Decomposition of Multi-Qubit Gates for Circuit Cutting

2026-03-27

AI Summary

This work addresses the challenge of executing large-scale quantum circuits on current noisy intermediate-scale quantum (NISQ) devices, which often requires circuit cutting due to hardware qubit limitations. Conventional decomposition methods for multi-controlled gates—such as MCX and CCCX—significantly increase sampling overhead during classical post-processing. To mitigate this, the authors propose a novel decomposition strategy tailored for circuit cutting that introduces a small number of ancillary qubits at cut locations to restructure gate implementations. This approach preserves functional equivalence while substantially reducing the number of samples required for accurate reconstruction. Experimental results demonstrate that the method effectively lowers sampling costs for MCX and CCCX gates, thereby enhancing the feasibility of running large quantum circuits on existing NISQ hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

A large-scale quantum circuit can be partitioned into multiple subcircuits through circuit cutting, where each subcircuit is executed multiple times and the expectation value of the original circuit is reconstructed by classical post-processing from their measurement (sampling) results. In this process, appropriate cut locations are identified after the user-designed quantum circuit, including multi-qubit gates that act on three or more qubits, has been decomposed into single-qubit gates and two-qubit gates such as the CNOT gate. Here, we present a method for reducing the sampling overhead, which refers to the increase in the number of samples required due to the cutting process, by modifying the decomposition strategy of multi-qubit gates. Using MCX and CCCX gates as representatives of multi-qubit gates, we demonstrate that the proposed decomposition method, which introduces a small number of ancilla qubits according to the identified cut locations, effectively decreases the sampling overhead.

Problem

• circuit cutting
• multi-qubit gates
• sampling overhead
• quantum circuit decomposition
• ancilla qubits

Innovation

• circuit cutting
• multi-qubit gate decomposition
• sampling overhead
• ancilla qubits
• quantum circuit partitioning