32 research outputs found
A Simple Quantum Neural Net with a Periodic Activation Function
In this paper, we propose a simple neural net that requires only
number of qubits and quantum gates: Here, is the number of input
parameters, and is the number of weights applied to these parameters in the
proposed neural net. We describe the network in terms of a quantum circuit, and
then draw its equivalent classical neural net which involves nodes in
the hidden layer. Then, we show that the network uses a periodic activation
function of cosine values of the linear combinations of the inputs and weights.
The backpropagation is described through the gradient descent, and then iris
and breast cancer datasets are used for the simulations. The numerical results
indicate the network can be used in machine learning problems and it may
provide exponential speedup over the same structured classical neural net.Comment: a discussion session is added. 5 pages, conference paper. To appear
in The 2018 IEEE International Conference on Systems, Man, and Cybernetics
(SMC2018
Building quantum neural networks based on swap test
Artificial neural network, consisting of many neurons in different layers, is
an important method to simulate humain brain. Usually, one neuron has two
operations: one is linear, the other is nonlinear. The linear operation is
inner product and the nonlinear operation is represented by an activation
function. In this work, we introduce a kind of quantum neuron whose inputs and
outputs are quantum states. The inner product and activation operator of the
quantum neurons can be realized by quantum circuits. Based on the quantum
neuron, we propose a model of quantum neural network in which the weights
between neurons are all quantum states. We also construct a quantum circuit to
realize this quantum neural network model. A learning algorithm is proposed
meanwhile. We show the validity of learning algorithm theoretically and
demonstrate the potential of the quantum neural network numerically.Comment: 10 pages, 13 figure
Smart Nanostructures and Synthetic Quantum Systems
So far proposed quantum computers use fragile and environmentally sensitive
natural quantum systems. Here we explore the notion that synthetic quantum
systems suitable for quantum computation may be fabricated from smart
nanostructures using topological excitations of a neural-type network that can
mimic natural quantum systems. These developments are a technological
application of process physics which is a semantic information theory of
reality in which space and quantum phenomena are emergent.Comment: LaTex,14 pages 1 eps file. To be published in BioMEMS and Smart
Nanostructures, Proceedings of SPIE Conference #4590, ed. L. B. Kis
Synthetic Quantum Systems
So far proposed quantum computers use fragile and environmentally sensitive
natural quantum systems. Here we explore the new notion that synthetic quantum
systems suitable for quantum computation may be fabricated from smart
nanostructures using topological excitations of a stochastic neural-type
network that can mimic natural quantum systems. These developments are a
technological application of process physics which is an information theory of
reality in which space and quantum phenomena are emergent, and so indicates the
deep origins of quantum phenomena. Analogous complex stochastic dynamical
systems have recently been proposed within neurobiology to deal with the
emergent complexity of biosystems, particularly the biodynamics of higher brain
function. The reasons for analogous discoveries in fundamental physics and
neurobiology are discussed.Comment: 16 pages, Latex, 1 eps figure fil
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