Improved quantum circuit cutting reduces overhead in distributed computing
Entanglement-Capability-Aware Finite-Bond Circuit Cutting for Distributed Quantum Computing
Distributed, Parallel, and Cluster Computing
Summary
Quantum computers sometimes need to split big tasks across several smaller devices, but breaking up the problem can be tricky and inefficient. The authors introduce a new way to decide where to cut quantum circuits by estimating how much the parts will get tangled together and how costly they are to run. Their method, called SACE, helps reduce the extra work needed when combining results from different pieces. This approach leads to more accurate and efficient computations, especially on complex quantum circuits like those used in factoring and optimization.
What this means in practice
- •For quantum hardware engineers: Optimize splitting large quantum algorithms across limited processors using entanglement-aware partitioning to reduce execution overhead and improve accuracy.
- •For quantum software developers: Implement circuit cutting strategies based on SACE to efficiently simulate and reconstruct large quantum circuits beyond single-device capacity.
Authors
Weidong Liu, Qingqing Jiang, Yufu Liu, Ruiqing He, Jiandong Shang, Hengliang Guo, Qiang Chen
Abstract
Distributed quantum computing distributes workloads that exceed a single processor's capacity across resource-constrained components. Circuit cutting enables this decomposition, but its effectiveness depends on partition choice. We present SACE, a Simulation-Aware Circuit Partitioning framework with Entanglement-Capability-Aware Screening. EP-Cost screens partitions using a parameter-dependent entanglement-capability proxy and native implementation cost. Under reconstruction constraints, finite-bond refinement selects the candidate that minimizes the worst-subcircuit discrepancy between normalized MPS simulations at bond dimensions $χ$ and $2χ$. Across 14 exhaustively enumerated small-scale candidate spaces, EP-Cost-guided genetic algorithm selections attain co-optimal native quasiprobability decomposition (QPD) overhead, with a median within-circuit Spearman correlation of 0.9965 between EP-Cost and QPD overhead. At matched cut counts, EP-Cost reduces QPD overhead by $25.20\times$ and $16.89\times$ for 8- and 9-qubit QFT circuits, respectively, and improves sampled reconstruction. On 12-qubit approximate QFT, deterministic reconstruction reduces Pauli-observable mean absolute error in all six low-bond conditions and total-variation distance in five. With five cuts and 7,776 QPD terms, SACE is best on all four reconstruction metrics in four of six conditions. At 24 qubits, SACE improves exact-local QFT accuracy in all 30 instance-bond-dimension conditions against each EP-Cost-only, Kernighan-Lin, and spectral baseline. The 50-qubit QFT comparisons remain favorable as reference convergence improves. Weighted QAOA circuits provide complementary, more bond-sensitive evidence. Refinement benefits persist across three search methods; larger candidate panels improve the optimized proxy without guaranteeing lower exact-local error.