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Excitement about the promise of quantum computers is tempered by the realitythat the hardware remains exceptionally fragile and error-prone, forming abottleneck in the development of novel applications. In this manuscript, wedescribe and experimentally test a fully autonomous workflow designed todeterministically suppress errors in quantum algorithms from the gate levelthrough to circuit execution and measurement. We introduce the key elements ofthis workflow, delivered as a software package called Fire Opal, and survey theunderlying physical concepts: error-aware compilation, automated system-widegate optimization, automated dynamical decoupling embedding for circuit-levelerror cancellation, and calibration-efficient measurement-error mitigation. Wethen present a comprehensive suite of performance benchmarks executed on IBMhardware, demonstrating up to > 1000X improvement over the best alternativeexpert-configured techniques available in the open literature. Benchmarkingincludes experiments using up to 16 qubit systems executing: BernsteinVazirani, Quantum Fourier Transform, Grover's Search, QAOA, VQE, Syndromeextraction on a five-qubit Quantum Error Correction code, and Quantum Volume.Experiments reveal a strong contribution of Non-Markovian errors to baselinealgorithmic performance; in all cases the deterministic error-suppressionworkflow delivers the highest performance and approaches incoherent errorbounds without the need for any additional sampling or randomization overhead,while maintaining compatibility with all additional probabilistic errorsuppression techniques.

Experimental benchmarking of an automated deterministic error suppression workflow for quantum algorithms