Quantum Circuit Mapping Using Binary Integer Nonlinear Programming

As Quantum Computers continue to increase in size, throughput has not increased proportionally [1]. Errors in qubit measurement and gate operations continue to increase with circuit width and depth. In order to maximize machine throughput, researchers have begun exploring ways of parallelizing circuit execution [2]–[9]. Due to the noisyness of quantum computers, this requires new algorithms for efficient resource allocation, qubit mapping, and scheduling. We improve on existing greedy algorithms by formulating the mapping search as a Binary Integer Non-Linear Programming (BINLP) problem. We model practical constraints and propose new heuristics for determining the goodness of a mapping. We evaluate our mappings on several sets of benchmark circuits and investigate the usefulness of parallel execution for circuit cutting, QAOAs, and deep circuits. We observe similar fidelity compared to Qiskit's transpiler for circuit cutting and throughput benchmarks. We observe greater fidelity over Qiskit's transpiler for dense QAOA ansatzes. Finally, we find that parallel circuit cutting provides greater fidelity than full-circuit execution.