51 citations · 100 across the 5 of their papers we have counts for
3 papers · 1 filter
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…
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…
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…