Problem
Research questions and friction points this paper is trying to address.
quantum circuit cutting
sampling overhead
gate cutting
wire cutting
cut placement
Scalable Circuit Cutting: A Framework for Combined Gate and Wire Cuts Using Gate Groups
🤖 AI Summary
Current noisy intermediate-scale quantum devices are constrained by limited qubit counts, and existing circuit cutting methods treat gate cutting and circuit partitioning separately, leading to high sampling overhead and poor scalability. This work proposes the first unified framework that integrates both strategies within a single partitioning scheme, introducing an innovative joint gate-group cutting mechanism. The selection of optimal cutting locations is formulated as a scalable graph partitioning problem. By leveraging graph-based optimization algorithms, gate-group identification, and a diagnostic feedback mechanism, the approach substantially reduces sampling overhead, enables efficient decomposition of large-scale quantum circuits, and provides a practical assessment of circuit cuttability.
📝 Abstract
Quantum circuit cutting enables the execution of large circuits on devices with a limited number of qubits by partitioning circuits into independent subcircuits. However, this introduces a sampling overhead, which grows exponentially with the number of cuts, rendering the choice of cut placements critical for practical circuit cutting. Determining optimal cut placements remains computationally challenging, particularly as circuits grow in size. Additionally, existing circuit cutting approaches typically treat gate and wire cuts independently. Those combining both cutting approaches, however, do not take advantage of joint cutting, i.e., identifying common gate groups and cutting them jointly for a reduced overhead. This work presents a unified framework that combines gate and wire cutting within a single partitioning strategy, enabling more efficient circuit decompositions. Moreover, our approach incorporates joint cutting via a novel gate grouping technique, further reducing sampling overhead. By formulating the cut placement problem as a scalable graph partitioning task, our method efficiently identifies near-optimal cut placements for large circuits, also providing diagnostic feedback on whether circuits are suitable for cutting.
Innovation
Methods, ideas, or system contributions that make the work stand out.
circuit cutting
gate grouping
joint cutting
graph partitioning
sampling overhead
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