Lectures on our Come Lake school on Machine Learning and Photonics are now available on Youtube!
Stay tuned for the new edition next years (hopefully on site and in person!)
Many developments in science and engineering depend on tackling complex optimizations on large scales. The challenge motivates an intense search for specific computing hardware that takes advantage of quantum features, nonlinear dynamics, or photonics. A paradigmatic optimization problem is to find low-energy states in classical spin systems with fully random interactions. To date, no alternative computing platform can address such spin-glass problems on a large scale. Here, we propose and realize an optical scalable spin-glass simulator based on spatial light modulation and multiple light scattering. By tailoring optical transmission through a disordered medium, we optically accelerate the computation of the ground state of large spin networks with all-to-all random couplings. Scaling of the operation time with the problem size demonstrates an optical advantage over conventional computing. Our results highlight optical vector-matrix multiplication as a tool for spin-glass problems and provide a general route toward large-scale computing that exploits speed, parallelism, and coherence of light.
We use neural networks to represent the characteristic function of many-body Gaussian states in the quantum phase space. By a pullback mechanism, we model transformations due to unitary operators as linear layers that can be cascaded to simulate complex multi-particle processes. We use the layered neural networks for non-classical light propagation in random interferometers, and compute boson pattern probabilities by automatic differentiation. We also demonstrate that multi-particle events in Gaussian boson sampling can be optimized by a proper design and training of the neural network weights. The results are potentially useful to the creation of new sources and complex circuits for quantum technologies.
We study the Aharonov-Bohm caging effect in a one-dimensional lattice of theta-shaped units defining a chain of interconnected plaquettes, each one threaded by two synthetic flux lines. In the proposed system, light trapping results from the destructive interference of waves propagating along three arms, this implies that the caging effect is tunable and it can be controlled by changing the tunnel couplings J. These features reflect on the diffraction pattern allowing to establish a clear connection between the lattice topology and the resulting AB interference.