5 papers
Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design
Gloria Turati, Simone FoderÃ, 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…
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…
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 FoderÃ, 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…