Low overhead circuit cutting with operator backpropagation

Current quantum computers suffer from noise due to lack of error correction. Several techniques to mitigate the effect of noise have been stud- ied, in particular to extract the expectation value of observables. One such technique, circuit cutting, partitions large circuits into smaller, less noisy sub- circuits, but the exponential increase in the number of circuit executions limits its scalability. Another method, operator backpropagation (OBP) reduces cir- cuit 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 paper introduces an optimized approach for minimizing noise in quantum circuits using operator backpropagation (OBP) combined with circuit cutting. We demonstrate that strategic use of OBP with cutting can mitigate the execution overhead. By employing simulated anneal- ing, our proposed method identifies the optimal backpropagation for specific circuits and observables, maximizing resource reduction in cutting. Results show a 3 × and 10 × decrease in resources for Variational Quantum Eigen- solver 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, provid- ing an efficient method to lower circuit cutting overhead without compromising performance.