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. Despite these advancements, there remain significant gaps in the field, such as the need for scalable and efficient photonic quantum gates, improved methods for single-photon detection, and the development of robust photonic physical unclonable functions. Ongoing debates and research are focused on optimizing material platforms like Silicon photonics and III-V materials, and integrating these technologies into practical computing systems.

This Research Topic aims 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. Additionally, the research will explore various aspects of photonic quantum computing, including 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, and advancements in optical packaging for quantum optics. The objective is to provide a comprehensive overview of the current state of optical computing and identify areas where further investigation is needed.

To gather further insights in the field of optical computing, we welcome articles addressing, but not limited to, the following themes:
- 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

Keywords: Optical computing, Quantum computing, Quantum optics, Integrated optics, Integrated photonics, Optical accelerators, Machine Learning, Artificial intelligence

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.