3 papers
quant-ph2026
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…
quant-ph2025
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…
quant-ph2024
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…