28,822 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
Blackbox identity testing for bounded top fanin depth-3 circuits: the field doesn't matter
Let C be a depth-3 circuit with n variables, degree d and top fanin k (called
sps(k,d,n) circuits) over base field F. It is a major open problem to design a
deterministic polynomial time blackbox algorithm that tests if C is identically
zero. Klivans & Spielman (STOC 2001) observed that the problem is open even
when k is a constant. This case has been subjected to a serious study over the
past few years, starting from the work of Dvir & Shpilka (STOC 2005).
We give the first polynomial time blackbox algorithm for this problem. Our
algorithm runs in time poly(nd^k), regardless of the base field. The only field
for which polynomial time algorithms were previously known is F=Q (Kayal &
Saraf, FOCS 2009, and Saxena & Seshadhri, FOCS 2010). This is the first
blackbox algorithm for depth-3 circuits that does not use the rank based
approaches of Karnin & Shpilka (CCC 2008).
We prove an important tool for the study of depth-3 identities. We design a
blackbox polynomial time transformation that reduces the number of variables in
a sps(k,d,n) circuit to k variables, but preserves the identity structure.Comment: 14 pages, 1 figure, preliminary versio
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