40 citations · 107 across the 12 of their papers we have counts for
7 papers · 1 filter
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…
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…
Higgs analysis with quantum classifiers
Vasileios Belis, Samuel González-Castillo, Christina Reissel +4
We have developed two quantum classifier models for the classification problem, both of which fall into the category of hybrid quantum-classical algorithms fo…
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Su Yeon Chang, Steven Herbert, Sofia Vallecorsa +2
Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in cer…