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Journal of System and Management Sciences

A Reinforcement Learning Model for Quantum Network Data Aggregation and Analysis

Published Feb 20, 2022• 5 citations
Publisher

Abstract

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.

DOI
10.33168/jsms.2022.0120

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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.
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.
With the continuous expansion of quantum communication networks and the increasing diversity of their business types, how to achieve efficient configuration under limited quantum resources has become the key to improving system performance. The quantum resources described in this article mainly include entanglement pairs, quantum bits, and channel capacity. Traditional optimization methods typically rely on precise mathematical models, which are difficult to adapt to dynamically evolving network environments and susceptible to noise interference. Therefore, this study proposes a machine learning based framework for optimizing the allocation of quantum communication resources. This framework integrates deep reinforcement learning and graph neural network technology, enabling collaborative optimization of entanglement allocation, routing selection, and time slice scheduling between nodes. Three typical quantum communication scenarios were constructed and tested in a simulation environment, including static topology, dynamic links, and high noise environments. The proposed method was compared and analyzed with integer linear programming, heuristic greedy algorithms, and rule-based strategies. Experimental data shows that this research method achieved an improvement of approximately 12% to 18% in average fidelity retention, a 15% to 22% increase in resource utilization, and a 20% reduction in latency. This study provides useful reference for the intelligent resource management of large-scale quantum networks in the future.
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.
Current approaches for entanglement distribution networks face a largely untackled problem: precise timing and management of entanglement generation and swapping attempts is key in order to make entanglement distribution between any two nodes.To address this, we model a quantum network as two graphs, one containing the telecom fibers enabling physically the entanglement, and another containing the Bell pairs stored in its memories. Then, efficient entanglement distribution becomes a task of populating a mix of both graphs with a connection between one Alice and one Bob subject to constraints. Here, machine learning techniques are useful to find efficient policies through reinforcement learning. This way, we manage to consider inhomogeneous probabilities of generation and swapping, as well as complex networks beyond the chain scenario.
This paper investigates resource allocation for entanglement distribution in multi-node, multi-channel quantum networks at the metropolitan scale. A multi-slot quantum network simulation framework is developed across physical and network layers, incorporating heterogeneous link characteristics, limited quantum memories, request queuing, retry mechanisms, and concurrent entanglement distribution. Based on this framework, a centralized scheduling architecture is proposed to coordinate quantum memories, communication channels, and routing paths for multiple simultaneous entanglement requests. Three classes of resource allocation strategies are evaluated: heuristic approaches, a mixed-integer linear programming (MILP) optimization method, and a Proximal Policy Optimization (PPO)-based reinforcement learning approach. The heuristic schemes reveal fundamental trade-offs between request delay and entanglement success rate: Dynamic Efficient minimizes delay, Success Enhancement improves success probability through adaptive multi-path allocation, and Static Efficient provides a balance between these objectives. The MILP approach achieves optimized resource allocation by jointly considering request handling and multi-path assignment, while the PPO-based method learns adaptive scheduling policies to improve overall performance. Simulation results demonstrate that these approaches provide different performance advantages in terms of request delay, entanglement success rate, capacity utilization, and request handling rate, highlighting the trade-offs between efficiency and reliability in metropolitan-scale quantum network scheduling.
Tomorrow's quantum technologies for communication, sensing, and distributed computing will rely on networks with entanglement shared between spatially separated nodes. The authors provide improved protocols and policies for entanglement distribution along a chain of nodes, accounting for practical limitations such as photon losses, nonideal measurements, and quantum memories with short coherence times. These policies feature dynamic, state-dependent memory cutoffs and collaboration between nodes, all of which are quantified. Nesting policies for small repeater chains yields policies for large chains that improve upon a swap-as-soon-as-possible approach, and thus pave the way to scaling up.