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.
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.
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 network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only viable approach is teleportation of quantum states, which requires a prior distribution of entangled pairs (EPs) of qubits. Establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. To reduce EP generation latency, prior works have looked at selection of efficient entanglement-routing paths and simultaneous use of multiple such paths for EP generation.In this paper, we propose and investigate a complementary technique to reduce EP generation latency—to pre-distribute EPs over certain (pre-determined) pairs of network nodes; these pre-distributed EPs can then be used to generate EPs for the requested pairs, when needed, with lower generation latency. For such a pre-distribution approach to be most effective, we need to address an optimization problem of selection of node-pairs where the EPs should be pre-distributed to minimize the generation latency of expected EP requests, under a given cost constraint. In this paper, we appropriately formulate the above optimization problem and design two efficient algorithms, one of which is a greedy approach based on an approximation algorithm for a special case. Via extensive evaluations over the NetSquid simulator [1], we demonstrate the effectiveness of our approach and developed techniques; we show that our developed algorithms outperform a naive approach by up to an order of magnitude.
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.
Long-distance quantum communication in quantum networks faces significant challenges due to the constraints imposed by the no-cloning theorem. Most existing quantum communication protocols rely on the a priori distribution of entanglement pairs (EPs), a process known to incur considerable latency due to its stochastic nature. In this work, we consider the problem of minimizing the latency of establishing an EP across a pair of nodes in a quantum network. While prior research has primarily focused on minimizing the expected generation latency by selecting static entanglement routes and/or swapping trees in advance, our approach considers a real-time adaptive strategy-wherein the order of entanglement-swapping operations (hence, the swapping tree used) is progressively determined at runtime based on the runtime success/failure of the stochastic events. In this context, we present a greedy algorithm that iteratively determines the best route and/or entanglement-swapping operation to perform at each stage based on the current network. We evaluate our schemes on randomly generated networks and observe a reduction in latency of up to 40% from the optimal offline approach.
Quantum network communication is challenging, as the no-cloning theorem in the quantum regime makes many classical techniques inapplicable; in particular, the direct transmission of qubit states over long distances is infeasible due to unrecoverable errors. For the long-distance communication of unknown quantum states, the only viable communication approach (assuming local operations and classical communications) is the teleportation of quantum states, which requires a prior distribution of the entangled pairs (EPs) of qubits. The establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. The focus of our work is to develop efficient techniques that minimize EP generation latency. Prior works have focused on selecting entanglementpaths; in contrast, we selectentanglement swapping trees—a more accurate representation of the entanglement generation structure. We develop a dynamic programming algorithm to select an optimal swapping tree for a single pair of nodes, under the given capacity and fidelity constraints. For the general setting, we develop an efficient iterative algorithm to compute a set of swapping trees. We present simulation results, which show that our solutions outperform the prior approaches by an order of magnitude and are viable for long-distance entanglement generation.
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.