3 papers
physics.comp-ph2026
Parallelized contraction of tensor trains or matrix product operators
Simone FoderÃ, Marc K. Ritter, Hiroshi Shinaoka +1
Tensor Trains (TT), also known as Matrix Product States (MPS) and Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and oper…
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-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…