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- W2799143845 abstract "Recently several gain-dissipative platforms based on the network of optical parametric oscillators, lasers and various non-equilibrium Bose-Einstein condensates have been proposed as analog Hamiltonian simulators for solving large-scale hard optimisation problems. The parameters of such problems depend on the node occupancies that are not a priory known, which limits the applicability of the gain-dissipative simulators to the classes of problems easily solvable by classical computations. We show how to overcome this difficulty and formulate algorithms for solving the NP-hard large-scale optimisation problems such as constant modulus continuous quadratic optimisation for any general matrix. We show that to solve such problems any gain-dissipative simulator has to implement a feedback mechanism for the dynamical adjustment of the gain, so that occupancy of each node is the same. Based on the principle of operation of such simulators we propose a novel class of classical gain-dissipative algorithms and show its advantage in comparison with classical algorithms. The estimates of the time operation of the physical implementation of the gain-dissipative simulators for large matrices show the speed-up of the several orders of magnitude in comparison with classical computations." @default.
- W2799143845 created "2018-05-07" @default.
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- W2799143845 date "2018-04-24" @default.
- W2799143845 modified "2023-09-27" @default.
- W2799143845 title "Gain-dissipative simulators for large-scale hard classical optimisation" @default.
- W2799143845 hasPublicationYear "2018" @default.
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