arXiv subjects

Tristan Austin

Publications and source records attributed to Tristan Austin.

2 recordsLinked to original sources

arXiv2026

Photonic Ising machines toward and beyond a million spins

Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.

physics.optics↗

arXiv2024

Hybrid Quantum-Classical Photonic Neural Networks

Neuromorphic (brain-inspired) photonics leverages photonic chips to accelerate artificial intelligence, offering high-speed and energy efficient solutions in RF communication, tensor processing, and data classification. However, the limited physical size of integrated photonic hardware constrains network complexity and computational capacity. In light of recent advances in photonic quantum technology, it is natural to utilize quantum exponential speedup to scale photonic neural network capabilities. Here we show a combination of classical network layers with trainable continuous variable quantum circuits yields hybrid networks with improved trainability and accuracy. On a classification task, hybrid networks achieve the same performance when benchmarked against fully classical networks that are twice the size. When the bit precision of the optimized networks is reduced through added noise, the hybrid networks still achieve greater accuracy when evaluated at state of the art bit precision. These hybrid quantum classical networks demonstrate a unique route to improve computational capacity of integrated photonic neural networks without increasing the physical network size.

quant-ph↗