Near-term quantum computers are expected to work in an environment where each operation is noisy, with no error correction. Therefore, quantum-circuit optimizers are applied to minimize the number of noisy operations. Today, physicists are constantly experimenting with novel devices and architectures. For every new physical substrate and for every modification of a quantum computer, we need to modify or rewrite major pieces of the optimizer to run successful experiments. Prior work uses manually derived or domain specific optimizations, which require experts to discover and verify. This work presents QUESO, an efficient approach for automatically synthesizing a quantum-circuit optimizer for a given quantum device. For instance, in 1.2 minutes, QUESO can synthesize an optimizer with high-probability correctness guarantees for IBM computers that significantly outperforms leading compilers, such as IBM's Qiskit and TKET, on the majority (85%) of the circuits in a diverse benchmark suite.
*This work is supported by NSF grants #1652140 and #2212232 and awards from Meta and Amazon. This research is also partially supported by the OVCRGE at the University of Wisconsin–Madison with funding from the Wisconsin Alumni Research Foundation. Lauren Pick is supported by NSF grant #2127309 to the Computing Research Association for the CIFellows Project.
