Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere

• URL: http://arxiv.org/abs/2407.03970v1
• Date: Thu, 4 Jul 2024 14:44:53 GMT
• Title: Overdispersion in gate tomography: Experiments and continuous, two-scale random walk model on the Bloch sphere
• Authors: Wolfgang Nowak, Tim Brünnette, Merel Schalkers, Matthias Möller,
• Abstract summary: We show that existing noise models fail to properly capture the aggregation of noise effects over an algorithm's runtime. We develop noise model for the readout probabilities as a function of the number of gate operations. We superimpose a second random walk at the scale of multiple readouts to account for overdispersion.
• Score: 0.6249768559720122
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Noisy intermediate-scale quantum computers (NISQ) are in their childhood, but showing high promise. One main concern for NISQ machines is their inherent noisiness, as the qubit states are subject to disturbances with each algorithmic operation applied. In this study, we conduct experiments on quantum noise. Based on our data, we show that existing noise models fail to properly capture the aggregation of noise effects over an algorithm's runtime. They are underdispersed, meaning that observable frequencies scatter much more between repeated experiments than what the standard assumptions of the binomial distribution allow for. We develop noise model for the readout probabilities as a function of the number of gate operations. The model is based on a continuous random walk on the (Bloch) sphere, where the angular diffusion coefficient characterizes the noisiness of gate operations. We superimpose a second random walk at the scale of multiple readouts to account for overdispersion. The interaction of these two random walks predicts theoretical, runtime-dependent bounds for probabilities. Overall, it is a three-parameter distributional model that fits the data better than the corresponding one-scale model (without overdispersion). We demonstrate the fit and the plausibility of the predicted bounds via Bayesian data-model analysis.

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