63 citations · 84 across the 14 of their papers we have counts for
14 papers
Pushing the Classical Frontier of 1D Fermi-Hubbard Quench Dynamics Beyond Current Quantum Simulations
Roman Rausch, Sukhbinder Singh, Saeed S. Jahromi +2
Establishing quantum advantage requires comparison against the best achievable classical simulation. The Q-CTRL team recently simulated quench dynamics of the one-dimensional Fermi…
Fast Tensorization of Neural Networks via Slice-wise Feature Distillation
Safa Hamreras, Sukhbinder Singh, Román Orús
We propose a scalable tensorization framework for neural network compression based on slice-wise feature distillation. Unlike conventional tensor decomposition methods that rely on…
Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters
Borja Aizpurua, Sukhbinder Singh, Augustine Kshetrimayum +2
Large language models (LLMs) have transformed artificial intelligence, yet classical architectures impose a fundamental constraint: every trainable parameter demands classical memo…
Quantum Advantage: a Tensor Network Perspective
Augustine Kshetrimayum, Saeed S. Jahromi, Sukhbinder Singh +1
We review the recent quantum advantage experiments by IBM, D-Wave, and Google, focusing on cases where efficient classical simulations of the experiment were demonstrated or attemp…
Weight Clustering Reveals the Importance of Relative Rank in Large Language Models
Borja Aizpurua, Sukhbinder Singh, Román Orús
Large language models (LLMs) contain billions of parameters, but many exact weight values are not essential. We apply scalar -means clustering to pretrained models, replacing ea…
Globally optimized SVD compression of LLMs via Fermi-function-based rank selection and gauge fixing
Roman Rausch, David Jansen, Sukhbinder Singh +1
Large Language Models (LLMs) are very demanding in terms of their computational resources. Low-rank decompositions of LLM weights, e.g. via Singular Value Decomposition (SVD), is a…
Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
Borja Aizpurua, Sukhbinder Singh, Román Orús
We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz f…
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
Safa Hamreras, Sukhbinder Singh, Román Orús
Tensorizing a neural network involves reshaping some or all of its dense weight matrices into higher-order tensors and approximating them using low-rank tensor network decompositio…
Quantum Large Language Models via Tensor Network Disentanglers
Borja Aizpurua, Fernando Loren, Saeed S. Jahromi +2
We introduce a framework for seamlessly integrating quantum computing into pretrained large language models (LLMs). The key idea is to construct a hybrid quantum-classical represen…
Projected Entangled Pair States with flexible geometry
Siddhartha Patra, Sukhbinder Singh, Román Orús
Projected Entangled Pair States (PEPS) are a class of quantum many-body states that generalize Matrix Product States for one-dimensional systems to higher dimensions. In recent yea…
Tensor network compressibility of convolutional models
Sukhbinder Singh, Saeed S. Jahromi, Roman Orus
Convolutional neural networks (CNNs) are one of the most widely used neural network architectures, showcasing state-of-the-art performance in computer vision tasks. Although larger…
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Andrei Tomut, Saeed S. Jahromi, Abhijoy Sarkar +15
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such a…
Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
Pablo Martin-Ramiro, Unai Sainz de la Maza, Sukhbinder Singh +2
Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neur…
Efficient tensor network simulation of IBM's largest quantum processors
Siddhartha Patra, Saeed S. Jahromi, Sukhbinder Singh +1
We show how quantum-inspired 2d tensor networks can be used to efficiently and accurately simulate the largest quantum processors from IBM, namely Eagle (127 qubits), Osprey (433 q…