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Quantum gradient descent and Newton's method for constrained polynomial optimization
Patrick Rebentrost, Maria Schuld, Leonard Wossnig +2
Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the directio…
Quantum Machine Learning
Jacob Biamonte, Peter Wittek, Nicola Pancotti +3
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce co…
An efficient quantum algorithm for spectral estimation
A. Steffens, P. Rebentrost, I. Marvian +2
We develop an efficient quantum implementation of an important signal processing algorithm for line spectral estimation: the matrix pencil method, which determines the frequencies…
Quantum singular value decomposition of non-sparse low-rank matrices
Patrick Rebentrost, Adrian Steffens, Seth Lloyd
In this work, we present a method to exponentiate non-sparse indefinite low-rank matrices on a quantum computer. Given an operation for accessing the elements of the matrix, our me…