Recirculating Quantum Photonic Networks for Fast Deterministic Quantum Information Processing
Emil Grovn, Matias Bundgaard-Nielsen, Jesper M{\o}rk, Dirk Englund, and Mikkel Heuck

TL;DR
This paper introduces a recirculating quantum photonic network architecture that reduces processing time and hardware requirements for quantum information tasks, enabling faster and more efficient photonic quantum computing.
Contribution
It proposes a novel recirculating network architecture that minimizes processing duration and enhances efficiency for quantum photonic operations, surpassing traditional device-focused approaches.
Findings
Faster multi-qubit gate operations through simultaneous processing.
Achieved up to seven-fold speedups in photon loss correction.
Significantly improved hardware efficiency over existing architectures.
Abstract
A fundamental challenge in photonics-based deterministic quantum information processing is to realize key transformations on time scales shorter than those of detrimental decoherence and loss mechanisms. This challenge has been addressed through device-focused approaches that aim to increase nonlinear interactions relative to decoherence rates. In this work, we adopt a complementary architecture-focused approach by proposing a recirculating quantum photonic network (RQPN) that minimizes the duration of quantum information processing tasks, thereby reducing the requirements on nonlinear interaction rates. The RQPN consists of a network of all-to-all connected nonlinear cavities with dynamically controlled waveguide couplings, and it processes information by capturing a photonic input state, recirculating photons between the cavities, and releasing a photonic output state. We demonstrate…
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Taxonomy
TopicsQuantum Information and Cryptography · Neural Networks and Reservoir Computing · Quantum Computing Algorithms and Architecture
