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Articles by Patrick Rebentrost
[2609.39908] Quantum Sensing and Hamiltonian Learning under Stochastic Parameter Evolution
We introduce the notion of stochastically evolving Hamiltonians (SEHs), which are local Hamiltonians whose coefficients are governed by stochastic differential equations (SDEs). After developing some theoretical aspects of these objects, we consider the task of Hamiltonian learning for SEHs.
Artificial intelligence for representing and characterizing quantum systems
Abstract Efficient characterization of large-scale quantum systems, especially those produced by quantum analog simulators and megaquop quantum computers, poses a central challenge in quantum science owing to the exponential scaling of the Hilbert space with respect to system size. Recent advances in artificial intelligence (AI), with its aptitude for high-dimensional pattern recognition and function approximation, have emerged as a powerful tool to address this challenge.
Quantum learning with tunable loss functions
Abstract Learning from quantum data presents new challenges, particularly regarding robustness in the presence of real-world noise, data contamination, and outliers. While recent work on Quantum Empirical Risk Minimization (QERM) provides a foundational framework for learning from classical-quantum data, its sensitivity to data anomalies can limit its practical utility.
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
Abstract We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compositional structure of KAN and is based on block-encodings, constructed recursively from a single layer using quantum singular value transformation. We demonstrate the algorithmic utility of QKAN in two applications.
Fast algorithm for quantum polar decomposition and applications
Abstract The polar decomposition of a matrix is a key element in the quantum linear algebra toolbox. We show that the problem of quantum polar decomposition, recently studied in Lloyd et al. [arXiv:2006.00841], has a simple and concise implementation via the quantum singular value transform (QSVT).
[1611.09347] Quantum Machine Learning
(Submitted on 28 Nov 2016 ( v1 ), last revised 10 May 2018 (this version, v2)) Submission history From: Nicola Pancotti Mr. [ view email ] [v1] Mon, 28 Nov 2016 20:59:46 UTC (991 KB) [v2] Thu, 10 May 2018 19:44:58 UTC (105 KB)
Quantum machine learning
Long before we possessed computers, human beings strove to find patterns in data. Ptolemy fitted observations of the motions of the stars to a geocentric model of the cosmos, with complex epicycles to explain the retrograde motions of the planets. In the sixteenth century, Kepler analysed the data of Copernicus and Brahe to reveal a previously hidden pattern: planets move in ellipses with the Sun at one focus of the ellipse.
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