A Case for Quantum Circuit Cutting for NISQ Applications: Impact of topology, determinism, and sparsity

TL;DR

This paper demonstrates that quantum circuit cutting, combined with ansatz Clifford structure and parameter pruning, can significantly reduce costs and enable scalable NISQ applications on over 200 qubits.

Contribution

It introduces a method leveraging circuit topology, determinism, and sparsity to improve quantum circuit cutting efficiency for variational algorithms.

Findings

Reduced experiment count by up to 16x

Achieved scalability to over 200 qubits

Matched or surpassed classical shadows error mitigation

Abstract

We make the case that variational algorithm ansatzes for near-term quantum computing are well-suited for the quantum circuit cutting strategy. Previous demonstrations of circuit cutting focused on the exponential execution and postprocessing costs due to the cuts needed to partition a circuit topology, leading to overly pessimistic evaluations of the approach. This work observes that the ansatz Clifford structure and variational parameter pruning significantly reduce these costs. By keeping track of the limited set of correct subcircuit initializations and measurements, we reduce the number of experiments needed by up to 16x, matching and beating the error mitigation offered by classical shadows tomography. By performing reconstruction as a sparse tensor contraction, we scale the feasible ansatzes to over 200 qubits with six ansatz layers, beyond the capability of prior work.