A simple scheme for universal linear optics quantum computing with constant experimental complexity using fiber-loops

Abstract

Recently, Motes, Gilchrist, Dowling & Rohde [Phys. Rev. Lett. 113, 120501 (2014)] presented a scheme for photonic boson-sampling using a fiber-loop architecture. Here we show that the same architecture can be modified to implement full, universal linear optics quantum computing, in various incarnations. The scheme employs two embedded fiber-loops, a single push-button photon source, three dynamically controlled beamsplitters, and a single time-resolved photo-detector. The architecture has only a single point of interference, and thus may be significantly easier to align than other schemes. The experimental complexity of the scheme is constant, irrespective of the size of the computation, limited only by fiber lengths and their respective loss rates.

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Cited by in corpus (18)

• Perspective: Toward large-scale fault-tolerant universal photonic quantum computing
• Scalable performance in solid-state single-photon sources
• Sequential generation of linear cluster states from a single photon emitter
• Universal Quantum Computing with Measurement-Induced Continuous-Variable Gate Sequence in a Loop-Based Architecture
• Deterministic photonic quantum computation in a synthetic time dimension
• Multiplexed single-photon state preparation using a fibre-loop architecture
• Physical-depth architectural requirements for generating universal photonic cluster states
• Time-Domain Universal Linear-Optical Operations for Universal Quantum Information Processing
• Artificial coherent states of light by multi-photon interference in a single-photon stream
• Simple source for large linear cluster photonic states
• Implementing Scalable Boson Sampling with Time-Bin Encoding: Analysis of Loss, Mode Mismatch, and Time Jitter
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• Bosonic Indistinguishability-Dependent Contextuality
• Resource-efficient universal photonic processor based on time-multiplexed hybrid architectures
• Training continuously-coupled reconfigurable photonic chips with quantum machine learning