QuBound uses historical performance traces decomposed into trend and residual to train an LSTM that predicts tight, fast performance bounds for quantum circuits under time-varying noise.
Impact of unreliable devices on stability of quantum computations
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abstract
Noisy intermediate-scale quantum (NISQ) devices are valuable platforms for testing the tenets of quantum computing, but these devices are susceptible to errors arising from de-coherence, leakage, cross-talk and other sources of noise. This raises concerns regarding the stability of results when using NISQ devices since strategies for mitigating errors generally require well-characterized and stationary error models. Here, we quantify the reliability of NISQ devices by assessing the necessary conditions for generating stable results within a given tolerance. We use similarity metrics derived from device characterization data to derive and validate bounds on the stability of a 5-qubit implementation of the Bernstein-Vazirani algorithm. Simulation experiments conducted with noise data from IBM Washington, spanning January 2022 to April 2023, revealed that the reliability metric fluctuated between 41% and 92%. This variation significantly surpasses the maximum allowable threshold of 2.2% needed for stable outcomes. Consequently, the device proved unreliable for consistently reproducing the statistical mean in the context of the Bernstein-Vazirani circuit.
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Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise
QuBound uses historical performance traces decomposed into trend and residual to train an LSTM that predicts tight, fast performance bounds for quantum circuits under time-varying noise.