Titlebook: Machine Learning with Quantum Computers; Maria Schuld,Francesco Petruccione Book 2021Latest edition The Editor(s) (if applicable) and The

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妨礙

Maria Schuld,Francesco Petrucciones a probe of the electronic spectrum of the SL), may be a useful technique to elucidate some of the expected features which rely on the non-periodic modulation. The available optical data, mainly on Fibonacci SLs [5.15], are inconclusive, and most theoretical predictions [5.9–12, 24–26, 32–37, 39, 4

進(jìn)入

s a probe of the electronic spectrum of the SL), may be a useful technique to elucidate some of the expected features which rely on the non-periodic modulation. The available optical data, mainly on Fibonacci SLs [5.15], are inconclusive, and most theoretical predictions [5.9–12, 24–26, 32–37, 39, 4

向外

2364-9054 s and physicists at the graduate level onwards.?.The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years..978-3-030-83100-4978-3-030-83098-4Series ISSN 2364-9054 Series E-ISSN 2364-9062

蒼白

Introduction,higher level approaches have been adopted. We then give a first taste of what it means to learn from data with quantum computers by working through a toy example based on a very small interference circuit.

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獸皮

Representing Data on a Quantum Computer,etimes an entire dataset, can be encoded into quantum states. We present quantum routines for this task, discuss their runtimes and review their interpretation as feature maps known in classical machine learning.

Lyme-disease

Fault-Tolerant Quantum Machine Learning,ntum computers. We discuss quantum machine learning algorithms based on linear algebra subroutines such as matrix inversion, and those based on amplitude amplification or Grover search. We will then have a look at how classical probabilistic models like Bayesian nets and Boltzmann machines can be im

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