32 citations · 87 across the 16 of their papers we have counts for
16 papers
Scalable Thermodynamic Second-order Optimization
Kaelan Donatella, Samuel Duffield, Denis Melanson +7
Many hardware proposals have aimed to accelerate inference in AI workloads. Less attention has been paid to hardware acceleration of training, despite the enormous societal impact…
Thermodynamic Algorithms for Quadratic Programming
Patryk-Lipka Bartosik, Kaelan Donatella, Maxwell Aifer +4
Thermodynamic computing has emerged as a promising paradigm for accelerating computation by harnessing the thermalization properties of physical systems. This work introduces a nov…
Thermodynamic Bayesian Inference
Maxwell Aifer, Samuel Duffield, Kaelan Donatella +6
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks…
Scalable Bayesian Learning with posteriors
Samuel Duffield, Kaelan Donatella, Johnathan Chiu +2
Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior…
Thermodynamic Natural Gradient Descent
Kaelan Donatella, Samuel Duffield, Maxwell Aifer +3
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead.…
Error Mitigation for Thermodynamic Computing
Maxwell Aifer, Denis Melanson, Kaelan Donatella +3
While physics-based computing can offer speed and energy efficiency compared to digital computing, it also is subject to errors that must be mitigated. For example, many error miti…
Thermodynamic Computing System for AI Applications
Denis Melanson, Mohammad Abu Khater, Maxwell Aifer +7
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI. Physics-based…
Exploiting Inductive Biases in Video Modeling through Neural CDEs
Johnathan Chiu, Samuel Duffield, Max Hunter-Gordon +3
We introduce a novel approach to video modeling that leverages controlled differential equations (CDEs) to address key challenges in video tasks, notably video interpolation and ma…
Thermodynamic Linear Algebra
Maxwell Aifer, Kaelan Donatella, Max Hunter Gordon +5
Linear algebraic primitives are at the core of many modern algorithms in engineering, science, and machine learning. Hence, accelerating these primitives with novel computing hardw…
Thermodynamic AI and the fluctuation frontier
Patrick J. Coles, Collin Szczepanski, Denis Melanson +3
Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic fluctuations. We connect these physics-inspired AI algorithms by unifying them under a si…
Efficient estimation of trainability for variational quantum circuits
Valentin Heyraud, Zejian Li, Kaelan Donatella +2
Parameterized quantum circuits used as variational ansätze are emerging as promising tools to tackle complex problems ranging from quantum chemistry to combinatorial optimization.…
Dynamics with autoregressive neural quantum states: application to critical quench dynamics
Kaelan Donatella, Zakari Denis, Alexandre Le Boité +1
Despite very promising results, capturing the dynamics of complex quantum systems with neural-network ansätze has been plagued by several problems, one of which being stochastic no…
Machine learning via relativity-inspired quantum dynamics
Zejian Li, Valentin Heyraud, Kaelan Donatella +2
We present a machine-learning scheme based on the relativistic dynamics of a quantum system, namely a quantum detector inside a cavity resonator. An equivalent analog model can be…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits
Kaelan Donatella, Zakari Denis, Alexandre Le Boité +1
We investigate the continuous-time dynamics of highly-entangling intermediate-scale quantum circuits in the presence of dissipation and decoherence. By compressing the Hilbert spac…
Entanglement dynamics in dissipative photonic Mott insulators
Kaelan Donatella, Alberto Biella, Alexandre Le Boité +1
We theoretically investigate the entanglement dynamics in photonic Mott insulators in the presence of particle losses and dephasing. We explore two configurations where entanglemen…