26 citations · 27 across the 7 of their papers we have counts for
7 papers
Quantum Approaches to Urban Logistics: From Core QAOA to Clustered Scalability
F. Picariello, G. Turati, R. Antonelli +10
The Traveling Salesman Problem (TSP) is a fundamental challenge in combinatorial optimization, widely applied in logistics and transportation. As the size of TSP instances grows, t…
Hamiltonian Expressibility for Ansatz Selection in Variational Quantum Algorithms
Filippo Brozzi, Gloria Turati, Maurizio Ferrari Dacrema +2
In the context of Variational Quantum Algorithms (VQAs), selecting an appropriate ansatz is crucial for efficient problem-solving. Hamiltonian expressibility has been introduced as…
Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design
Gloria Turati, Simone Foderà, Riccardo Nembrini +2
As quantum computing continues to gain attention, there is growing interest in how classical machine learning can assist quantum workflows in practice. Automated circuit design, so…
An Empirical Analysis on the Effectiveness of the Variational Quantum Linear Solver
Gloria Turati, Alessia Marruzzo, Maurizio Ferrari Dacrema +1
Variational Quantum Algorithms (VQAs) have emerged as promising methods for tackling complex problems on near-term quantum devices. Among these algorithms, the Variational Quantum…
Reinforcement Learning for Variational Quantum Circuits Design
Simone Foderà, Gloria Turati, Riccardo Nembrini +2
Variational Quantum Algorithms have emerged as promising tools for solving optimization problems on quantum computers. These algorithms leverage a parametric quantum circuit called…
Benchmarking Adaptative Variational Quantum Algorithms on QUBO Instances
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
In recent years, Variational Quantum Algorithms (VQAs) have emerged as a promising approach for solving optimization problems on quantum computers in the NISQ era. However, one lim…
Feature Selection for Classification with QAOA
Gloria Turati, Maurizio Ferrari Dacrema, Paolo Cremonesi
Feature selection is of great importance in Machine Learning, where it can be used to reduce the dimensionality of classification, ranking and prediction problems. The removal of r…