurements heavily burden quantum hardware due to the need for signal analysis and the integration of a classical feedback loop. Then, this poster establishes the equivalence between the dynamic circuits and the PCM regarding their expressive power. This finding implies the potential use of probabilistic gates in designing quantum algorithms. Finally, this poster takes errors of a depolarizing channel as an example to showcase the ability of probabilistic circuits to model nondeterministic behaviors in circuits. With PCM, the behavior of stochastic errors in circuits can be concisely visualized and modeled by probabilistic gates.
Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Variational Quantum Eigensolvers (VQE), yet transpiling these programs across frameworks remains challenging due to inconsistent support for control flow and measurement semantics. We present a transpilation pipeline that converts OpenQASM 3.0 programs with classical control structures (conditionals and bounded loops) into optimized CUDA-Q C++ kernels, leveraging CUDA-Q's native mid-circuit measurement and host-language control flow to translate dynamic patterns without static circuit expansion. Our open-source framework is validated on comprehensive test suites derived from IBM Quantum's classical feedforward guide, including conditional reset, if-else branching, multi-bit predicates, and sequential feedforward, and on VQE-style parameterized circuits with runtime parameter optimization. Experiments show that the resulting CUDA-Q kernels reduce circuit depth by avoiding branch duplication, improve execution efficiency via low-latency classical feedback, and enhance code readability by directly mapping OpenQASM 3.0 control structures to C++ control flow, thereby bridging OpenQASM 3.0's portable circuit specification with CUDA-Q's performance-oriented execution model for NISQ-era applications requiring dynamic circuit capabilities.