Photonic circuits, specialised low-power processors, are emerging as one of the most promising technologies for accelerating the execution of complex algorithms in the fields of machine learning and scientific computing, while maintaining low heat dissipation.

The success of simulating quantum systems and implementing quantum-inspired simulation algorithms on photonic units suggests the potential of these accelerators to advance computing capabilities in the fields of computational chemistry and materials science.

The aim of this project is to integrate photonic technologies with neural and tensor networks, pushing back the limits of quantum simulations and classical devices. This is a promising direction for the future of hardware-accelerated, specialised algorithmic innovation.

This research will focus on adapting algorithms to photonic devices, optimising energy consumption and developing new algorithms inspired by the specificities of hardware.