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Enhanced Quantum Circuit Cutting Framework for Sampling Overhead Reduction

Po-Hung Chen, Dah-Wei Chiou, Bo-Hung Chen, Jie-Hong Roland Jiang

TL;DR

This paper tackles the sampling overhead bottleneck in quantum circuit cutting by introducing ShotQC, an approach that jointly optimizes shot distribution and cut parameterization to reduce variance in reconstructed circuit outputs. Grounded in tensor-network circuit representations, ShotQC expands the postprocessing design space and employs adaptive shot allocation to minimize the total sampling required under a fixed resource budget. Through theoretical analysis and GPU-accelerated evaluations on standard benchmarks, ShotQC achieves up to 19x variance reduction (and thus sampling overhead) in various circuits, while offering practical settings that balance runtime and overhead. The work highlights a scalable path to make circuit cutting more feasible on NISQ devices and opens avenues for further improvements in initial-state design and optimization strategies.

Abstract

The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In this work, we propose an enhanced circuit cutting framework, ShotQC, which effectively reduces the sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo strategy to dynamically allocate more quantum resources to subcircuit configurations that contribute more to the variance in the final outcome. The latter exploits additional degrees of freedom in postprocessing to further suppress variance. Integrating these optimizations, ShotQC significantly reduces the sampling overhead without increasing classical postprocessing complexity, as demonstrated across a range of benchmark circuits.

Enhanced Quantum Circuit Cutting Framework for Sampling Overhead Reduction

TL;DR

This paper tackles the sampling overhead bottleneck in quantum circuit cutting by introducing ShotQC, an approach that jointly optimizes shot distribution and cut parameterization to reduce variance in reconstructed circuit outputs. Grounded in tensor-network circuit representations, ShotQC expands the postprocessing design space and employs adaptive shot allocation to minimize the total sampling required under a fixed resource budget. Through theoretical analysis and GPU-accelerated evaluations on standard benchmarks, ShotQC achieves up to 19x variance reduction (and thus sampling overhead) in various circuits, while offering practical settings that balance runtime and overhead. The work highlights a scalable path to make circuit cutting more feasible on NISQ devices and opens avenues for further improvements in initial-state design and optimization strategies.

Abstract

The recently developed quantum circuit cutting technique greatly extends the capabilities of current noisy intermediate-scale quantum (NISQ) hardware. However, it introduces substantial overhead in both classical postprocessing and quantum resources, as the postprocessing complexity and sampling cost scale exponentially with the number of circuit cuts. In this work, we propose an enhanced circuit cutting framework, ShotQC, which effectively reduces the sampling overhead through two key optimizations: shot distribution and cut parameterization. The former employs an adaptive Monte Carlo strategy to dynamically allocate more quantum resources to subcircuit configurations that contribute more to the variance in the final outcome. The latter exploits additional degrees of freedom in postprocessing to further suppress variance. Integrating these optimizations, ShotQC significantly reduces the sampling overhead without increasing classical postprocessing complexity, as demonstrated across a range of benchmark circuits.

Paper Structure

This paper contains 29 sections, 3 theorems, 50 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Quantum circuits as tensor networks
  3. Circuit Cutting
  4. Theory
  5. Workflow
  6. Example
  7. Sampling Overhead
  8. Problem Formulation
  9. ShotQC: Framework
  10. ShotQC: Overview
  11. ShotQC: Cut Parameterization Optimization
  12. Problem Reduction
  13. Degrees of Freedom
  14. Design Trade-offs for Overhead Reduction
  15. ShotQC: Shot Distribution Optimization
  16. ...and 14 more sections

Key Result

Lemma 3.1

Let $(G(E, V), \mathcal{A})$ be a tensor network of a quantum circuit, and let $(u\rightarrow v)\in E$ be an edge in $G$ (with $u,v\in V$). Replacing the edge $(u\rightarrow v)$ with the summation of measure-and-prepare channels as \begin{tikzpicture} \begin{scope}[shift={(0,0)}] \draw [thick, ->]

Figures (6)

  • Figure 1: A 3-qubit circuit is cut into two 2-qubit subcircuits.
  • Figure 2: The workflow of ShotQC. The cut subcircuits is obtained by applying cut-finding methods on the original circuit.
  • Figure 3: Posterior shot segmentation.
  • Figure 4: Simulation results: variance is normalized by the baseline. Hollow bars indicate timeout beyond 10 hours.
  • Figure 5: The effect of different parameters on variance.
  • ...and 1 more figures

Theorems & Definitions (6)

  • Lemma 3.1
  • proof
  • Lemma 5.1
  • proof
  • Lemma 5.2
  • proof