6/12/2023 0 Comments Big quantum error after all![]() An important family of errors are readout errors. Mitigating errors is hard in general because quantum bits (“qubits”) cannot be copied 2, 3, 4. One significant limitation is the rate of errors and decoherence in noisy intermediate-scale quantum (NISQ) computers 1. While quantum algorithms are promising techniques for a variety of scientific and industrial applications, current challenges limit their immediate applicability. This method is shown to avoid pathologies from commonly used matrix inversion and least squares methods. We study one such method, known as iterative Bayesian unfolding, as a potential tool for correcting readout errors from universal gate-based quantum computers. This challenge is familiar to HEP, where prior-independent regularized matrix inversion techniques (“unfolding”) have been developed for years to correct for acceptance and detector effects, when performing differential cross section measurements. ![]() One challenge with inverting matrices with large off-diagonal components is that the results are sensitive to statistical fluctuations. The most basic method to correct readout errors is matrix inversion, using a response matrix built from simple operations to probe the rate of transitions from known initial quantum states to readout outcomes. An important class of qubit errors are readout errors. ![]() ![]() This is a significant challenge for interpreting results from quantum computer simulations for quantum chemistry, nuclear physics, high energy physics (HEP), and other emerging scientific applications. In the current era of noisy intermediate-scale quantum computers, noisy qubits can result in biased results for early quantum algorithm applications. ![]()
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