Emerging quantum networks enable distributed quantum computing (DQC) by interconnecting modest, noisy quantum processors. However, practical deployments face tight memory budgets, probabilistic entanglement generation, and decoherence, all while serving multiple users. In realistic settings—cloud execution, iterative/interactive workloads, and concurrent jobs-circuits do not arrive in a single batch but as a stream. This creates a fundamentally online problem: the system must react to stochastic arrivals, balance latency and throughput, and remain stable under varying loads without foresight of future jobs. We study Online Scheduling and Distribution of Quantum Circuits (OS-DQC) and formalize the online scheduling problem for quantum networks: circuits arrive over time and must be executed without knowledge of future arrivals. At each event time (arrival or completion), the system decides which waiting circuits to launch and how to place their qubits, while accounting for congestion from in-flight circuits and constraints due to finite memories and decoherence. The objective is to minimize the mean completion time and the makespan subject to these physical and resource constraints.

This paper presents a field-programmable gate array (FPGA) implementation of a quantum circuit simulator capable of processing 30-qubit systems, representing over 1.07 billion quantum states stored in 8 GB of memory. Built on the Xilinx Alveo U55C platform with High Bandwidth Memory (HBM) technology, our system demonstrates deterministic performance through a 16-bank parallel memory distribution architecture integrated with the Qiskit quantum computing framework. We achieved complete verification across all qubit configurations from 1 to 30 qubits, with execution times ranging from 5 milliseconds to approximately 49 minutes. The simulator supports a comprehensive quantum gate library including single-qubit operations (Hadamard, Pauli gates, rotation gates) and multi-qubit gates (CNOT, controlled-Z, Toffoli. Our performance analysis reveals distinct operational regimes where FPGA excels for small-scale circuits (1-12 qubits) but faces scalability challenges beyond 15 qubits when compared to GPU architectures. This work establishes FPGA as a practical platform for quantum algorithm development and circuit verification, particularly valuable for applications requiring deterministic timing and energy-efficient computation. The architecture provides clear pathways for expansion to 31+ qubits through memory configuration enhancements.

Distributing quantum circuits across a network of quantum devices is a recent approach to improve the scalability of quantum computation. Entangled pairs of qubits distributed across network nodes are used as a communication resource for executing inter-node (remote) gates. However, establishing these entanglement pairs incurs significant latency, and doing so during circuit execution may lead to decoherence of logical qubits. Instead, it may be beneficial to proactively distribute certain entanglement pairs a priori (before execution begins). Thus, we consider the problem of distributing a quantum circuit over a quantum network with pre-distributed entanglement pairs, and seek to minimize the circuit's execution time. We develop algorithms for (i) allocating a limited predistribution budget across memory pairs (optionally guided by a training-circuit distribution) and (ii) computing a static qubit-to-memory allocation that exploits the resulting pre-distributed resources to reduce execution time. We evaluate the proposed methods in NetSquid under a telegate-based execution model on standard benchmark circuit families.

Scaling fault-tolerant quantum computing to practical advantage requires distributing quantum circuits across networks of heterogeneous processors. However, managing physical qubit overhead while satisfying strict logical error targets remains a critical bottleneck. We introduce the Error-Aware Distributed Quantum Computing (EA-DQC) problem, a unified framework for minimizing the physical footprint of distributed circuits under global error and per-processor capacity constraints. To drive our algorithm design and enable formal performance guarantees, we leverage an aggregation model that frames single-qubit allocation as a Generalized Assignment Problem (GAP), allowing us to prove the optimality of our main algorithm (BPF) in several hardware-specific cases. We also consider the generalized setting of multi-qubit block-encodings, which introduce sub-additive costs and non-monotonic scaling that break standard assignment frameworks, necessitating a density-aware algorithm (BCF-Set) to exploit synergistic resource efficiencies. We evaluate our framework on a diverse set of synthetic and benchmark circuits (QFT, QPE, GHZ) across heterogeneous network topologies. Our results show that our combinatorial heuristics achieve up to a 50% reduction in physical-qubit overhead compared to stochastic baselines such as Simulated Annealing. This work provides a rigorous bridge between quantum error correction and classical optimization, offering a scalable path for resource-efficient faulttolerant distributed quantum architectures.

As quantum networks evolve from experimental testbeds to fault-tolerant systems, the primary performance metric shifts from physical link fidelity to end-to-end logical error rate. However, current control planes remain ill-equipped for this transition: routing decisions are typically decoupled from Quantum Error Correction (QEC) strategies, relying on topology or scalar fidelity metrics that fail to predict how specific physical noise structures interact with logical codes. Optimizing this coupled route-and-code performance requires precise, real-time visibility into network error biases, yet traditional active tomography is operationally prohibitive due to throughput collapse and service interruption. We present SCOPE (Syndrome-based COntrol PlanE), a network-layer architecture that enables joint routing and coding optimization using purely passive telemetry. Instead of injecting probes, SCOPE harvests error syndromes – the parity-check outcomes naturally generated by QEC decoders during user service. By aggregating these signals, SCOPE's inference engine reconstructs the network's time-varying error map, capturing complex, context-dependent noise correlations. This visibility drives a decision engine that proactively pushes optimal route-and-code configurations to source nodes. NetSquid and IBM-calibrated simulations show that SCOPE reduces estimation error by more than 60

In Cybersecurity, Intrusion detection system is a tool that is used to identify the abnormal activity on the network in a timely manner. This is done by inspecting and analyzing the features of network packets to find any anomaly in them. In high dimensional traffic, the intrusion detection system needs to inspect and analyze all the features of a network packet to make the decision, which is an overhead. Feature reduction techniques are used to map the existing feature set to a new but less feature space to make the intrusion detection system more efficient. In this work we compared the performance of three commonly used feature reduction techniques, viz. Principle Component Analysis, Independent Component Analysis and Linear Discriminant Analysis. We used k-Nearest Neighbor and Naïve Bayes algorithm as classifiers. It was observed that Linear Discriminant Analysis resulted in improved accuracy of 95-100% and reduced false alarms between 0-0.5%.

As quantum computing continues to scale, the ability to execute quantum circuits across distributed quantum networks is becoming increasingly important. While prior work has largely focused on distributing a single circuit to optimize the number of entanglement pairs (EPs) used or the execution time, future applications will require the efficient scheduling and execution of multiple circuits on a shared quantum network. Therefore, we study the problem of efficiently distributing multiple quantum circuits across a shared quantum network under decoherence and network constraints and seek to minimize the execution time required to execute all circuits (makespan). Solving the above problem involves jointly determining when and where each circuit should be executed, and how to schedule concurrent EP generation required to execute remote gates. We propose several algorithmic approaches for this multi-circuit distribution problem and provide theoretical performance guarantees for special cases. To assess the practical effectiveness of our methods, we conduct extensive simulations using the NetSquid quantum network simulator.

We consider problems of distributing high-fidelity entangled states across nodes of a quantum network. We consider a repeater-based network architecture with entanglement swapping (fusion) operations for generating long-distance entanglements, and purification operations that produce high-fidelity states from several lower-fidelity states. The contributions of this paper are two-fold: First, while there have been several works on fidelity-aware routing and incorporating purification into routing for generating EPs, this paper presents the first algorithms for optimal solutions to the high-fidelity EP distribution problem. We provide a dynamic programming algorithm for generating the optimal tree of operations to produce a high-fidelity EP, and an LP-based algorithm for generating an optimal collection of trees. Second, following the EP algorithms, this paper presents the first algorithms for the high-fidelity GHZ-state distribution problem and characterizes its optimality. We evaluate our techniques via simulations over NetSquid, a quantum network simulator.

Distribution of quantum circuits (DQC) is a promising strategy to enable large-scale quantum computations by utilizing a network of quantum computers. However, executing distributed quantum programs entails generating entanglements (to execute remote gates), which can incur significant latency and lead to qubits’ decoherence. Prior works on the problem of distributing quantum circuits with minimum circuit-execution time have only considered a static allocation of qubits to network nodes, presumably for simplicity. In this work, we show that distributing quantum circuits with a dynamic allocation of qubits, i.e., qubit allocation (over the quantum computers) that changes during the circuit execution, can lower circuit execution time. For this DQC problem wherein qubit allocation can be dynamic (i.e., change over time), we design efficient algorithms and demonstrate the effectiveness of designed techniques via extensive simulations over NetSquid, a quantum network simulator; our techniques outperform the best prior work (using static qubit allocation) by up to 50%.

The development of large-scale quantum networks requires not only advances in physical-layer technologies but also a comprehensive protocol stack that integrates communication, control, and resource management across all layers. We present the first such protocol stack, which introduces a Global Entanglement Module (GEM) that maintains a consistent, network-wide view of entanglement resources through distributed synchronization strategies. By enabling real-time adaptive execution of entanglement distribution plans, GEM bridges the gap between static planning and dynamic operation. The stack naturally supports pre-distributed entanglement, purification, and multi-partite state generation, making it applicable to a broad range of quantum networking applications. We design and evaluate multiple adaptive heuristics for real-time execution and show that a lightweight scoring-based strategy consistently achieves the best performance, improving entanglement generation rates by about 20

Large Language Models (LLMs) excel at generating contextually relevant text but lack logical reasoning abilities. They rely on statistical patterns rather than logical inference, making them unreliable for structured decision-making. Integrating LLMs with task planning can address this limitation by combining their natural language understanding with the precise, goal-oriented reasoning of planners. This paper introduces ViPlan, a hybrid system that leverages Vision Language Models (VLMs) to extract high-level semantic information from visual and textual inputs while integrating classical planners for logical reasoning. ViPlan utilizes VLMs to generate syntactically correct and semantically meaningful PDDL problem files from images and natural language instructions, which are then processed by a task planner to generate an executable plan. The entire process is embedded within a behavior tree framework, enhancing efficiency, reactivity, replanning, modularity, and flexibility. The generation and planning capabilities of ViPlan are empirically evaluated with simulated and real-world experiments.

We address the problem of efficiently distributing high-fidelity entangled states across the nodes of a quantum network—a fundamental requirement for quantum communication, distributed quantum computing, and emerging quantum internet applications. We consider a repeater-based architecture that combines entanglement swapping (or fusion) with purification operations, which enhance fidelity by consuming additional entangled pairs. While prior work has explored routing and limited purification strategies, we present the first algorithms for fidelity-constrained entanglement distribution that are near-optimal and compatible with arbitrary purification models. Our approach introduces the notion of purification-augmented entanglement trees, and we develop dynamic programming (DP) algorithms to construct such trees that maximize the generation rate of entangled pairs (EPs) under fidelity constraints. We further extend our framework to a linear programming (LP)–based flow model that enables simultaneous generation of EPs across multiple source-destination pairs. For multipartite entanglement, we generalize our methods to GHZ and arbitrary graph states, supported by novel analytical models for fidelity evolution under fusion and purification. We evaluate our techniques using the NetSquid quantum network simulator and observe consistent performance improvements over prior approaches—achieving an average 50\% increase in generation rate, and up to 100-150\% in some scenarios.

Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum Networks (QNs) can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, QNs can enable fully secured long-distance communication. Thus, quantum networks lie at the heart of the success of future quantum information technologies. In quantum networks, multipartite entangled states distributed over the network help implement and support many quantum network applications for communications, sensing, and computing. Our work focuses on developing optimal techniques to generate and distribute multipartite entanglement states efficiently. Prior works on generating general multipartite entanglement states have focused on the objective of minimizing the number of maximally entangled pairs (EPs) while ignoring the heterogeneity of the network nodes and links as well as the stochastic nature of underlying processes. In this work, we develop a hypergraph based linear programming framework that delivers optimal (under certain assumptions) generation schemes for general multipartite entanglement represented by graph states, under the network resources, decoherence, and fidelity constraints, while considering the stochasticity of the underlying processes. We illustrate our technique by developing generation schemes for the special cases of path and tree graph states, and discuss optimized generation schemes for more general classes of graph states. Using extensive simulations over a quantum network simulator (NetSquid), we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.

In today’s cashless society, credit cards offer unparalleled convenience, driving a widespread adoption of electronic transactions. Both individuals and governments are embracing this trend, emphasizing the importance of secure payment methods. However, with the surge in credit card usage comes a corresponding increase in fraudulent activities, leading to substantial losses for both consumers and financial institutions. Recognizing this challenge, there is a growing demand for robust fraud detection mechanisms to safeguard transaction integrity. This paper aims to explore the application of machine learning for enhancing fraud detection in credit card transactions, addressing the pressing need for a secure transaction environment.

Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is a promising way to circumvent these challenges and facilitate large quantum computations. However, distributed quantum computations require entanglements (to execute remote gates) which can incur significant generation latency and, thus, lead to decoherence of qubits. In this work, we consider the problem of distributing quantum circuits across a quantum network to minimize the execution time. The problem entails mapping the circuit qubits to network memories, including within each computer since limited connectivity within computers can affect the circuit execution time. We provide two-step solutions for the above problem: In the first step, we allocate qubits to memories to minimize the estimated execution time; for this step, we design an efficient algorithm based on an approximation algorithm for the max-quadratic-assignment problem. In the second step, we determine an efficient execution scheme, including generating required entanglements with minimum latency under the network resource and decoherence constraints; for this step, we develop two algorithms with appropriate performance guarantees under certain settings or assumptions. We consider multiple protocols for executing remote gates, viz., telegates and cat-entanglements. With extensive simulations over NetSquid, a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform a scheme based on prior work by 40 to 50% on average and up to 95% in some cases.