Quantum Photonics and Neural Networks Generate Large Entangled Photon Clusters

The development of scalable quantum technologies hinges on the reliable creation and manipulation of entangled photons – a fundamental requirement for quantum computing and communication. Researchers are now demonstrating a novel approach to generating large, complex networks of these entangled particles, termed ‘cluster states’, utilising a recurrent photonic neural network. This architecture circumvents limitations inherent in existing methods, offering a pathway towards arbitrarily large cluster states constrained only by unavoidable signal loss. Jacob Ewaniuk, Bhavin J. Shastri, and Nir Rotenberg, from the Centre for Nanophotonics at Queen’s University, alongside Shastri’s affiliation with the Vector Institute, detail their findings in a paper entitled ‘Large-Scale Tree-Type Photonic Cluster State Generation with Recurrent Quantum Photonic Neural Networks’.

Researchers have demonstrated a novel architecture utilising recurrent photonic neural networks (QPNNs) to generate large, multi-photon entangled cluster states – a crucial resource for advanced quantum technologies. This approach addresses key limitations of existing methods for creating these states, offering a potentially pragmatic route towards practical implementation of quantum communication and computation.

Cluster states are complex quantum correlations between multiple photons, essential for measurement-based quantum computation and long-distance quantum communication. Traditional methods for generating these states rely on precise control of individual photonic components, a demanding task susceptible to imperfections. QPNNs, however, learn the necessary operations – photon routing and entanglement generation – adapting to and compensating for system imperfections. This learning-based approach enhances robustness and scalability.

The core of the system is a recurrent neural network implemented using photons as information carriers. Recurrence allows the network to maintain a ‘memory’ of previous states, crucial for complex operations. Unlike classical neural networks which use electronic signals, QPNNs utilise the quantum properties of photons to perform computations.

Simulations indicate the potential to generate cluster states comprising 60 photons using currently available photonic technology. Projections suggest scalability to hundreds of photons with modest improvements in component loss – the unavoidable attenuation of photons as they travel through optical components. Photon loss fundamentally limits the performance of all quantum communication protocols.

Researchers modelled a one-way quantum repeater – a device that extends the range of quantum communication – utilising these generated cluster states. This analysis establishes performance requirements for a global quantum network and demonstrates the potential of QPNNs to facilitate its realisation. The system’s performance is primarily constrained by photon loss, highlighting the need for materials and fabrication techniques that minimise this effect.

Future research will focus on optimising the QPNN architecture and exploring new materials to reduce photon loss. The integration of quantum error correction codes will also be investigated to mitigate the effects of noise and imperfections. Beyond quantum communication, researchers plan to explore the application of QPNNs to other quantum information processing tasks, including quantum computation and quantum sensing.