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.
Benchmarking of different optimizers in the variational quantum algorithms for applications in quantum chemistry,
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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.