12 citations · 31 across the 9 of their papers we have counts for
11 papers
Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data
Boaz Micah, Nadia Milazzo, Maissa Beji +8
Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separatel…
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…
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…
Tensor Network based Gene Regulatory Network Inference for Single-Cell Transcriptomic Data
Olatz Sanz Larrarte, Borja Aizpurua, Reza Dastbasteh +2
Deciphering complex gene-gene interactions remains challenging in transcriptomics as traditional methods often miss higher-order and nonlinear dependencies. This study introduces 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…
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…
Hacking Cryptographic Protocols with Tensor Network Attacks
Borja Aizpurua, Siddhartha Patra, Josu Etxezarreta Martinez +1
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 rec…
Tensor Networks for Explainable Machine Learning in Cybersecurity
Borja Aizpurua, Samuel Palmer, Roman Orus
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 M…
Hacking Cryptographic Protocols with Advanced Variational Quantum Attacks
Borja Aizpurua, Pablo Bermejo, Josu Etxezarreta Martinez +1
Here we introduce an improved approach to Variational Quantum Attack Algorithms (VQAA) on crytographic protocols. Our methods provide robust quantum attacks to well-known cryptogra…
Improving Gradient Methods via Coordinate Transformations: Applications to Quantum Machine Learning
Pablo Bermejo, Borja Aizpurua, Roman Orus
Machine learning algorithms, both in their classical and quantum versions, heavily rely on optimization algorithms based on gradients, such as gradient descent and alike. The overa…
Multi-disk clutch optimization using quantum annealing
John D. Malcolm, Alexander Roth, Mladjan Radic +5
In this work, we develop a new quantum algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how qua…