Experimental benchmarking of an automated deterministic error
  suppression workflow for quantum algorithms
Excitement about the promise of quantum computers is tempered by the reality
that the hardware remains exceptionally fragile and error-prone, forming a
bottleneck in the development of novel applications. In this manuscript, we
describe and experimentally test a fully autonomous workflow designed to
deterministically suppress errors in quantum algorithms from the gate level
through to circuit execution and measurement. We introduce the key elements of
this workflow, delivered as a software package called Fire Opal, and survey the
underlying physical concepts: error-aware compilation, automated system-wide
gate optimization, automated dynamical decoupling embedding for circuit-level
error cancellation, and calibration-efficient measurement-error mitigation. We
then present a comprehensive suite of performance benchmarks executed on IBM
hardware, demonstrating up to > 1000X improvement over the best alternative
expert-configured techniques available in the open literature. Benchmarking
includes experiments using up to 16 qubit systems executing: Bernstein
Vazirani, Quantum Fourier Transform, Grover's Search, QAOA, VQE, Syndrome
extraction on a five-qubit Quantum Error Correction code, and Quantum Volume.
Experiments reveal a strong contribution of Non-Markovian errors to baseline
algorithmic performance; in all cases the deterministic error-suppression
workflow delivers the highest performance and approaches incoherent error
bounds without the need for any additional sampling or randomization overhead,
while maintaining compatibility with all additional probabilistic error
suppression techniques.
Pranav S. Mundada Aaron Barbosa Smarak Maity Yulun Wang T. M. Stace Thomas Merkh Felicity Nielson Andre R. R. Carvalho Michael Hush Michael J. Biercuk Yuval Baum