Global and local dissipation in a quantum map

Alireza Nourmandipour, Mohammad Kazem Tavassoly, Stefano Mancini

We consider a model of two qubits dissipating into both local and global environments (generally at non-zero temperatures), with the possibility of interpolating between purely local dissipation and purely global one. The corresponding dissipative dynamical map is characterized in terms of its Kraus operators focusing on the stationary regime. We then determine conditions under which entanglement can be induced by the action of such a map. It results (rather counterintuitively) that in order to have entanglement in the presence of local environment, this latter must be at nonzero temperature.

Hilda A. Cerdeira, Ramakrishna Ramaswamy

Sergey N. Filippov, Alexey A. Melnikov, Mário Ziman

We study the dynamics of the entanglement structure of a multipartite system experiencing a dissipative evolution. We characterize the processes leading to a particular form of output-system entanglement and provide a recipe for their identification via concatenations of particular linear maps with entanglement-breaking operations. We illustrate the applicability of our approach by considering local and global depolarizing noises acting on general multiqubit states. A difference in the typical entanglement behavior of systems subjected to these noises is observed: the originally genuine entanglement dissociates by splitting off particles one by one in the case of local noise, whereas intermediate stages of entanglement clustering are present in the case of global noise. We also analyze the definitive phase of evolution when the annihilation of the entanglement compound finally takes place.

Robert Graham

Zhang, Jincheng

This paper proposes a novel heuristic optimization algorithm that directly draws upon the core principles of quantum thermodynamics, including coherent evolution, thermal dissipation, quantum fluctuations, entropy generation, and the work-information coupling mechanism. Traditional optimization algorithms often get trapped in local optima when dealing with complex non-convex problems, lacking global exploration capabilities and dynamic diversity control. The proposed algorithm constructs a density matrix to represent the candidate solution set, maps the objective function to a dynamic energy level Hamiltonian, and achieves an organic combination of global exploration and local accelerated convergence through alternating updates using coherent evolution, thermal dissipation, quantum fluctuation operators, and work-information coupling operators. This paper elaborates on the algorithm's mathematical model, update mechanism, entropy-energy control strategy, and pseudocode implementation, providing a complete theoretical framework and a new approach to complex optimization problems.