709 citations · 1.3k across the 15 of their papers we have counts for
16 papers
A Blueprint for Equilibrium-Based Differentiable Continuous-Variable Thermodynamic Computing
Owen Lockwood, Jérémy Béjanin, Joost Bus +4
To help address the escalating energy and latency demands of machine-learning workloads, we introduce a blueprint for an energy-efficient and fast thermodynamic computing stack tha…
Thermalizing Stochastic Programs
Mirko Amico, Andraž Jelinčič, Colin Oscar Nancarrow +6
We present a set of tools for mapping general stochastic programs to thermodynamic hardware designed for energy-efficient stochastic sampling. Given a target stochastic program exp…
A Framework for Stochastic Differentiable Programming
Guillaume Verdon, Leo Tyrpak, Owen Lockwood +5
We introduce Parametrized Stochastic Circuits (PSCs), a gate-based intermediate representation for programmable stochastic dynamics in which typed local stochastic kernels with tun…
An efficient probabilistic hardware architecture for diffusion-like models
Andraž Jelinčič, Owen Lockwood, Akhil Garlapati +4
The proliferation of probabilistic AI has prompted proposals for specialized stochastic computers. Despite promising efficiency gains, these proposals have failed to gain traction…
Quantum Dynamical Hamiltonian Monte Carlo
Owen Lockwood, Peter Weiss, Filip Aronshtein +1
One of the open challenges in quantum computing is to find meaningful and practical methods to leverage quantum computation to accelerate classical machine learning workflows. A ub…
Challenges and Opportunities in Quantum Machine Learning
M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang +2
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with app…
Provably efficient variational generative modeling of quantum many-body systems via quantum-probabilistic information geometry
Faris M. Sbahi, Antonio J. Martinez, Sahil Patel +4
The dual tasks of quantum Hamiltonian learning and quantum Gibbs sampling are relevant to many important problems in physics and chemistry. In the low temperature regime, algorithm…
Group-Invariant Quantum Machine Learning
Martin Larocca, Frederic Sauvage, Faris M. Sbahi +3
Quantum Machine Learning (QML) models are aimed at learning from data encoded in quantum states. Recently, it has been shown that models with little to no inductive biases (i.e., w…
A semi-agnostic ansatz with variable structure for quantum machine learning
M. Bilkis, M. Cerezo, Guillaume Verdon +2
Quantum machine learning -- and specifically Variational Quantum Algorithms (VQAs) -- offers a powerful, flexible paradigm for programming near-term quantum computers, with applica…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…
Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm
Guillaume Verdon, Jacob Marks, Sasha Nanda +2
We introduce a new class of generative quantum-neural-network-based models called Quantum Hamiltonian-Based Models (QHBMs). In doing so, we establish a paradigmatic approach for qu…
Quantum Graph Neural Networks
Guillaume Verdon, Trevor McCourt, Enxhell Luzhnica +3
We introduce Quantum Graph Neural Networks (QGNN), a new class of quantum neural network ansatze which are tailored to represent quantum processes which have a graph structure, and…
Learning to learn with quantum neural networks via classical neural networks
Guillaume Verdon, Michael Broughton, Jarrod R. McClean +5
Quantum Neural Networks (QNNs) are a promising variational learning paradigm with applications to near-term quantum processors, however they still face some significant challenges.…
A Quantum Approximate Optimization Algorithm for continuous problems
Guillaume Verdon, Juan Miguel Arrazola, Kamil Brádler +1
We introduce a quantum approximate optimization algorithm (QAOA) for continuous optimization. The algorithm is based on the dynamics of a quantum system moving in an energy potenti…
A Universal Training Algorithm for Quantum Deep Learning
Guillaume Verdon, Jason Pye, Michael Broughton
We introduce the Backwards Quantum Propagation of Phase errors (Baqprop) principle, a central theme upon which we construct multiple universal optimization heuristics for training…
A quantum algorithm to train neural networks using low-depth circuits
Guillaume Verdon, Michael Broughton, Jacob Biamonte
Can near-term gate model based quantum processors offer quantum advantage for practical applications in the pre-fault tolerance noise regime? A class of algorithms which have shown…