Advances in Optical Computing

Optical computing is gaining prominence in meeting the performance demands of both classical and quantum applications. In classical computing, optics enhances artificial intelligence (AI) and machine learning (ML), addressing the need for fast and energy-efficient computing required in AI applications. In quantum applications, integrated photonics emerges as a promising candidate for quantum computing due to the remarkable resistance of photons to decoherence, the preservation of quantum states at room temperature, and efficient, low-loss photon transmission over extended distances. Furthermore, optics and photonics offer promising solutions for network and data security in the post-quantum era. This research topic encompasses classical optical computing, quantum optical computing, and optics for security.

The objective of this Research Topic is to encompass recent advancements, accomplishments, and techniques in optical computing. This includes optical computing employing integrated photonics on platforms such as Silicon photonics, III-V, and other innovative materials.
Furthermore, the scope of this research topic encompasses various aspects of photonic quantum computing. This includes the generation of entangled photons, the development of scalable photonic quantum gates and circuits, innovative methods for encoding information on photons, novel techniques for single-photon detection, recent advancements in optical packaging for quantum optics, and integrated photonics for security and photonic physical unclonable functions.

“Advanced Optical Computing” publishes research and review articles covering a broad spectrum of topics, which include, but are not limited to:

- Optical computing for ML and AI
- AI and inverse design for photonic integrated circuits
- Photonic quantum computing
- Integrated optics for computing
- Optical accelerators
- Photonic physical unclonable functions
- Programmable photonics