Patterns for Quantum Circuit Cutting

Betis Baheri, Qiang Guan, Shuai Xu +1 more

In this paper, we are investigating the idea of cutting the original quantum circuits into multiple sub-circuits and evaluating the fidelity (correctness) amount of the sub-circuits results. We introduce an analysis method, Distributed Quantum Circuits (DQC), to encounter all aspects of Quantum properties from qubit's error data to the topology of Quantum processors on cutting the quantum circuits. Finally, we share our observations and experiments to introduce a new design pattern for managing distributed Quantum circuits on smaller Quantum computers.

Margareta Vamos

Michel Meulen, Niels M. P. Neumann, Jasper Verbree

Current quantum computers suffer from a limited number of qubits and high error rates, limiting practical applicability. Different techniques exist to mitigate these effects and run larger algorithms. In this work, we analyze one of these techniques called quantum circuit cutting. With circuit cutting, a quantum circuit is decomposed into smaller sub-circuits, each of which can be run on smaller quantum hardware. We compare the performance of quantum circuit cutting with different cutting strategies, and then apply circuit cutting to a QAOA algorithm. Using simulations, we first show that Randomized Clifford measurements outperform both Pauli and random unitary measurements. Second, we show that circuit cutting has trouble providing correct answers in noisy settings, especially as the number of circuits increases.

BAO JINGE

Xinglei Dou, Lei Liu, Zhuohao Wang +1 more

Distributed quantum computing (DQC) is a promising way to achieve large-scale quantum computing. However, mapping large-sized quantum circuits in DQC is a challenging job; for example, it is difficult to find an ideal cutting and mapping solution when many qubits, complicated qubit operations, and diverse QPUs are involved. In this study, we propose LarQucut, a new quantum circuit cutting and mapping approach for large-sized circuits in DQC. LarQucut has several new designs. (1) LarQucut can have cutting solutions that use fewer cuts, and it does not cut a circuit into independent sub-circuits, therefore reducing the overall cutting and computing overheads. (2) LarQucut finds isomorphic sub-circuits and reuses their execution results. So, LarQucut can reduce the number of sub-circuits that need to be executed to reconstruct the large circuit's output, reducing the time spent on sampling the sub-circuits. (3) We design an adaptive quantum circuit mapping approach, which identifies qubit interaction patterns and accordingly enables the best-fit mapping policy in DQC. The experimental results show that, for large circuits with hundreds to thousands of qubits in DQC, LarQucut can provide a better cutting and mapping solution with lower overall overheads and achieves results closer to the ground truth.

Mohammad Pirhooshyaran, Tamás Terlaky B