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<p>Current quantum computers suffer from noise due to lack of error correction. Several techniques to mitigate the effect of noise have been studied, in particular to extract the expectation value of observables. One such technique, circuit cutting, partitions large circuits into smaller, less noisy subcircuits, but the exponential increase in the number of circuit executions limits its scalability. Another method, operator backpropagation (OBP), reduces circuit depth by classically simulating parts of it, yet often escalates the number of circuit executions by some factor due to additional non-commuting terms in the updated observable. This study introduces an optimized approach for lowering the effect of noise in quantum circuits using OBP combined with circuit cutting. We demonstrate that the strategic use of OBP with circuit cutting can mitigate execution overhead. By employing simulated annealing, our proposed method identifies the optimal backpropagation parameter for specific circuits and observables, maximizing the reduction in resources in cutting. Results show a <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(3\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>3</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(10\times \)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mn>10</mn> <mo>×</mo> </mrow> </math></EquationSource> </InlineEquation> decrease in resource requirements for Variational Quantum Eigensolver and Hamiltonian simulation circuits, respectively, while maintaining or even enhancing accuracy. This approach also yields similar savings for other circuits from the Benchpress database and various observable weights, providing an efficient method to lower circuit cutting overhead without compromising performance.</p>

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Low overhead circuit cutting with operator backpropagation

  • 期刊论文

  • 发表日期：

    2026年7月30日

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  • Debarthi Pal,
  • Ritajit Majumdar

摘要

Current quantum computers suffer from noise due to lack of error correction. Several techniques to mitigate the effect of noise have been studied, in particular to extract the expectation value of observables. One such technique, circuit cutting, partitions large circuits into smaller, less noisy subcircuits, but the exponential increase in the number of circuit executions limits its scalability. Another method, operator backpropagation (OBP), reduces circuit depth by classically simulating parts of it, yet often escalates the number of circuit executions by some factor due to additional non-commuting terms in the updated observable. This study introduces an optimized approach for lowering the effect of noise in quantum circuits using OBP combined with circuit cutting. We demonstrate that the strategic use of OBP with circuit cutting can mitigate execution overhead. By employing simulated annealing, our proposed method identifies the optimal backpropagation parameter for specific circuits and observables, maximizing the reduction in resources in cutting. Results show a \(3\times \) 3 × and \(10\times \) 10 × decrease in resource requirements for Variational Quantum Eigensolver and Hamiltonian simulation circuits, respectively, while maintaining or even enhancing accuracy. This approach also yields similar savings for other circuits from the Benchpress database and various observable weights, providing an efficient method to lower circuit cutting overhead without compromising performance.