Curator's Take
AI Commentary
This review consolidates the rapidly expanding toolbox of photonic quantum processors, clarifying how diverse encoding schemes and gate‑ versus cluster‑state approaches translate into concrete performance metrics across experiments. By juxtaposing universal architectures with near‑term non‑universal tasks such as boson sampling, variational eigensolvers and quantum machine‑learning models, it highlights why photons are emerging as a leading platform for scalable, low‑noise quantum advantage in both algorithmic research and practical applications. The comprehensive tables of experimental results give researchers a valuable benchmark for assessing progress and identifying the most promising pathways toward fault‑tolerant optical quantum computers.
— Mark Eatherly
Summary
Under the label of optical quantum computing, there are a variety of protocols and experiments that use the quantum properties of light to achieve a computational advantage over classical computing machines. In this review, we describe some of the main implementations, which differ in the type of encoding and in how the computation is performed, whether using gates or cluster states. For each protocol, we describe advantages and challenges with an overview of the experimental results obtained, summarized in tables. Because of the great relevance achieved in this field, there is a section dedicated to non-universal quantum computation with photons, where boson sampling, variational quantum eigensolvers, and quantum machine learning applications are described. The aim is to give the reader the broadest overview of the applications where photons and their quantum properties play a key role in computation.