The quest for a Quantum Neural Network

Schuld, Maria; Sinayskiy, Ilya; Petruccione, Francesco

doi:10.1007/s11128-014-0809-8

Cited by 436 publications

(305 citation statements)

References 66 publications

(113 reference statements)

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“…In addition to the papers that we have cited in this paper so far, we are also inspired by and have benefited from reading these ones [9][10][11][12][13][14][15][16][17].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Two Quantum Nearest Neighbor Classifiers on IBM’s Quantum Simulator

Hu¹

2018

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Today computers are used to store data in memory and then process them. In our big data era, we are facing the challenge of storing and processing the data simply due to their fast ever growing size. Quantum computation offers solutions to these two prominent issues quantum mechanically and beautifully. Through careful design to employ superposition, entanglement, and interference of quantum states, a quantum algorithm can allow a quantum computer to store datasets of exponentially large size as linear size and then process them in parallel. Quantum computing has found its way in the world of machine learning where new ideas and approaches are in great need as the classical computers have reached their capacity and the demand for processing big data grows much faster than the computing power the classical computers can provide today. Nearest neighbor algorithms are simple, robust, and versatile supervised machine learning algorithms, which store all training data points as their learned "model" and make the prediction of a new test data point by computing the distances between the query point and all the training data points. Quantum counterparts of these classical algorithms provide efficient and elegant ways to deal with the two major issues of storing data in memory and computing the distances. The purpose of our study is to select two similar quantum nearest neighbor algorithms and use a simple dataset to give insight into how they work, highlight their quantum nature, and compare their performances on IBM's quantum simulator.

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“…In addition to the papers that we have cited in this paper so far, we are also inspired by and have benefited from reading these ones [9][10][11][12][13][14][15][16][17].…”

Section: Discussionmentioning

confidence: 99%

Comparison of Two Quantum Nearest Neighbor Classifiers on IBM’s Quantum Simulator

Hu¹

2018

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“…Implementation of the quantum weightless neural network mathematically described in this paper requires a real quantum computer. Recent reviews on quantum neural networks can be found in [39,40].…”

Section: Quantum Neural Networkmentioning

confidence: 99%

Weightless neural network parameters and architecture selection in a quantum computer

2016

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Training artificial neural networks requires a tedious empirical evaluation to determine a suitable neural network architecture. To avoid this empirical process several techniques have been proposed to automatise the architecture selection process. In this paper, we propose a method to perform parameter and architecture selection for a quantum weightless neural network (qWNN). The architecture selection is performed through the learning procedure of a qWNN with a learning algorithm that uses the principle of quantum superposition and a non-linear quantum operator. The main advantage of the proposed method is that it performs a global search in the space of qWNN architecture and parameters rather than a local search.

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“…The peculiar features of quantum computing such as super position, entanglement, and interference of quantum states are generally considered resources for this speed up. Examples of quantum machine learning algorithms can be found in [1][2][3][4][5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer

Hu¹

2018

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A recent work has shown that using an ion trap quantum processor can speed up the decision making of a reinforcement learning agent. Its quantum advantage is observed when the external environment changes, and then agent needs to relearn again. One character of this quantum hardware system discovered in this study is that it tends to overestimate the values used to determine the actions the agent will take. IBM's five qubit superconducting quantum processor is a popular quantum platform. The aims of our study are twofold. First we want to identify the hardware characteristic features of IBM's 5Q quantum computer when running this learning agent, compared with the ion trap processor. Second, through careful analysis, we observe that the quantum circuit employed in the ion trap processor for this agent could be simplified. Furthermore, when tested on IBM's 5Q quantum processor, our simplified circuit demonstrates its enhanced performance over the original circuit on one of the hard learning tasks investigated in the previous work. We also use IBM's quantum simulator when a good baseline is needed to compare the performances. As more and more quantum hardware devices are moving out of the laboratory and becoming generally available to public use, our work emphasizes the fact that the features and constraints of the quantum hardware could take a toll on the performance of quantum algorithms.

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The quest for a Quantum Neural Network

Cited by 436 publications

References 66 publications

Comparison of Two Quantum Nearest Neighbor Classifiers on IBM’s Quantum Simulator

Comparison of Two Quantum Nearest Neighbor Classifiers on IBM’s Quantum Simulator

Weightless neural network parameters and architecture selection in a quantum computer

Empirical Analysis of Decision Making of an AI Agent on IBM’s 5Q Quantum Computer

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