26,545 research outputs found
Continuous-variable quantum neural networks
We introduce a general method for building neural networks on quantum
computers. The quantum neural network is a variational quantum circuit built in
the continuous-variable (CV) architecture, which encodes quantum information in
continuous degrees of freedom such as the amplitudes of the electromagnetic
field. This circuit contains a layered structure of continuously parameterized
gates which is universal for CV quantum computation. Affine transformations and
nonlinear activation functions, two key elements in neural networks, are
enacted in the quantum network using Gaussian and non-Gaussian gates,
respectively. The non-Gaussian gates provide both the nonlinearity and the
universality of the model. Due to the structure of the CV model, the CV quantum
neural network can encode highly nonlinear transformations while remaining
completely unitary. We show how a classical network can be embedded into the
quantum formalism and propose quantum versions of various specialized model
such as convolutional, recurrent, and residual networks. Finally, we present
numerous modeling experiments built with the Strawberry Fields software
library. These experiments, including a classifier for fraud detection, a
network which generates Tetris images, and a hybrid classical-quantum
autoencoder, demonstrate the capability and adaptability of CV quantum neural
networks
Quantum classical hybrid neural networks for continuous variable prediction
Within this decade, quantum computers are predicted to outperform
conventional computers in terms of processing power and have a disruptive
effect on a variety of business sectors. It is predicted that the financial
sector would be one of the first to benefit from quantum computing both in the
short and long terms. In this research work we use Hybrid Quantum Neural
networks to present a quantum machine learning approach for Continuous variable
prediction
Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks
Entanglement in continuous-variable non-Gaussian states provides
irreplaceable advantages in many quantum information tasks. However, the sheer
amount of information in such states grows exponentially and makes a full
characterization impossible. Here, we develop a neural network that allows us
to use correlation patterns to effectively detect continuous-variable
entanglement through homodyne detection. Using a recently defined stellar
hierarchy to rank the states used for training, our algorithm works not only on
any kind of Gaussian state but also on a whole class of experimentally
achievable non-Gaussian states, including photon-subtracted states. With the
same limited amount of data, our method provides higher accuracy than usual
methods to detect entanglement based on maximum-likelihood tomography.
Moreover, in order to visualize the effect of the neural network, we employ a
dimension reduction algorithm on the patterns. This shows that a clear boundary
appears between the entangled states and others after the neural network
processing. In addition, these techniques allow us to compare different
entanglement witnesses and understand their working. Our findings provide a new
approach for experimental detection of continuous-variable quantum correlations
without resorting to a full tomography of the state and confirm the exciting
potential of neural networks in quantum information processing.Comment: 9 pages (incl. appendix), 6 figures, comments welcome
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