Communication Networks have limited resources like bandwidth and battery power.Hence efficient node scheduling protocols and communication protocols are needed to maximize the network throughput.In this paper, we discussed an optimal data aggregation scheduling problem for quantum networks.To optimize the utilization of the network bandwidth much research is done to find schedules with minimum frame length.In this paper we proposed a method for finding efficient quantum node scheduling.The schedule contains a set of slots where each slot is allotted to one or more quantum nodes for transmitting at the same time.Adjacent Quantum nodes can create entanglement for transmitting the data.The quantum nodes entangles with its neighbor quantum nodes during the slot allotted to them.Loss and distortion are the two major problems in free space channels transmission.The optical noise produced when two or more nodes are transmitting data concurrently interferes with the actual data.Hence the optical signal to noise ratio OSNR ie Square root of the signal power divided the square root of the noise power is used as a threshold on concurrent transmitters.In this proposed method we used OSNR for finding a set of nodes that can entangle concurrently for aggregating the data to the base station.Entanglement rate of each node is used as a metric for finding a schedule that maximizes the quantum network throughput.We derive a Power Optimized Maximal Link Entanglement Rate based Scheduling Method which maximizes the quantum network throughput and also increases the network life time.
Communication Networks have limited resources like bandwidth and battery power.Hence efficient node scheduling protocols and communication protocols are needed to maximize the network throughput.In this paper, we discussed an optimal data aggregation scheduling problem for quantum networks.To optimize the utilization of the network bandwidth much research is done to find schedules with minimum frame length.In this paper we proposed a method for finding efficient quantum node scheduling.The schedule contains a set of slots where each slot is allotted to one or more quantum nodes for transmitting at the same time.Adjacent Quantum nodes can create entanglement for transmitting the data.The quantum nodes entangles with its neighbor quantum nodes during the slot allotted to them.Loss and distortion are the two major problems in free space channels transmission.The optical noise produced when two or more nodes are transmitting data concurrently interferes with the actual data.Hence the optical signal to noise ratio OSNR ie Square root of the signal power divided the square root of the noise power is used as a threshold on concurrent transmitters.In this proposed method we used OSNR for finding a set of nodes that can entangle concurrently for aggregating the data to the base station.Entanglement rate of each node is used as a metric for finding a schedule that maximizes the quantum network throughput.We derive a Power Optimized Maximal Link Entanglement Rate based Scheduling Method which maximizes the quantum network throughput and also increases the network life time.
Quantum Entanglement and Quantum swapping are major research areas nowadays.Remote quantum entanglement is used in many applications like secure communication, secret sharing, data aggregation, and precision sensing.In data aggregation applications, every sensor node captures data and communicates to the central node.Efficient Data aggregation depends on whether the local information or global quantum network information is used for constructing the aggregation schedules.In addition, Quantum networks suffer from lossy optical links and with limited resources such as quantum memories, edge capacities.Computation of optimal schedules deals with large quantities of data and complex time-consuming calculations.However, the quantum memories cannot hold the qubits for a longer time as the stored qubit completely decoheres an infinite amount of time.Hence, there is a necessity for finding new data aggregation scheduling protocols, which use optimal channel capacity and optimal size of memory for improving the network throughput.This paper uses a reinforcement-learning technique that considers entanglement pairing and swapping the success probability of nodes with their neighbors while finding an optimal scheduling policy.The proposed method uses local network information for constructing optimal data aggregation schedules by prior sharing the maximally entangled qubit pairs between the nodes through optimal usage of the processes, channel capacity, and memory at the intermediate nodes.Experiments show that our proposed method can maximize the network throughput.
Data Aggregation Scheduling in Ubiquitous sensor networks is a major research interest for many researchers with the objective of reducing energy consumption and maximizing the network lifetime.Also the capacity of a channel for transmitting quantum states called the quantum capacity should be maximized,The sensor nodes should periodically forward data to the base station.Since battery power and bandwidth are the two resources to be effectively utilized the sensor nodes forwards data to the nearest neighbor node which inturn forwards the data to its neighbors and this repeats until all the sensors data aggregates at the base station.In a large network scheduling the sensor nodes for forwarding the data is very much essential and since the topology of the USN changes dynamically , finding such a schedule is also very difficult.In [1] the authors proposed a bosonic network model for communication.In this paper we present a quantum bosonic data aggregation method for constructing the optimal schedule of nodes for data aggregation called QBDAS (Quantum Boson Data aggregation scheduling) protocol.The main idea is to search a optimal schedule through utilizing the observable or measurable physical properties the network topology.
Quantumnetworking is emerging as a new research area to explore the opportunities of interconnecting quantum systems through end-to-end entanglement of qubits at geographical distance via quantum repeaters. A promising architecture has been proposed in the literature that decouples entanglement between adjacent quantum nodes/repeaters from establishing end-to-end paths by adopting a time slotted approach. Within this model, we destructure further end-to-end path establishment into two subproblems: path selection and scheduling. The former is set to determine the best repeaters to connect two end nodes, provided that all their local entanglements have succeeded. On the other hand, scheduling is concerned with deciding, which pairs of end nodes are served in the current time slot, while the others remain queued for later time slots. Unlike path selection, scheduling has not been investigated so far in the literature, particularly in presence of quantum noise, which makes both problems even more challenging. In this article, we propose to address it via a general framework of heuristic algorithms, for which we propose three illustrative instances with the objective of keeping the application delay small while achieving a good system utilization, in terms of high entanglement rate and fidelity of remotely entangled qubits. The system proposed is evaluated extensively via event-driven quantum network simulations, with noisy repeaters, in different node topologies under a Poisson arrival of requests from quantum applications. The results show the existence of a fundamental tradeoff between system- and application-level metrics, such as fairness versus entanglement and fidelity, which lays the foundations for further studies in this thriving research area.
Recent technological advancements in satellite based quantum communication has made it a promising technology for realizing global scale quantum networks. Due to better loss distance scaling compared to ground based fiber communication, satellite quantum communication can distribute high quality quantum entanglements among ground stations that are geographically separated at very long distances. This work focuses on optimal distribution of bipartite entanglements to a set of pair of ground stations using a constellation of orbiting satellites. In particular, we characterize the optimal satellite-to-ground station transmission scheduling policy with respect to the aggregate entanglement distribution rate subject to various resource constraints at the satellites and ground stations. We cast the optimal transmission scheduling problem as an integer linear programming problem and solve it efficiently for some specific scenarios. Our framework can also be used as a benchmark tool to measure the performance of other potential transmission scheduling policies.
Quantum networks must deliver end-to-end entanglement whose fidelity meets application requirements, which typically requires entanglement purification along multi-hop paths. Existing scheduling approaches either purify at the link level or optimize end-to-end fidelity for a static set of requests, and do not capture concurrent requests competing for network resources over time. This paper studies concurrent entanglement purification scheduling in a time-slotted quantum network with heterogeneous links, in which requests compete for link capacity and quantum memories, and each request may be served by multiple parallel paths for increased reliability. We formulate a mixed-integer linear programming (MILP) scheduler that maximizes the number of scheduled requests, with max-min path allocation as a fairness criterion, and develop reinforcement learning (RL) schedulers based on DQN, PPO, and SAC, whose rewards balance request success against Bell-pair consumption. Simulations under varying network load, topologies and target fidelity thresholds show that the RL schedulers achieve the highest success rates, improving on MILP by up to 6% and on Progressive Filling and Greedy heuristics by 16.5% and 19.5% respectively, at the cost of higher Bell pair consumption and the deferral of a small fraction of requests. MILP, in contrast, achieves the highest fairness and the lowest Bell pair consumption in most scenarios.
Abstract Quantum networks can establish End-to-End (E2E) entanglement connections between two arbitrary nodes for various quantum applications through entanglement swapping. To support quantum applications reliably, quantum networks should establish E2E entanglement connections with desired entanglement fidelity by performing entanglement purification. The existing works provide mechanisms for link-level purification, which, however, leads to low throughput because of lacking consideration on purifications at network-level. (That is, efficiently allocating scarce resources to jointly purify entanglements under the situation with multiple concurrent requests in a network remains a critical but unsolved problem.) To address this problem, we analyze the cost of purification, design the E2E fidelity calculation method in detail, and propose a novel approach called Purification Scheduling Control (PSC). The basic idea of PSC is to determine the appropriate purification through jointly optimizing purification and resource allocation processes based on conflict avoidance. We conduct extensive experiments showing that PSC can maximize throughput under the fidelity requirement. This solution should spur the development of more applied reliable routing designs for quantum networks, combining quantum error correction, quantum applications, and computer network theory.