Emerging Paradigms of Machine Learning

*Artificial Intelligence - Applications in Medicine and Biology*

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18.01-eng.pdf

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Chapter 5

Abstract

1. Introduction

95

Experience cloud-based access.

Quantum Neural Machine

Carlos Pedro dos Santos Gonçalves

learn to optimize tasks and act accordingly.

Learning: Theory and Experiments

Cloud-based access to quantum computers opens up the way for the empirical implementation of quantum artificial neural networks and for the future integration of quantum computation in different devices, using the cloud to access a quantum computer. The current work experimentally implements quantum artificial neural networks on IBM's quantum computers, accessed via cloud. Examples are provided for the XOR Boolean function representation problem and decision under risk; in the last case, quantum object-oriented programming using IBM's Qiskit Python library is employed to implement a form of quantum neural reinforcement learning applied to a classical decision under risk problem, showing how decision can be integrated into a quantum artificial intelligence system, where an artificial agent learns how to select an optimal action when facing a classical gamble. A final reflection is provided on quantum robotics and a future where robotic systems are connected to quantum computers via cloud, using quantum neural computation to

Keywords: quantum artificial neural networks, quantum neural reinforcement

Research on quantum neural machine learning has, until recently, mostly been a theoretical effort, anticipating a future where quantum computers would become available and sufficiently advanced to support quantum neural machine learning

implementing quantum artificial neural networks (QUANNs) experimentally, and one is able to access these computers via cloud. This brings QUANNs from the purely theoretical realm to the experimental realm, setting up the new stage for the expansion of quantum connectionism. In the current chapter, we address this issue, by implementing different QUANNs on IBM's quantum computers using the IBM Q

The chapter is divided into three sections. In Section 2, we address the basic properties of quantum neural computation, the connection with the quantum circuit computation model, and how different interpretations of quantum mechanics

In Section 3, we discuss how the IBM quantum computers can be considered QUANNs, illustrating with an example of a QUANN applied to the problem of the XOR Boolean function computation, implemented experimentally on two of IBM's

learning, quantum object-oriented programming, decision under risk

[1–5]. However, we now have quantum computers that are capable of

may address the basic computational dynamics involved.
