Optical Algorithm for Derivative of Real-Valued Functions
arXiv:2408.04710 · doi:10.1016/j.optlastec.2024.112137
Abstract
The derivation of a function is a fundamental tool for solving problems in calculus. Consequently, the motivations for investigating physical systems capable of performing this task are numerous. Furthermore, the potential to develop an optical computer to replace conventional computers has led us to create an optical algorithm and propose an experimental setup for implementing the derivative of one-dimensional real-valued functions using a paraxial and monochromatic laser beam. To complement the differentiation algorithm, we have experimentally implemented a novel optical algorithm that can transfer a two-dimensional phase-encoded function to the intensity profile of a light beam. Additionally, we demonstrate how to implement the n-th derivative of functions encoded in the phase of the transverse profile of photons.
7 Pages, 9 figures
References in corpus (10)
- Photonics for artificial intelligence and neuromorphic computing
- Noise tailoring for scalable quantum computation via randomized compiling
- The physics of optical computing
- Generalized spatial differentiation from spin Hall effect of light
- Distributed Quantum Computation Based-on Small Quantum Registers
- A silicon photonic modulator neuron
- First-order optical spatial differentiator based on a guided-mode resonant grating
- Experimental investigation of quantum key distribution with position and momentum of photon pairs
- Scalability of all-optical neural networks based on spatial light modulators
- Optical integration of a real-valued function by measurement of a Stokes Parameter