Pith. sign in

REVIEW 3 cited by

Circuit knitting with classical communication

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2205.00016 v3 pith:S4Y2AA5M submitted 2022-04-29 quant-ph

classification quant-ph
keywords gatescircuitclassicalknittingquantumsimulationcircuitscommunication
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

The scarcity of qubits is a major obstacle to the practical usage of quantum computers in the near future. To circumvent this problem, various circuit knitting techniques have been developed to partition large quantum circuits into subcircuits that fit on smaller devices, at the cost of a simulation overhead. In this work, we study a particular method of circuit knitting based on quasiprobability simulation of nonlocal gates with operations that act locally on the subcircuits. We investigate whether classical communication between these local quantum computers can help. We provide a positive answer by showing that for circuits containing $n$ nonlocal CNOT gates connecting two circuit parts, the simulation overhead can be reduced from $O(9^n)$ to $O(4^n)$ if one allows for classical information exchange. Similar improvements can be obtained for general Clifford gates and, at least in a restricted form, for other gates such as controlled rotation gates.

Discussion (0). Sign in to comment.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting

    cs.DC 2026-02 conditional novelty 5.0 of 10

    DistributedEstimator demonstrates that circuit cutting preserves test accuracy and robustness in QNN training on Iris and MNIST while revealing that classical reconstruction dominates runtime and exponential subcircui...

  2. CutReg: A loss regularizer for enhancing the scalability of QML via adaptive circuit cutting

    quant-ph 2025-06 conditional novelty 5.0 of 10

    Adding a log-overhead regularizer with trainable cutting angles reduces circuit-cutting sampling cost in QML regression, at similar reported accuracy, but without an unregularized baseline.

  3. Perspectives on Utilization of Measurements in Quantum Algorithms

    quant-ph 2025-07 conditional novelty 3.0 of 10

    A survey that categorizes quantum measurement uses into static circuits, dynamic circuits, and challenge-solving techniques, and argues measurements deserve more attention in algorithm design.

Pith tools