424 citations · 855 across the 27 of their papers we have counts for
9 papers · 1 filter
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…
Minor Embedding for Quantum Annealing with Reinforcement Learning
Riccardo Nembrini, Maurizio Ferrari Dacrema, Paolo Cremonesi
Quantum Annealing (QA) is a quantum computing paradigm for solving combinatorial optimization problems formulated as Quadratic Unconstrained Binary Optimization (QUBO) problems. An…
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…
Comment on arXiv:2307.08384 "Efficient Quantum State Preparation with Walsh Series"
Riccardo Pellini, Maurizio Ferrari Dacrema
In this paper, we discuss the Walsh Series Loader (WSL) algorithm, proposed by. In particular, we observe that the paper does not describe how to implement the term of order zero o…
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…
Adaptive Learning for Quantum Linear Regression
Costantino Carugno, Maurizio Ferrari Dacrema, Paolo Cremonesi
The recent availability of quantum annealers as cloud-based services has enabled new ways to handle machine learning problems, and several relevant algorithms have been adapted to…
Analyzing the Effectiveness of Quantum Annealing with Meta-Learning
Riccardo Pellini, Maurizio Ferrari Dacrema
The field of Quantum Computing has gathered significant popularity in recent years and a large number of papers have studied its effectiveness in tackling many tasks. We focus in p…
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…