Optical Neural Computers

Experimental Demonstrations of Optical Neural Computers

377 We describe two expriments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-Iike learning algorithm is implemented with photorefractive holography.

Bimodal optical computers.

Analog optical solutions of numerical problems tend to be fast, simple, and inaccurate. Digital optical or electronic solutions to the same problems tend to be slower, harder, and more accurate. In circumstances outlined here, hybrid analog-digital systems can be built which give the accuracy of digital solutions with intermediate degrees of speed and simplicity. Because at any instant these pr...

Concepts for numerical optical computers

1. Background 2. Residue number system 2.1. Encoding 2.2. Decoding 2.3. Scaling 3. Basis for numerical methods 4. Processor design concepts 4.1. Cyclic phase implementation 4.2. Mapping with beam position control 4.3. Multipurpose arithmetic module 5. Extended computing applications 5.1. Polynomial evaluation 5.2. Matrix multiplication and transforms 6. Concluding remarks 7. References Abstract...

Controlling computers with neural signals.

Noise thresholds for optical quantum computers.

In this Letter we numerically investigate the fault-tolerant threshold for optical cluster-state quantum computing. We allow both photon loss noise and depolarizing noise (as a general proxy for all local noise), and obtain a threshold region of allowed pairs of values for the two types of noise. Roughly speaking, our results show that scalable optical quantum computing is possible for photon l...