20 citations · 48 across the 8 of their papers we have counts for
11 papers
Shaping double descent in quantum extreme learning through model design
Sabri Meyer, Ege Yilmaz, Aurelien Lucchi +2
Increasing capacity can induce a machine learning model to generalize worse until it perfectly interpolates the training data, and better once it moves beyond interpolation. This i…
Spectral Gap Estimation via Adiabatic Preparation
Davide Cugini, Francesco Ghisoni, Angela Rosy Morgillo +1
Estimating energy gaps, i.e. the energy difference between two different states, in quantum systems is crucial for understanding their properties. Conventionally, spectral gap esti…
Noise-Induced Equalization in quantum learning models
Francesco Scala, Giacomo Guarnieri, Aurelien Lucchi
Quantum noise is known to strongly affect quantum computation, thus potentially limiting the performance of currently available quantum processing units. Even learning models based…
Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes
Sabri Meyer, Francesco Scala, Francesco Tacchino +1
Variational Quantum Algorithms are promising candidates for near-term quantum computing, yet they face scalability challenges due to barren plateaus, where gradients vanish exponen…
Towards Practical Quantum Neural Network Diagnostics with Neural Tangent Kernels
Francesco Scala, Christa Zoufal, Dario Gerace +1
Knowing whether a Quantum Machine Learning model would perform well on a given dataset before training it can help to save critical resources. However, gathering a priori informati…
Resource-efficient quantum algorithm for linear systems of equations
Francesco Ghisoni, Francesco Scala, Daniele Bajoni +1
Finding the solution to linear systems is at the heart of many applications in science and technology. Over the years a number of algorithms have been proposed to solve this proble…
Spectral Gap Superposition States
Davide Cugini, Francesco Ghisoni, Angela Rosy Morgillo +1
This work introduces a novel NISQ-friendly procedure for estimating spectral gaps in quantum systems. By leveraging Adiabatic Thermalization, we are able to create the Spectral Gap…
A General Approach to Dropout in Quantum Neural Networks
Francesco Scala, Andrea Ceschini, Massimo Panella +1
In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A…
Deterministic entangling gates with nonlinear quantum photonic interferometers
Francesco Scala, Davide Nigro, Dario Gerace
The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements ou…
A Quantum Algorithmic Approach to Multiconfigurational Valence Bond Theory: Insights from Interpretable Circuit Design
Jakob S. Kottmann, Francesco Scala
Efficient ways to prepare fermionic ground states on quantum computers are in high demand and different techniques have been developed over the last years. Despite having a vast se…
Quantum variational learning for entanglement witnessing
Francesco Scala, Stefano Mangini, Chiara Macchiavello +2
Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means.…