17 citations · 132 across the 43 of their papers we have counts for
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Generative modeling using evolved quantum Boltzmann machines
Mark M. Wilde
Born-rule generative modeling, a central task in quantum machine learning, seeks to learn probability distributions that can be efficiently sampled by measuring complex quantum sta…
Fundamentals of quantum Boltzmann machine learning with visible and hidden units
Mark M. Wilde
One of the primary applications of classical Boltzmann machines is generative modeling, wherein the goal is to tune the parameters of a model distribution so that it closely approx…
Performance Guarantees for Quantum Neural Estimation of Entropies
Sreejith Sreekumar, Ziv Goldfeld, Mark M. Wilde
Estimating quantum entropies and divergences is an important problem in quantum physics, information theory, and machine learning. Quantum neural estimators (QNEs), which utilize a…
Accelerated optimization of measured relative entropies
Zixin Huang, Mark M. Wilde
The measured relative entropy and measured Rényi relative entropy are quantifiers of the distinguishability of two quantum states and . They are defined as the maximum class…
Solving Conic Programs over Sparse Graphs using a Variational Quantum Approach: The Case of the AC Optimal Power Flow
Thinh Viet Le, Mark M. Wilde, Vassilis Kekatos
Conic programs arising in physics, quantum information, machine learning, and engineering are often defined over sparse graphs. Although such problems can be solved in polynomial t…
Near-optimal performance of square-root measurement for general score functions and quantum ensembles
Hemant K. Mishra, Ludovico Lami, Mark M. Wilde
The Barnum-Knill theorem states that the optimal success probability in the multiple state discrimination task is not more than the square root of the success probability when the…
Fundamental limits for thermodynamic control with quantum feedback
Kaiyuan Ji, Gilad Gour, Mark M. Wilde
The study of feedback control inspired by Maxwell's demon has yielded fundamental insights into the relationship between thermodynamics and information. However, its fully quantum…
Quantum natural gradient with thermal-state initialization
Michele Minervini, Dhrumil Patel, Mark M. Wilde
Parameterized quantum circuits (PQCs) are central to variational quantum algorithms (VQAs), yet their performance is hindered by complex loss landscapes that make their trainabilit…
Digital Quantum Simulations of the Non-Resonant Open Tavis-Cummings Model
Aidan N. Sims, Dhrumil Patel, Aby Philip +4
The open Tavis--Cummings model consists of quantum emitters interacting with a common cavity mode, accounts for losses and decoherence, and is frequently explored for quantum i…
Converse bounds for quantum hypothesis exclusion: A divergence-radius approach
Kaiyuan Ji, Hemant K. Mishra, Milán Mosonyi +1
Hypothesis exclusion is an information-theoretic task in which an experimenter aims at ruling out a false hypothesis from a finite set of known candidates, and an error occurs if a…
Evolved Quantum Boltzmann Machines
Michele Minervini, Dhrumil Patel, Mark M. Wilde
We introduce evolved quantum Boltzmann machines as a variational ansatz for quantum optimization and learning tasks. Given two parameterized Hamiltonians and , an evol…