Corpus ID: 5828119

# Quantum Neuron: an elementary building block for machine learning on quantum computers

@article{Cao2017QuantumNA,
title={Quantum Neuron: an elementary building block for machine learning on quantum computers},
author={Yudong Cao and G. Guerreschi and Al{\'a}n Aspuru-Guzik},
journal={ArXiv},
year={2017},
volume={abs/1711.11240}
}
• Published 2017
• Computer Science, Physics
• ArXiv
Even the most sophisticated artificial neural networks are built by aggregating substantially identical units called neurons. A neuron receives multiple signals, internally combines them, and applies a non-linear function to the resulting weighted sum. Several attempts to generalize neurons to the quantum regime have been proposed, but all proposals collided with the difficulty of implementing non-linear activation functions, which is essential for classical neurons, due to the linear nature of… Expand
75 Citations
Continuous-variable quantum neural networks
• Mathematics, Computer Science
• ArXiv
• 2018
A general method for building neural networks on quantum computers and 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 are introduced. Expand
A Variational Algorithm for Quantum Neural Networks
• Computer Science
• ICCS
• 2020
This work introduces a novel variational algorithm for quantum Single Layer Perceptron and designs a quantum circuit to perform linear combinations in superposition, and discusses adaptations to classification and regression tasks. Expand
Variational Learning for Quantum Artificial Neural Networks
In the past few years, quantum computing and machine learning fostered rapid developments in their respective areas of application, introducing new perspectives on how information processing systemsExpand
Building quantum neural networks based on swap test
• Physics, Mathematics
• 2019
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 isExpand
An artificial neuron implemented on an actual quantum processor
• Computer Science
• 2018
It is shown that this quantum model of a perceptron can be trained in a hybrid quantum-classical scheme employing a modified version of the perceptron update rule and used as an elementary nonlinear classifier of simple patterns, as a first step towards practical quantum neural networks efficiently implemented on near-term quantum processing hardware. Expand
Towards a Real Quantum Neuron
Google’s AlphaGo represents the impressive performance of deep learning and the backbone of deep learning is the workhorse of highly versatile neural networks. Each network is made up of layers ofExpand
Variational learning for quantum artificial neural networks
• Computer Science
• 2020 IEEE International Conference on Quantum Computing and Engineering (QCE)
• 2020
An original realization of efficient individual quantum nodes based on variational unsampling protocols is presented, suggesting a viable approach towards the use of quantum neural networks for pattern classification on near-term quantum hardware. Expand
Training deep quantum neural networks
A noise-robust architecture for a feedforward quantum neural network, with qudits as neurons and arbitrary unitary operations as perceptrons, whose training procedure is efficient in the number of layers. Expand
Implementing Any Nonlinear Quantum Neuron
• Computer Science, Medicine
• IEEE Transactions on Neural Networks and Learning Systems
• 2020
This brief presents an architecture to simulate the computation of an arbitrary nonlinear function as a quantum circuit on the phase of an adequately designed quantum state, and quantum phase estimation recovers the result in a circuit with linear complexity in function of ANN input size. Expand
An Analytical Review of Quantum Neural Network Models and Relevant Research
• Computer Science
• 2020 5th International Conference on Communication and Electronics Systems (ICCES)
• 2020
The quantum analogue of one of the most important and popular model of machine learning, namely Artificial Neural Network is studied and reviewed here in this paper. Expand

#### References

SHOWING 1-10 OF 33 REFERENCES
Quantum generalisation of feedforward neural networks
• Computer Science, Physics
• 2016
It is demonstrated numerically that the proposed quantum generalisation of a classical neural network can compress quantum states onto a minimal number of qubits, create a quantum autoencoder, and discover quantum communication protocols such as teleportation. Expand
The quest for a Quantum Neural Network
• Computer Science, Physics
• Quantum Inf. Process.
• 2014
This article presents a systematic approach to QNN research, concentrating on Hopfield-type networks and the task of associative memory, and outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quantum computing. Expand
Simulations of quantum neural networks
• Mathematics, Computer Science
• Inf. Sci.
• 2000
It is shown that a single quantum dot molecule evolving in real time can act as a recurrent temporal quantum neural network, and the quantum Hopfield net, a regular array of quantum dot molecules on a suitable substrate, is simulated. Expand
A Quantum Recurrent Neural Network
• Mathematics
• 2017
Quantum computing allows for the potential of significant advancements in both the speed and the capacity of widely-used machine learning algorithms. In this paper, we introduce quantum algorithmsExpand
Quantum artificial neural network architectures and components
• Computer Science
• Inf. Sci.
• 2000
Overall, this work provides a first insight into the expected behaviour of individual components of QUANNs, if and when quantum hardware is ever built, and raises questions about the interface between quantum and classical components of futureQUANNs. Expand
On Quantum Neural Computing
• S. Kak
• Computer Science
• Inf. Sci.
• 1995
It is shown that information is not a locally additive variable in a quantum computation; this property may be used to examine the nature of biological information structures. Expand
Quantum autoencoders for efficient compression of quantum data
• Mathematics, Physics
• 2017
Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x, to map x to aExpand
Practical optimization for hybrid quantum-classical algorithms
• Mathematics, Physics
• 2017
A novel class of hybrid quantum-classical algorithms based on the variational approach have recently emerged from separate proposals addressing, for example, quantum chemistry and combinatorialExpand
Quantum arithmetic and numerical analysis using Repeat-Until-Success circuits
• Computer Science, Mathematics
• Quantum Inf. Comput.
• 2016
We develop a method for approximate synthesis of single--qubit rotations of the form $e^{-i f(\phi_1,\ldots,\phi_k)X}$ that is based on the Repeat-Until-Success (RUS) framework for quantum circuitExpand
Quantum associative memory with exponential capacity
• Mathematics
• 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227)
• 1998
Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts byExpand