  Quantum processing unit (QPU) has to satisfy highly demanding quantity and
quality requirements on its qubits to produce accurate results for problems at
useful scales. Furthermore, classical simulations of quantum circuits generally
do not scale. Instead, quantum circuit cutting techniques cut and distribute a
large quantum circuit into multiple smaller subcircuits feasible for less
powerful QPUs. However, the classical post-processing incurred from the cutting
introduces runtime and memory bottlenecks. Our tool, called ScaleQC, addresses
the bottlenecks by developing novel algorithmic techniques including (1) a
quantum states merging framework that quickly locates the solution states of
large quantum circuits; (2) an automatic solver that cuts complex quantum
circuits to fit on less powerful QPUs; and (3) a tensor network based
post-processing that minimizes the classical overhead. Our experiments
demonstrate both QPU requirement advantages over the purely quantum platforms,
and runtime advantages over the purely classical platforms for benchmarks up to
1000 qubits.
