Abstract: The specification describes a method of reconfiguring particles in a grid of particle traps from an initial configuration to a target configuration, the grid of particle traps having target particle traps and surplus particle traps, the particles being movable along rows of the grid, or along columns of the grid, the rows being transversal to the columns.

Abstract: Analog optical neural networks (ONNs) can reduce the energy of matrix-vector multiplication in neural network inference below that of digital electronics. However, realizing this promise remains challenging due to digital-to-analog (DAC) conversion—even at low bit precisions b, encoding 2b levels of digital weights and inputs into the analog domain involves power-hungry electronics. Faced with similar challenges, telecommunications uses complex-valued Quadrature-Amplitude Modulation (QAM). QAM maximally exploits the complex amplitude to provide a quadratic 0(N2)?0(N) energy saving over intensity-only modulation. QAMNet, an ONN with lower energy consumption than existing ONNs, uses the complex nature of the amplitude of light with QAM. QAMNet accelerates complex-valued deep neural networks with accuracies indistinguishable from digital hardware.

Abstract: The specification describes a method of reconfiguring particles in a grid of particle traps from an initial configuration to a target configuration, the grid of particle traps having target particle traps and surplus particle traps, the particles being movable along rows of the grid, or along columns of the grid, the rows being transversal to the columns.