12 citations · 26 across the 10 of their papers we have counts for
11 papers
LLM Compression by Block Removal with Constrained Binary Optimization
David Jansen, Roman Rausch, Ali Hashemi +2
In this paper, we formulate the compression of large language models (LLMs) by optimally deleting transformer blocks (``block removal'') as a constrained binary optimization (CBO)…
Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Iker García-Ferrero, David Montero, Roman Orus
We introduce Refusal Steering, an inference-time method to exercise fine-grained control over Large Language Models refusal behaviour on politically sensitive topics without retrai…
epiGPTope: A machine learning-based epitope generator and classifier
Natalia Flechas Manrique, Alberto Martínez, Elena López-Martínez +5
Epitopes are short antigenic peptide sequences which are recognized by antibodies or immune cell receptors. These are central to the development of immunotherapies, vaccines, and d…
Synthetic Data Generation and Differential Privacy using Tensor Networks' Matrix Product States (MPS)
Alejandro Moreno R., Desale Fentaw, Samuel Palmer +7
Synthetic data generation is a key technique in modern artificial intelligence, addressing data scarcity, privacy constraints, and the need for diverse datasets in training robust…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
Quantum-Inspired Solver for Simulating Material Deformations
Mazen Ali, Aser Cortines, Siddhartha Morales +5
This paper explores the application of tensor networks (TNs) to the simulation of material deformations within the framework of linear elasticity. Material simulations are essentia…
Encoding of Probability Distributions for Quantum Monte Carlo Using Tensor Networks
Antonio Pereira, Alba Villarino, Aser Cortines +5
The application of Tensor Networks (TN) in quantum computing has shown promise, particularly for data loading. However, the assumption that data is readily available often renders…
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…
Entanglement, quantum phase transitions and quantum algorithms
Roman Orus
The work that we present in this thesis tries to be at the crossover of quantum information science, quantum many-body physics, and quantum field theory. We use tools from these th…
Systematic Analysis of Majorization in Quantum Algorithms
Roman Orus, Jose I. Latorre, Miguel A. Martin-Delgado
Motivated by the need to uncover some underlying mathematical structure of optimal quantum computation, we carry out a systematic analysis of a wide variety of quantum algorithms f…