My personal quantum paper library

Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch

Python library for running large-scale computations on LightOn's OPUs

ML benchmarks performance featuring LightOn's Optical Processing Unit (OPU) vs CPU and GPU.

Code to perform Model-Free Episodic Control using Aurora OPUs

Implementation of a compact optical neural network SqueezeLight based on multi-operand micro-rings, DATE 2021

Repository of the package Fast Wave

Double Trouble in the Double Descent Curve with Optical Processing Units.

Dual adaptive training of photonic neural networks

This is a transaction-level, event-driven python-based simulator for evaluation of stochastic computing based optical neural network accelerators for various quantized Convolutional Neural Network models. This can generate metrics of an accelerator like latency, area, energy consumption and power

Linear Optical Circuits Optimisation using Perceval Quandela and Variational Algorithm (VQE)- MIT iQuHack- Quantum Computing Project

OPICS : An S-parameter based photonic circuit simulator

Photonic-inspired reservoir computing for signal classification

Automated Probe Station for testing Photonic Integrated Circuits

LAGC is a CPU-optimized Python library for quantum photonics. It features a Loss-Aware Graph Compiler for large-scale MBQC. Using XOR-based graph surgery and recursive tensor slicing, LAGC bypasses GPU VRAM limits to simulate thousands of qubits on standard RAM. Perfect for photon loss recovery and high-fidelity circuit verification at scale.

This is a simulation of the HOM Effect done using Strawberry Fields