Zobrazeno 1 - 10
of 192
pro vyhledávání: '"Orus, Roman"'
Here we introduce the application of Tensor Networks (TN) to launch attacks on symmetric-key cryptography. Our approaches make use of Matrix Product States (MPS) as well as our recently-introduced Flexible-PEPS Quantum Circuit Simulator (FQCS). We co
Externí odkaz:
http://arxiv.org/abs/2409.04125
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 years, PEPS have advanced understanding of strongly correlated systems, e
Externí odkaz:
http://arxiv.org/abs/2407.21140
This article introduces a novel approach to perform the simulation of a single qubit quantum algorithm using laser beams. Leveraging the polarization states of photonic qubits, and inspired by variational quantum eigensolvers, we develop a variationa
Externí odkaz:
http://arxiv.org/abs/2405.04142
Variational quantum algorithms are gaining attention as an early application of Noisy Intermediate-Scale Quantum (NISQ) devices. One of the main problems of variational methods lies in the phenomenon of Barren Plateaus, present in the optimization of
Externí odkaz:
http://arxiv.org/abs/2403.15031
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 CNNs generally exhibit higher accuracy, their size can be effectively
Externí odkaz:
http://arxiv.org/abs/2403.14379
Publikováno v:
J. Chem. Theory Comput. 2024, 20, 5133-5144
The Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (ADAPT-VQE) has emerged as a pivotal promising approach for electronic structure challenges in quantum chemistry with noisy quantum devices. Nevertheless, to surmount ex
Externí odkaz:
http://arxiv.org/abs/2403.09624
Autor:
Tomut, Andrei, Jahromi, Saeed S., Sarkar, Abhijoy, Kurt, Uygar, Singh, Sukhbinder, Ishtiaq, Faysal, Muñoz, Cesar, Bajaj, Prabdeep Singh, Elborady, Ali, del Bimbo, Gianni, Alizadeh, Mehrazin, Montero, David, Martin-Ramiro, Pablo, Ibrahim, Muhammad, Alaoui, Oussama Tahiri, Malcolm, John, Mugel, Samuel, Orus, Roman
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 as huge training and inference costs, substantial energy demands, and l
Externí odkaz:
http://arxiv.org/abs/2401.14109
In this paper we show how tensor networks help in developing explainability of machine learning algorithms. Specifically, we develop an unsupervised clustering algorithm based on Matrix Product States (MPS) and apply it in the context of a real use-c
Externí odkaz:
http://arxiv.org/abs/2401.00867
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 Neural Network (T-CNN) and examine its performance on a real defect detect
Externí odkaz:
http://arxiv.org/abs/2401.01373
Here we introduce an improved approach to Variational Quantum Attack Algorithms (VQAA) on crytographic protocols. Our methods provide robust quantum attacks to well-known cryptographic algorithms, more efficiently and with remarkably fewer qubits tha
Externí odkaz:
http://arxiv.org/abs/2311.02986