Linear Optics to Scalable Photonic Quantum Computing

TL;DR

Recent progress in integrated photonic platforms, high-efficiency photon sources, and error correction schemes have significantly advanced scalable, fault-tolerant photonic quantum computing, demonstrating practical implementations and increased computational capabilities.

Contribution

This paper reviews recent experimental and technological advances that enable scalable and fault-tolerant photonic quantum computing, highlighting new integrated platforms, sources, detectors, and encoding methods.

Findings

Coupling efficiencies above 90% achieved in integrated photonics

Photon indistinguishability exceeding 99% demonstrated

Boson sampling with over 100 photons performed

Abstract

Recent advancements in quantum photonics have driven significant progress in photonic quantum computing (PQC), addressing challenges in scalability, efficiency, and fault tolerance. Experimental efforts have focused on integrated photonic platforms utilizing materials such as silicon photonics and lithium niobate to enhance performance. Parameters like photon loss rates, coupling efficiencies, and fidelities have been pivotal, with state-of-the-art systems achieving coupling efficiencies above 90% and photon indistinguishability exceeding 99%. Quantum error correction schemes have reduced logical error rates to below $10^{-3}$, marking a step toward fault-tolerant PQC. Photon generation has also advanced with deterministic sources, such as quantum dots, achieving brightness levels exceeding $10^6$ photon pairs/s/mW and time-bin encoding enabling scalable entanglement. Heralded single-photon sources now exhibit purities above 99%, driven by innovations in fabrication techniques. High-efficiency photon detectors, such as superconducting nanowire single-photon detectors (SNSPDs), have demonstrated detection efficiencies exceeding 98%, dark count rates below 1 Hz, and timing jitters as low as 15 ps, ensuring precise photon counting and manipulation. Moreover, demonstrations of boson sampling with over 100 photons underscore the growing computational power of photonic systems, surpassing classical limits. The integration of machine learning has optimized photonic circuit design, while frequency multiplexing and time-bin encoding have increased system scalability. Together, these advances bridge the gap between theoretical potential and practical implementation, positioning PQC as a transformative technology for computing, communication, and quantum sensing.