Tootfinder

Optimizing quantum sensing networks via genetic algorithms and deep learning
Asghar Ullah, \"Ozg\"ur E. M\"ustecapl{\i}o\u{g}lu, Matteo G. A. Paris
https://arxiv.org/abs/2507.17460

Optimizing quantum sensing networks via genetic algorithms and deep learning
We investigate the optimization of graph topologies for quantum sensing networks designed to estimate weak magnetic fields. The sensors are modeled as spin systems governed by a transverse-field Ising Hamiltonian in thermal equilibrium at low temperatures. Using a genetic algorithm (GA), we evolve network topologies to maximize a perturbative spectral sensitivity measure, which serves as the fitness function for the GA. For the best-performing graphs, we compute the corresponding quantum Fisher…

Dynamics of Quantum Analogs of Classical Impact Oscillators
Arnab Acharya, Titir Mukherjee, Deepshikha Singh, Soumitro Banerjee
https://arxiv.org/abs/2509.12835 https://

Dynamics of Quantum Analogs of Classical Impact Oscillators
This paper investigates the dynamics of quantum analogs of classical impact oscillators to explore how complex nonlinear behaviors manifest in quantum systems. While classical impact oscillators exhibit chaos and bifurcations, quantum systems, governed by linear equations, appear to forbid such dynamics. Through simulations of unforced, forced, and dissipative quantum oscillators, we uncover quasiperiodicity, strange nonchaotic dynamics, and even chaos in the presence of dissipation. Using entr…