178 citations · 218 across the 6 of their papers we have counts for
8 papers
Beyond Ansätze: Learning Quantum Circuits as Unitary Operators
Bálint Máté, Bertrand Le Saux, Maxwell Henderson
This paper explores the advantages of optimizing quantum circuits on wires as operators in the unitary group . We run gradient-based optimization in the Lie algebra $\m…
Synthetic weather radar using hybrid quantum-classical machine learning
Graham R. Enos, Matthew J. Reagor, Maxwell P. Henderson +4
The availability of high-resolution weather radar images underpins effective forecasting and decision-making. In regions beyond traditional radar coverage, generative models have e…
Quantum versus Classical Generative Modelling in Finance
Brian Coyle, Maxwell Henderson, Justin Chan Jin Le +3
Finding a concrete use case for quantum computers in the near term is still an open question, with machine learning typically touted as one of the first fields which will be impact…
Methods for Accelerating Geospatial Data Processing Using Quantum Computers
Maxwell Henderson, Jarred Gallina, Michael Brett
Quantum computing is a transformative technology with the potential to enhance operations in the space industry through the acceleration of optimization and machine learning proces…
Optimizing the Optimizer: Decomposition Techniques for Quantum Annealing
Gideon Bass, Max Henderson, Joshua Heath +1
Although quantum computing hardware has evolved significantly in recent years, spurred by increasing industrial and government interest, the size limitation of current generation q…
Quanvolutional Neural Networks: Powering Image Recognition with Quantum Circuits
Maxwell Henderson, Samriddhi Shakya, Shashindra Pradhan +1
Convolutional neural networks (CNNs) have rapidly risen in popularity for many machine learning applications, particularly in the field of image recognition. Much of the benefit ge…
Leveraging Adiabatic Quantum Computation for Election Forecasting
Maxwell Henderson, John Novak, Tristan Cook
Accurate, reliable sampling from fully-connected graphs with arbitrary correlations is a difficult problem. Such sampling requires knowledge of the probabilities of observing every…
Application of Quantum Annealing to Training of Deep Neural Networks
Steven H. Adachi, Maxwell P. Henderson
In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), t…