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Introduction
Abstract
Why should we look at quantum computing in machine learning? Apart from a speedup and increased storage capacity, quantum computing has further benefits for machine learning algorithms. Learning models lie at the core of data mining, a complex process of extracting meaningful information from large volumes of data. We expect a machine learning model to generalize well beyond a limited training collection. On classical computers, we are constrained by convexity conditions or heuristics to keep computational time under control. Quantum computers do not suffer from these issues in optimization problems; hence, we can achieve better generalization performance. Classical computers excel at other tasks; hence, a heterogeneous model is likely to prevail. Dozens of approaches have already been published on quantum machine learningâwe briefly overview the major ones. We further mention classical algorithms that borrow metaphors from quantum mechanics; this is also a prolific area of research.
Keywords
Quantum computing
Machine learning
Data mining
Optimization
Convexity
Nonconvex problems
Quantum speedup
The quest of machine learning is ambitious: the discipline seeks to understand what learning is, and studies how algorithms approximate learning. Quantum machine learning takes these ambitions a step further: quantum computing enrolls the help of nature at a subatomic level to aid the learning process.
Machine learning is based on minimizing a constrained multivariate function, and these algorithms are at the core of data mining and data visualization techniques. The result of the optimization is a decision function that maps input points to output points. While this view on machine learning is simplistic, and exceptions are countless, some form of optimization is always central to learning theory.
The idea of using quantum mechanics for computations stems from simulating such systems. Feynman (1982) noted that simulating quantum systems on classical computers becomes unfeasible as soon as the system size increases, whereas quantum particles would not suffer from similar constraints. Deutsch (1985) generalized the idea. He noted that quantum computers are universal Turing machines, and that quantum parallelism implies that certain probabilistic tasks can be performed faster than by any classical means.
Today, quantum information has three main specializations: quantum computing, quantum information theory, and quantum cryptography (Fuchs, 2002, p. 49). We are not concerned with quantum cryptography, which primarily deals with secure exchange of information. Quantum information theory studies the storage and transmission of information encoded in quantum states; we rely on some concepts such as quantum channels and quantum process tomography. Our primary focus, however, is quantum computing, the field of inquiry that uses quantum phenomena such as superposition, entanglement, and interference to operate on data represented by quantum states.
Algorithms of importance emerged a decade after the first proposals of quantum computing appeared. Shor (1997) introduced a method to factorize integers exponentially faster, and Grover (1996) presented an algorithm to find an element ...