Quantum support vector machine for big feature and big data classification

By Patrick Rebentrost, Masoud Mohseni, Seth Lloyd. Physical Review Letters, 2013

683 citations recorded by the source corpus.

Supervised machine learning is the classification of new data based on already classified training examples. In this work, we show that the support vector machine, an optimized binary classifier, can be implemented on a quantum computer, with complexity logarithmic in the size of the vectors and the number of training examples. In cases where classical sampling algorithms require polynomial time, an exponential speedup is obtained. At the core of this quantum big data algorithm is a nonsparse matrix exponentiation technique for efficiently performing a matrix inversion of the training data inner-product (kernel) matrix.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/d70cbaf8d9163d9c27a2635b2b764e4ebf400871

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Citations in the corpus

Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “The implementation of Quantum SVMs (QSVMs) has been presented in [185], where an exponential speed-up of QSVMs over classical SVMs has been reported.”
Quantum Recommendation Systems (2016, background): “This is a very powerful algorithm and has been very influential in recent times, where several works [16, 15, 17] obtained quantum algorithms for machine learning problems based on similar assumptions.”
Big data in healthcare: management, analysis and future prospects (2019, methodology): “In another example, the quantum support vector machine was implemented for both training and classification stages to classify new data [44].”
Hierarchical quantum classifiers (2018, methodology): “Quantum classifiers that use HHL include the quantum support vector machine [21] and the kernel least squares [22].”
Quantum-enhanced machine learning (2016, background): “Exactly this is done in [6, 8], for the purpose of improving computational complexity, with great success, as the improvement can be exponential.”
Effect of data encoding on the expressive power of variational quantum-machine-learning models (2020, methodology): “This model includes “parallel encodings” that repeat the encoding on different subsystems [29], as well as “data reuploading”, where the encoding is repeated multiple times in sequence [17] (see Fig.”
An introduction to quantum machine learning (2014, methodology): “[12] claim that, in general, the evaluation of an inner product can be done faster on a quantum computer.”
Quantum machine learning (2016, neutral): “In [20], Lloyd, Mohseni and Rebentrost devised a quantum algorithm for principal component analysis of an unknown low-rank density matrix.”