Quantum Vulnerability Analysis to Accurate Estimate the Quantum Algorithm Success Rate
arxiv(2022)
Abstract
While quantum computers provide exciting opportunities for information
processing, they currently suffer from noise during computation that is not
fully understood. Incomplete noise models have led to discrepancies between
quantum program success rate (SR) estimates and actual machine outcomes. For
example, the estimated probability of success (ESP) is the state-of-the-art
metric used to gauge quantum program performance. The ESP suffers poor
prediction since it fails to account for the unique combination of circuit
structure, quantum state, and quantum computer properties specific to each
program execution. Thus, an urgent need exists for a systematic approach that
can elucidate various noise impacts and accurately and robustly predict quantum
computer success rates, emphasizing application and device scaling. In this
article, we propose quantum vulnerability analysis (QVA) to systematically
quantify the error impact on quantum applications and address the gap between
current success rate (SR) estimators and real quantum computer results. The QVA
determines the cumulative quantum vulnerability (CQV) of the target quantum
computation, which quantifies the quantum error impact based on the entire
algorithm applied to the target quantum machine. By evaluating the CQV with
well-known benchmarks on three 27-qubit quantum computers, the CQV success
estimation outperforms the estimated probability of success state-of-the-art
prediction technique by achieving on average six times less relative prediction
error, with best cases at 30 times, for benchmarks with a real SR rate above
0.1
a promising compiling strategy at compile time.
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