Supervised learning with quantum-enhanced feature spaces

By Vojtěch Havlíček, A. C'orcoles, Kristan Temme, Aram Wettroth Harrow, Abhinav Kandala, Jerry M. Chow, Jay M. Gambetta. Nature, 2018

2,729 citations recorded by the source corpus.

Machine learning and quantum computing are two technologies that each have the potential to alter how computation is performed to address previously untenable problems. Kernel methods for machine learning are ubiquitous in pattern recognition, with support vector machines (SVMs) being the best known method for classification problems. However, there are limitations to the successful solution to such classification problems when the feature space becomes large, and the kernel functions become computationally expensive to estimate. A core element in the computational speed-ups enabled by quantum algorithms is the exploitation of an exponentially large quantum state space through controllable entanglement and interference. Here we propose and experimentally implement two quantum algorithms on a superconducting processor. A key component in both methods is the use of the quantum state space as feature space. The use of a quantum-enhanced feature space that is only efficiently accessible on a quantum computer provides a possible path to quantum advantage. The algorithms solve a problem of supervised learning: the construction of a classifier. One method, the quantum variational classifier, uses a variational quantum circuit1,2 to classify the data in a way similar to the method of conventional SVMs. The other method, a quantum kernel estimator, estimates the kernel function on the quantum computer and optimizes a classical SVM. The two methods provide tools for exploring the applications of noisy intermediate-scale quantum computers3 to machine learning. Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/0dfcec3139b2b52a3b6a144f323f89dd37de1fa4

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Citations in the corpus

Challenges and opportunities in quantum machine learning (2022, methodology): “Several embedding schemes have been proposed [22, 26, 62], and there are some desirable properties they must possess.”
Evaluating analytic gradients on quantum hardware (2018, background): “Examples of such algorithms are variational quantum eigensolvers [1], quantum approximate optimization [2], variational autoencoders [3], quantum feature embeddings [4, 5] and variational classiﬁers [6, 7], but also more general hybrid optimization frameworks [8].”
Robust data encodings for quantum classifiers (2020, methodology): “[40] V.”
Parameterized quantum circuits as machine learning models (2019, methodology): “[22] experimentally demonstrate QKE and VQM classifiers on two superconducting qubits of the IBM Q5 Yorktown.”