Jake McNaughton

Jake McNaughton, A. H. Abbas, Ivan S. Maksymov

The mathematical complexity and high dimensionality of neural networks hinder the training and deployment of machine learning (ML) systems while also requiring substantial computational resources. This fundamental limitation drives ML research, particularly in the exploration of alternative neural network architectures that integrate novel building blocks, such as advanced activation functions. Tunnel diodes are well-known electronic components that utilise the physical effect of quantum tunnelling (QT). Here, we propose using the current voltage characteristic of a tunnel diode as a novel, physics-based activation function for neural networks. We demonstrate that the tunnel-diode activation function (TDAF) outperforms traditional activation functions in terms of accuracy and loss during both training and evaluation. We also highlight its potential for implementation in electronic circuits suited to developing neuromorphic, quantum-inspired AI systems capable of operating in environments not suitable for qubit-based quantum computing hardware.

Jake McNaughton, Mohamed Hibat-Allah

Neural-network quantum states (NQS) are powerful neural-network ansätzes that have emerged as promising tools for studying quantum many-body physics through the lens of the variational principle. These architectures are known to be systematically improvable by increasing the number of parameters. Here we demonstrate an Adaptive scheme to optimize NQSs, through the example of recurrent neural networks (RNN), using a fraction of the computation cost while reducing training fluctuations and improving the quality of variational calculations targeting ground states of prototypical models in one- and two-spatial dimensions. This Adaptive technique reduces the computational cost through training small RNNs and reusing them to initialize larger RNNs. This work opens up the possibility for optimizing graphical processing unit (GPU) resources deployed in large-scale NQS simulations.