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Analogue Optical Computer
Analogue Optical Computer
The Analog Optical Computer (AOC), built by our collaborators at Microsoft Research Cambridge, offers an alternative way of solving difficult problems by reimaging computer architecture in a fully analog domain.
Spatial Photonic Ising Machine
Spatial Photonic Ising Machine
Coupled laser networks are a potential candidate as the physical platform to realise the analogue computing algorithms that we study. The Spatial Photonic Ising machine is a state-of-the-art realization of an Ising-class optimizer.
Polaritonic condensates
Polaritonic condensates
Arrays of polariton condensates hold considerable promise for developing ultrafast analog simulators proficient in modeling spin ordering, synchronization in lattices, neural networks, and NP-hard optimization problem solvers.
Analogue Computing Platforms
Analogue Computing Platforms
There is a shift towards alternative hardware architectures and specialized devices in the quest for enhanced computing performance. Among these, universal quantum computing emerges as a solution to reduce algorithmic complexity in challenging tasks by leveraging entangled states. An alternative strategy involves transitioning from electron-based to photon-based systems within classical or quantum-coherent optical computing. The rationale for this shift is evident: photons travel at light speed, generate minimal heat, enable high-density configurations, and can effectively interact with matter to harness nonlinear effects. Optical technologies have become ubiquitous, evidenced by their presence in everyday applications like fibre optic communications and optical disc readers. Yet, a significant challenge in integrating photonics with CMOS architecture is the energy-intensive and time-consuming conversion of photons to electrons, posing a substantial bottleneck in hybrid systems.
Optimisation
Optimisation
Physics-inspired optimisation leverages physical systems' properties, particularly in optics and photonics, to tackle a wide array of complex optimisation problems, offering new possibilities beyond the capabilities of traditional computing methods. Physics-inspired optimisation encompasses a broad spectrum of solutions for various optimisation problems, including linear and nonlinear tasks in binary, integer, real, or complex variables, both with and without constraints. This extensive applicability enables its use across diverse sectors such as social sciences, finance, telecommunications, and biological and chemical industries.
Neural Networks
Neural Networks
Optics and coherent light-matter systems have the potential to implement artificial neural networks (ANNs), a technology increasingly recognized for its inherent parallelism and low energy consumption, making it a suitable candidate for industrial and fundamental applications. While ANNs are predominantly electronic-based, the shift towards photonic neural networks is gaining momentum. In photonic ANNs, mathematical operations are mapped onto optical propagation characteristics, manipulated by programmable linear optics and nonlinearity. Synaptic weights in these networks are scalar and represent the pairwise connections between neurons, with the layout of interconnections mirroring matrix-vector operations where neuron inputs are the dot products of outputs from connected neurons with assigned weights.
Quantum Systems
Quantum Systems
Quantum systems, with their inherent properties of superposition and entanglement, offer a revolutionary perspective in computational optimisation and neural networks.
Quantum optimisers harness the unique capabilities of quantum states to explore complex problem spaces more efficiently than classical systems. Quantum annealing, for example, exploits quantum tunnelling to traverse energy landscapes, potentially finding global minima more effectively in optimisation tasks. This approach is particularly advantageous for solving problems that are intractable for traditional computers, such as specific combinatorial optimisation and machine learning tasks.
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