Dynamic quantum circuit compilation
arXiv:2310.11021 · doi:10.1109/TC.2025.3643826
Abstract
Quantum computing has shown tremendous promise in addressing complex computational problems, yet its practical realization is hindered by the limited availability of qubits for computation. Recent advancements in quantum hardware have introduced mid-circuit measurements and resets, enabling the reuse of measured qubits and significantly reducing the qubit requirements for executing quantum algorithms. In this work, we present a systematic study of dynamic quantum circuit compilation, a process that transforms static quantum circuits into their dynamic equivalents with a reduced qubit count through qubit-reuse. We establish the first general framework for optimizing the dynamic circuit compilation via graph manipulation. In particular, we completely characterize the optimal quantum circuit compilation using binary integer programming, provide efficient algorithms for determining whether a given quantum circuit can be reduced to a smaller circuit and present heuristic algorithms for devising dynamic compilation schemes in general. Furthermore, we conduct a thorough analysis of quantum circuits with practical relevance, offering optimal compilations for well-known quantum algorithms in quantum computation, ansatz circuits utilized in quantum machine learning, and measurement-based quantum computation crucial for quantum networking. We also perform a comparative analysis against state-of-the-art approaches, demonstrating the superior performance of our methods in both structured and random quantum circuits. Our framework lays a rigorous foundation for comprehending dynamic quantum circuit compilation via qubit-reuse, bridging the gap between theoretical quantum algorithms and their physical implementation on quantum computers with limited resources.
arXiv comment51 pages, 32 figures; comments are welcome; v2 reorganize the writing and strengthen the results
References in corpus (22)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Surface codes: Towards practical large-scale quantum computation
- Strong quantum computational advantage using a superconducting quantum processor
- Suppressing quantum errors by scaling a surface code logical qubit
- Measurement-based quantum computation
- Multi-particle entanglement of hot trapped ions
- Towards Quantum Chemistry on a Quantum Computer
- High-fidelity quantum logic gates using trapped-ion hyperfine qubits
- High-fidelity two-qubit quantum logic gates using trapped calcium-43 ions
- Demonstration of the trapped-ion quantum-CCD computer architecture
- High-Fidelity Universal Gate Set for Be Ion Qubits
- Benchmarking an 11-qubit quantum computer
- Beating the break-even point with a discrete-variable-encoded logical qubit
- Instantaneous Quantum Computation
- Phase transition in Random Circuit Sampling
- Qubit Mapping Based on Subgraph Isomorphism and Filtered Depth-Limited Search
- Synthesizing Quantum-Circuit Optimizers
- A randomized benchmarking suite for mid-circuit measurements
- Wire Recycling for Quantum Circuit Optimization
- Pauli Noise Learning for Mid-Circuit Measurements
- Symmetry-Based Quantum Circuit Mapping