Implementing a distance-based classifier with a quantum interference circuit

Schuld, Maria; Fingerhuth, Mark; Petruccione, Francesco

doi:10.1209/0295-5075/119/60002

Cited by 245 publications

(262 citation statements)

References 24 publications

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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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“…We will briefly present the distance based quantum classifier created in [2]. Quantum computing requires the data be encoded in quantum states for storage, transformation, and processing.…”

Section: A Quantum Classifiermentioning

confidence: 99%

“…Quantum computing requires the data be encoded in quantum states for storage, transformation, and processing. In the context of the analysis of classical data, one method is to encode the coordinates of a classical data point as amplitudes of the quantum state, adopted by [2]. Another straight forward encoding method is to encode one classical bit of information into a qubit.…”

Section: A Quantum Classifiermentioning

confidence: 99%

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Empirical Analysis of a Quantum Classifier Implemented on IBM’s 5Q Quantum Computer

Hu¹

2018

JQIS

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The development of artificial intelligence today is marked with increased computational power, new algorithms, and big data. One such milestone impressive achievement in this area is Google's AlphaGo. However, this advancement is beginning to face increasing challenges and the major bottleneck of AI today is the lack of adequate computing power in the processing of big data. Quantum computing offers a new and viable solution to deal with these challenges. A recent work designed a quantum classifier that runs on IBM's five qubit quantum computer and tested its performance on the Iris data set as well as a circles data set. As quantum machine learning is still an emerging discipline, it may be enlightening to conduct an empirical analysis of this quantum classifier on some artificial datasets to help learn its unique features and potentials. Our work on the quantum classifier can be summarized in three parts. The first is to run its original version as a binary classifier on some artificial datasets using visualization to reveal the quantum nature of this algorithm, and the second is to analyze the swap operation utilized in its original circuit due to the hardware constraint and investigate its impact on the performance of the classifier. The last part is to extend the original circuit for binary classification to a circuit for multiclass classification and test its performance. Our findings shed new light on how this quantum classifier works.

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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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Implementing a distance-based classifier with a quantum interference circuit

Cited by 245 publications

References 24 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

Empirical Analysis of a Quantum Classifier Implemented on IBM’s 5Q Quantum Computer

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

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