40 citations · 107 across the 12 of their papers we have counts for
12 papers
Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks
Rohit Sarma Sarkar, Rupayan Bhattacharjee, Elias F. Combarro +5
Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and pa…
Quantum Neural Network Architectures for Multivariate Time-Series Forecasting
Sandra Ranilla-Cortina, Diego A. Aranda, Jorge Ballesteros +4
In this paper, we address the challenge of multivariate time-series forecasting using quantum machine learning techniques. We introduce adaptation strategies that extend variationa…
Quantum Computing for Large-scale Network Optimization: Opportunities and Challenges
Sebastian Macaluso, Giovanni Geraci, Elías F. Combarro +4
The complexity of large-scale 6G-and-beyond networks demands innovative approaches for multi-objective optimization over vast search spaces, a task often intractable. Quantum compu…
Guided Graph Compression for Quantum Graph Neural Networks
Mikel Casals, Vasilis Belis, Elias F. Combarro +3
Graph Neural Networks (GNNs) are effective for processing graph-structured data but face challenges with large graphs due to high memory requirements and inefficient sparse matrix…
Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
Michael Schenk, Elías F. Combarro, Michele Grossi +4
Free energy-based reinforcement learning (FERL) with clamped quantum Boltzmann machines (QBM) was shown to significantly improve the learning efficiency compared to classical Q-lea…
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
Su Yeon Chang, Edwin Agnew, Elías F. Combarro +3
In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN. We applied the model on a…