Abstract
Circuit knitting is a technique that allows circuits that are too large to run on a given quantum processing unit (QPU) to be decomposed into an equivalent series of smaller subcircuits that can run on the QPU, where the resulting measurement outcomes of the subcircuits are then classically combined via post-processing. Circuits are decomposed by cutting gates and/or wires. To obtain results to the same accuracy as those of the original problem, each cut incurs additional sampling overhead that grows exponentially with the number of cuts. It is therefore desirable to find optimal sets of cuts that minimize the sampling overhead. To this end, we present the ''cut-finder''module of the Circuit Knitting Toolbox (CKT), which performs an automated search for LO gate and wire cuts that minimize the sampling overhead. Circuit knitting can also be used to engineer gates between distant qubits which would otherwise require a large swap overhead to implement. We show how this capability can be used to calculate certain expectation values on a graph state with a cylindrical topology to a better degree of accuracy than if the experiment were to be performed without circuit knitting.