Richard Küng

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Extracting important information from a quantum system as efficiently and
tractably as possible is an important subroutine in most quantum
technologies. We present an efficient method for constructing an
approximate classical description of a quantum state using very few
measurements of the state. This description, called a classical shadow, can
be used to predict many different properties. The required number of
measurements is independent of the system size and saturates
information-theoretic lower bounds [arXiv:2002.08953].

These quantum-to-classical converters pave the way for new synergies
between (near-term) quantum computing and classical machine learning
[arXiv:2106.12627] in the context of quantum many-body physics. Conversely,
instances where classical shadows fail constitute promising candidates for
new types of quantum advantage (quantum-enhanced learning)
[arXiv:2112.00778].

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