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69 papers · 1 filter
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility…
Measurement-induced entanglement phase transitions in variational quantum circuits
Roeland Wiersema, Cunlu Zhou, Juan Felipe Carrasquilla +1
Variational quantum algorithms (VQAs), which classically optimize a parametrized quantum circuit to solve a computational task, promise to advance our understanding of quantum many…
GraN-GAN: Piecewise Gradient Normalization for Generative Adversarial Networks
Vineeth S. Bhaskara, Tristan Aumentado-Armstrong, Allan Jepson +1
Modern generative adversarial networks (GANs) predominantly use piecewise linear activation functions in discriminators (or critics), including ReLU and LeakyReLU. Such models lear…
Decentralized Federated Learning through Proxy Model Sharing
Shivam Kalra, Junfeng Wen, Jesse C. Cresswell +2
Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework tha…
Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment
Matthew J. Filipovich, Zhimu Guo, Mohammed Al-Qadasi +6
There has been growing interest in using photonic processors for performing neural network inference operations; however, these networks are currently trained using standard digita…
Learning quantum dynamics with latent neural ODEs
Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw +1
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question. In the area of quan…