Bayesian learning is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Use this tag for questions regarding bayesian learning using quantum computers and/or quantum algorithms.
Questions tagged [bayesian-learning]
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Is it true to say that one qubit in an entangled state can instantaneously affect all others?
When a qubit is measured, there is a ‘collapse of the wave-function’ as a result is randomly chosen.
If the qubit is entangled with others, this collapse will also effect them. And the way it affects them depends on the way we chose to measure our…
James Wootton
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Can quantum computing speed up Bayesian learning?
One of the biggest drawbacks of Bayesian learning against deep learning is runtime: applying Bayes' theorem requires knowledge on how the data is distributed, and this usually requires either expensive integrals or some sampling mechanism (with the…
fr_andres
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How can a D-Wave style Annealing QPU help sampling?
This question is a follow-up on this one, with the hope of getting more specific clues, and was motivated by this answer by user Rob.
Also please note this posts that handle the topic of QA in much more…
fr_andres
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Quantum speedup in Bayesian machine learning on NISQ computers
It is well known that in Bayesian learning, applying Bayes' theorem requires knowledge on how the data is distributed, and this usually requires either expensive integrals or some sampling mechanism, and, using Bayesian machine learning, (instead of…
Carlos Alfredo Vergara Rojas
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In what limit does the estimator sample variance converge to the Cramer-Rao bound?
In the context of a single phase estimation problem of a quantum photonics experiment (related post). For example consider a 3-photon quantum circuit (such as the Mach-Zehnder which depends on some phase shift operator which encodes a parameter…
John Doe
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