35,145 research outputs found
Quantum reservoir processing
The concurrent rise of artificial intelligence and quantum information poses
opportunity for creating interdisciplinary technologies like quantum neural
networks. Quantum reservoir processing, introduced here, is a platform for
quantum information processing developed on the principle of reservoir
computing that is a form of artificial neural network. A quantum reservoir
processor can perform qualitative tasks like recognizing quantum states that
are entangled as well as quantitative tasks like estimating a non-linear
function of an input quantum state (e.g. entropy, purity or logarithmic
negativity). In this way experimental schemes that require measurements of
multiple observables can be simplified to measurement of one observable on a
trained quantum reservoir processor.Comment: 8 pages, 7 figure
Data reconstruction based on quantum neural networks
Reconstruction of large-sized data from small-sized ones is an important
problem in information science, and a typical example is the image
super-resolution reconstruction in computer vision. Combining machine learning
and quantum computing, quantum machine learning has shown the ability to
accelerate data processing and provides new methods for information processing.
In this paper, we propose two frameworks for data reconstruction based on
quantum neural networks (QNNs) and quantum autoencoder (QAE). The effects of
the two frameworks are evaluated by using the MNIST handwritten digits as
datasets. Simulation results show that QNNs and QAE can work well for data
reconstruction. We also compare our results with classical super-resolution
neural networks, and the results of one QNN are very close to classical ones
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