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Found 5 papers, 1 papers with code
Scalable Thermodynamic Second-order Optimization
Many hardware proposals have aimed to accelerate inference in AI workloads.
Thermodynamic Bayesian Inference
Thermodynamic computing has emerged as a paradigm for accelerating operations used in machine learning, such as matrix inversion, and is based on the mapping of Langevin equations to the dynamics of noisy physical systems.
Thermodynamic Computing System for AI Applications
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for novel computing hardware in order to truly unlock the potential for AI.
Thermodynamic AI and the fluctuation frontier
Hence, we propose a novel computing paradigm, where software and hardware become inseparable.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
