Machine Learning of Average Non-Markovianity from Randomized
Benchmarking
- URL: http://arxiv.org/abs/2207.01542v1
- Date: Mon, 4 Jul 2022 16:07:21 GMT
- Title: Machine Learning of Average Non-Markovianity from Randomized
Benchmarking
- Authors: Shih-Xian Yang, Pedro Figueroa-Romero and Min-Hsiu Hsieh
- Abstract summary: The presence of correlations in noisy quantum circuits will be an inevitable side effect as quantum devices continue to grow in size and depth.
RB is arguably the simplest method to initially assess the overall performance of a quantum device.
Here, we demonstrate a method exploiting the power of machine learning with matrix product operators to deduce the minimal average non-Markovianity displayed by the data of a RB experiment.
- Score: 12.547444644243544
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The presence of correlations in noisy quantum circuits will be an inevitable
side effect as quantum devices continue to grow in size and depth. Randomized
Benchmarking (RB) is arguably the simplest method to initially assess the
overall performance of a quantum device, as well as to pinpoint the presence of
temporal-correlations, so-called non-Markovianity; however, when such presence
is detected, it hitherto remains a challenge to operationally quantify its
features. Here, we demonstrate a method exploiting the power of machine
learning with matrix product operators to deduce the minimal average
non-Markovianity displayed by the data of a RB experiment, arguing that this
can be achieved for any suitable gate set, as well as tailored for most
specific-purpose RB techniques.
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