Prediction by linear regression on a quantum computer

Abstract

We give an algorithm for prediction on a quantum computer which is based on a linear regression model with least squares optimisation. Opposed to related previous contributions suffering from the problem of reading out the optimal parameters of the fit, our scheme focuses on the machine learning task of guessing the output corresponding to a new input given examples of data points. Furthermore, we adapt the algorithm to process non-sparse data matrices that can be represented by low-rank approximations, and significantly improve the dependency on its condition number. The prediction result can be accessed through a single qubit measurement or used for further quantum information processing routines. The algorithm's runtime is logarithmic in the dimension of the input space provided the data is given as quantum information as an input to the routine.

6 pages, 1 figure

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• Quantum algorithm for solving linear systems of equations
• Quantum random access memory
• Quantum Data Fitting
• Creating superpositions that correspond to efficiently integrable probability distributions
• Simulating a perceptron on a quantum computer
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