Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Temporal Quantum Tomography   Quantifying and verifying the control level in preparing a quantum state are
central challenges in building quantum devices. The quantum state is
characterized from experimental measurements, using a procedure known as
tomography, which requires a vast number of resources. Furthermore, the
tomography for a quantum device with temporal processing, which is
fundamentally different from the standard tomography, has not been formulated.
We develop a practical and approximate tomography method using a recurrent
machine learning framework for this intriguing situation. The method is based
on repeated quantum interactions between a system called quantum reservoir with
a stream of quantum states. Measurement data from the reservoir are connected
to a linear readout to train a recurrent relation between quantum channels
applied to the input stream. We demonstrate our algorithms for quantum learning
tasks followed by the proposal of a quantum short-term memory capacity to
evaluate the temporal processing ability of near-term quantum devices.

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The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
2
The categories of this paper are: math
27
The categories of this paper are: comp-gas