Photonic Network Memories: Accurate Performance Modelling with Channel Formalism.

The reliable storage and retrieval of quantum information represents a significant challenge in the development of quantum communication networks. Atomic ensemble quantum memories, which utilise the collective properties of atoms to store quantum states carried by photons, offer a promising solution; yet, accurately predicting their performance in realistic network conditions remains complex. Researchers at the German Aerospace Centre (DLR), alongside colleagues from Technische Universität Berlin, the Einstein Centre Digital Future and AQLS UG, address this need with a novel modelling framework.

Elizabeth Jane Robertson, Benjamin Maaß, Konrad Tschernig and Janik Wolters detail their work in “A digital twin of atomic ensemble quantum memories”, presenting a simulation environment that incorporates the inherent losses and noise present in physical devices, utilising the channel formalism – a mathematical framework describing the effects of noise on quantum systems – and a Kraus matrix representation to model memory performance. This digital twin demonstrates that it facilitates the evaluation of memory-assisted quantum communication protocols and offers a readily adaptable platform for assessing diverse memory implementations within existing network simulations.

Researchers have developed a comprehensive framework for modelling ensemble-based atomic memories, addressing a notable deficiency in current software which often neglects crucial considerations of loss and noise inherent in physical devices. This innovative approach utilises the channel formalism, a mathematical technique that describes the evolution of quantum states, to provide a more accurate estimation of memory performance —a critical factor for deployment within photonic networks. Photonic networks utilise photons to transmit quantum information, and atomic memories are essential for storing this information temporarily to overcome distance limitations.

The team constructs a Kraus matrix representation of several state-of-the-art, experimentally realised memories. A Kraus matrix describes how a quantum state evolves through a quantum channel, detailing the transformation of input states into output states, and accounting for both coherent, reversible processes and incoherent, irreversible processes. This representation facilitates the calculation of key performance metrics, including storage efficiency—the proportion of quantum information successfully stored—and fidelity, a measure of how closely the retrieved quantum state matches the original. Crucially, the model accurately captures the impact of decoherence —the loss of quantum information due to interaction with the environment —and loss mechanisms on stored quantum information.

Researchers demonstrate the framework’s utility by implementing a memory-assisted token protocol within the digital twin model. A token protocol is a method for establishing secure communication, and the digital twin serves as a virtual replica of the physical system. This simulation assesses the use of quantum memories to extend the range of quantum communication. Results indicate the model effectively predicts protocol performance under various noise conditions, accurately reflecting the interplay between memory characteristics and protocol performance. The digital twin model is designed for extensibility, readily accommodating different memory implementations and variations in experimental parameters, and exhibits compatibility with existing frameworks used for evaluating experimental photonic networks.

This approach offers a significant advantage over traditional simulation methods, providing a more accurate and comprehensive assessment of quantum memory performance. It enables researchers to identify and address potential limitations in real-world devices. Researchers plan to extend this work by exploring more complex memory architectures and noise models, further refining the accuracy and predictive power of the framework. They also intend to investigate the application of this framework to other quantum communication protocols and technologies, broadening its impact on the field. Future research will focus on developing efficient algorithms for simulating quantum memories and exploring the potential for using machine learning techniques to optimise memory performance.