Quantum entanglement is an essential resource for quantum networks, thus generation and distribution of entanglement between remote network nodes are two important tasks faced by practical implementation of quantum networks. Given that the number and scale of real quantum network testbeds are still limited we use simulation to study entanglement generation and distribution, aiming at providing insight and guidance for quantum network construction. We extend the functionality of the open-source Simulator of QUantum Network Communication (SeQUeNCe) developed by our group to support representation of quantum state under Fock basis, and use it to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. Regarding entanglement distribution, based on the so-called continuous generation scheme, we propose an adaptive scheme that uses history request information to better guide the choice of randomly generated quantum links before receiving future requests. We demonstrate the concept and power of our adaptive scheme, and explore relevant quantum memory allocation scenarios through simulation.
Quantum entanglement is an essential resource for quantum networks, thus generation and distribution of entanglement between remote network nodes are two important tasks faced by practical implementation of quantum networks. Given that the number and scale of real quantum network testbeds are still limited we use simulation to study entanglement generation and distribution, aiming at providing insight and guidance for quantum network construction. We extend the functionality of the open-source Simulator of QUantum Network Communication (SeQUeNCe) developed by our group to support representation of quantum state under Fock basis, and use it to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories. Regarding entanglement distribution, based on the so-called continuous generation scheme, we propose an adaptive scheme that uses history request information to better guide the choice of randomly generated quantum links before receiving future requests. We demonstrate the concept and power of our adaptive scheme, and explore relevant quantum memory allocation scenarios through simulation.
Quantum entanglement is an essential resource for quantum networks. However, the generation of entanglement between physical devices at remote network nodes is a challenging task towards practical implementation of quantum networks. In this work, we use the open-source Simulator of QUantum Network Communication (SeQUeNCe), developed by our team, to simulate entanglement generation between two atomic frequency comb (AFC) absorptive quantum memories to be deployed on the Argonne-Chicago quantum network. We realize the representation of photonic quantum states within truncated Fock spaces in SeQUeNCe and build models for a spontaneous parametric down-conversion (SPDC) source, AFC absorptive quantum memories, and measurement devices with non-number-resolving photon detectors. Based on these developments, we observe varying fidelity with SPDC source mean photon number, and varying entanglement generation rate with both mean photon number and memory mode number. We also simulate tomographic reconstruction of the effective density matrix for the bipartite photonic states retrieved from quantum memories. Our work extends the usability of the SeQUeNCe simulator with new hardware modules and Fock state representation that will improve the simulation of near term quantum network hardware and protocols.
Quantum networks, which enable the transfer of quantum information across long distances, promise to provide exciting benefits and new possibilities in many areas including communication, computation, security, and metrology. These networks rely on entanglement between qubits at distant nodes to transmit information; however, creation of these quantum links is not dependent on the information to be transmitted. Researchers have explored schemes for continuous generation of entanglement, where network nodes may generate entanglement links before receiving user requests. In this paper we present an adaptive scheme that uses information from previous requests to better guide the choice of randomly generated quantum links before future requests are received. We analyze parameter spaces where such a scheme may provide benefit and observe an increase in performance of up to 75% over other continuous schemes on single-bottleneck and autonomous systems networks. We also test the scheme for other parameter choices and observe continued benefits of up to 95%. The power of our adaptive scheme on a randomized request queue is demonstrated on a single-bottleneck topology. We also explore quantum memory allocation scenarios, where a difference in latency performance implies the necessity of optimal allocation of resources for quantum networks.
Generating and distributing remote entangled pairs (EPs) is a primary function of quantum networks, as entanglement is the fundamental resource for key quantum network applications. A critical performance metric for quantum networks is the time-to-serve (TTS) for users’ EP requests, which is the time to distribute EPs between the requested nodes. Minimizing the TTS is essential given the limited qubit coherence time. In this paper, we study the Adaptive Continuous entanglement generation Protocol (ACP), which enables quantum network nodes to continuously generate EPs with their neighbors, while adaptively selecting the neighbors to optimize TTS. Meanwhile, entanglement purification is used to mitigate decoherence in pre-generated EPs prior to the arrival of user requests. We extend the SeQUeNCe simulator to fully implement ACP and conduct extensive simulations across various network scales. Our results show that ACP reduces TTS by up to 94% and increases entanglement fidelity by up to 0.05.
Entanglement-based networks (EBNs) enable general-purpose quantum communication by combining entanglement and its swapping in a sequence that addresses the challenges of achieving long-distance communication with high fidelity associated with quantum technologies. In this context, entanglement distribution refers to the process by which two nodes in a quantum network share an entangled state, serving as a fundamental resource for communication. In this paper, we study the performance of entanglement distribution mechanisms over a physical topology comprising end nodes and quantum switches, which are crucial for constructing large-scale links. To this end, we implemented a switch-based topology in NetSquid and conducted a series of simulation experiments to gain insight into practical and realistic quantum network engineering challenges. These challenges include, on the one hand, aspects related to quantum technology, such as memory technology, gate durations, and noise; and, on the other hand, factors associated with the distribution process, such as the number of switches, distances, purification, and error correction. All these factors significantly impact the end-to-end fidelity across a path, which supports communication between two quantum nodes. We use these experiments to derive some guidelines towards the design and configuration of future EBNs.
End-to-end entanglement distribution is a key capability of upcoming quantum networks, enabling applications like distributed quantum computing, quantum sensor networks, and secure communications. Hence, its realistic and efficient simulation is crucial for quantum network design and for assessing the ability of a network to run certain applications. This work provides tools to scale-up and improve the realism of entanglement generation simulations in quantum networks. This is achieved by deriving analytical results that directly return the success probability, the output state and corresponding fidelity of a selected entanglement generation protocol, while accounting for a variety of noise sources affecting the protocol. These results are then integrated and streamlined in an upgraded version of SeQUeNCe, one of the most popular quantum network simulators. The resulting simulator features increased scalability by reducing computation time by more than 60%, while allowing for a variety of realistic noise sources, including imperfect mode matching, dark counts, and imperfect memory initialization. The simulator is also benchmarked with real experimental data and is capable of replicating the average entanglement generation time and the final state fidelity of a selected experiment. Altogether, the results can enhance current quantum network simulation capabilities towards large-scale networks, paving the way for the future quantum internet.
Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. Our simulator consists of five modules: hardware models, entanglement management protocols, resource management, network management, and application. This framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. The simulation capabilities are illustrated in three use cases. We show the dependence of quantum network throughput on several key hardware parameters and study the impact of classical control message latency. We also investigate quantum memory usage efficiency in routers and demonstrate that redistributing memory according to anticipated load increases network capacity by 69.1% and throughput by 6.8%. We design SeQUeNCe to enable comparisons of alternative quantum network technologies, experiment planning, and validation and to aid with new protocol design. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.