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Advanced Quantum Technologies

Inside Back Cover: Toward Arbitrary Control of Lattice Interactions in Nonequilibrium Condensates (Adv. Quantum Technol. 2/2020)

Kirill P. Kalinin, Natalia G. Berloff

Published Feb 1, 2020• 0 citations
Publisher

Abstract

There is a growing interest in investigating new states of matter using out-of-equilibrium lattice spin models in two dimensions. However, a control of pairwise interactions in such systems has been elusive as due to their nonequilibrium nature they maintain nontrivial particle fluxes even at the steady state. In article number 1900065 Natalia Berloff and co-workers suggest how to overcome this problem and formulate a method for engineering reconfigurable networks of nonequilibrium condensates with control of individual pairwise interactions.

DOI
10.1002/qute.202070024

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There is a growing interest in investigating new states of matter using out-of-equilibrium lattice spin models in two dimensions. However, a control of pairwise interactions in such systems has been elusive as due to their nonequilibrium nature they maintain nontrivial particle fluxes even at the steady state. In article number 1900065 Natalia Berloff and co-workers suggest how to overcome this problem and formulate a method for engineering reconfigurable networks of nonequilibrium condensates with control of individual pairwise interactions.
There is a growing interest in investigating new states of matter using out-of-equilibrium lattice spin models in two dimensions. However, a control of pairwise interactions in such systems has been elusive as due to their nonequilibrium nature they maintain nontrivial particle fluxes even at the steady state. Here we suggest how to overcome this problem and formulate a method for engineering reconfigurable networks of nonequilibrium condensates with control of individual pairwise interactions. Representing spin by condensate phase, the effective two spin interactions are created with nonresonant pumping, are directed with dissipative channels, and are further controlled with dissipative gates. The dissipative barriers are used to block unwanted interactions between condensates. Together, spatial anisotropy of dissipation and pump profiles allow an effective control of sign and intensity of the coupling strength between any two neighboring sites independent of the rest of the spins, which we demonstrate with a two dimensional square lattice of polariton condensates. Experimental realisation of such fully-controllable networks offers great potential for an efficient analog Hamiltonian optimizer and for reservoir computing.
Abstract There is a growing interest in investigating new states of matter using out‐of‐equilibrium lattice spin models in two dimensions. However, a control of pairwise interactions in such systems has been elusive as due to their nonequilibrium nature they maintain nontrivial particle fluxes even at the steady state. Here it is suggested how to overcome this problem and formulate a method for engineering reconfigurable networks of nonequilibrium condensates with control of individual pairwise interactions. Representing spin by condensate phase, the effective two spin interactions are created with nonresonant pumping, are directed with dissipative channels, and are further controlled with dissipative gates. The dissipative barriers are used to block unwanted interactions between condensates. Together, spatial anisotropy of dissipation and pump profiles allow an effective control of sign and intensity of the coupling strength between any two neighbouring sites independent of the rest of the spins, which is demonstrated with a 2D square lattice of polariton condensates. Experimental realization of such fully controllable networks offers great potential for reservoir computing, modelling of the systems of coupled oscillators, simulation of the new state of matters, studying the phase transitions in large‐scale systems, and optimization.
In article number 2400603, Alexey Melnikov and co-workers present a method to enhance the generalization capability of quantum machine learning models through controllable noise regularization. The cover visualizes a quantum circuit in which quantum gates are interleaved with engineered noise channels of tunable strength λ. This structured injection of noise acts as an implicit regularizer, stabilizing the training process and improving the model's predictive performance on previously unseen data.