Akash Sengupta

Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning

Nature Neuroscience · 2023 · 55 citations

Shadow tomography based on informationally complete positive operator-valued measure

Physical review. A/Physical review, A · 2021 · 42 citations

Recently introduced shadow tomography protocols use ``classical shadows'' of quantum states to predict many target functions of an unknown quantum state. Unlike full quantum state tomography, shadow tomography does not insist on accurate recovery of the density matrix for high rank mixed states. Yet, such a protocol makes multiple accurate predictions with high confidence, based on a moderate number of quantum measurements. One particular influential algorithm, proposed by Huang et al. [Huang, K…

Compressing the two-particle Green’s function using wavelets: Theory and application to the Hubbard atom

The European Physical Journal Plus · 2024-08-07 · 6 citations

Abstract Precise algorithms capable of providing controlled solutions in the presence of strong interactions are transforming the landscape of quantum many-body physics. Particularly, exciting breakthroughs are enabling the computation of non-zero temperature correlation functions. However, computational challenges arise due to constraints in resources and memory limitations, especially in scenarios involving complex Green’s functions and lattice effects. Leveraging the principles of signal proc…

Normative Framework for Deriving Neural Networks with Multicompartmental Neurons and Non-Hebbian Plasticity

PRX Life · 2023-08-10 · 4 citations

This study derives an online algorithm implementable in a neural network with local updating rules to solve a specific class of complex tasks establishing a precise relationship between realistic synaptic learning rules and underlying computational principles that guide their design.

Machine learning-based compression of quantum many body physics: PCA and autoencoder representation of the vertex function

Machine Learning Science and Technology · 2024-12-01 · 3 citations

Abstract Theoretical approaches to quantum many-body physics require developing compact representations of the complexity of generic quantum states. This paper explores an interpretable data-driven approach utilizing principal component analysis (PCA) and autoencoder neural networks to compress the two-particle vertex, a key element in Feynman diagram approaches. We show that the linear PCA offers more physical insight and better out-of-distribution generalization than the nominally more express…