Researchers Execute Quantum Algorithm on 110-Node Network Traffic Benchmark

Cameron V. Cogburn and colleagues have developed a pipeline for partitioning honeypot traffic using the Quantum Approximate Optimisation Algorithm (QAOA) on IBM quantum processors with over 100 qubits. The pipeline offers a framework for benchmarking hardware feasibility and architectural considerations for quantum optimisation in cybersecurity, highlighting the key role of reporting MaxCut cost, security quality, routing overhead, and runtime as distinct metrics. Current benchmark graphs are solvable by classical heuristics, but the study confirms that shallow QAOA implementations can handle real traffic partitioning workloads at a practical scale, enabling future investigations into quantum advantage in this vital area.

Real honeypot traffic partitioning benchmarks demonstrate scalability on IBM quantum processors

Real traffic partitioning workloads have now been executed on IBM quantum hardware, with benchmark graphs containing up to 110 event nodes, exceeding previous demonstrations limited to smaller datasets. This capability establishes a key threshold for assessing quantum hardware’s potential in cybersecurity applications, previously impossible given the scale of available quantum processors. The increasing prevalence of denial-of-service (DoS) and distributed denial-of-service (DDoS) attacks necessitates robust and scalable mitigation strategies. Traditional methods often struggle to differentiate between malicious and benign traffic with sufficient accuracy, leading to false positives and disruption of legitimate services. Honeypots, decoy systems designed to attract attackers, generate valuable data for analysing attack patterns. However, processing and partitioning this honeypot traffic to identify malicious sources is computationally intensive. This work details a reproducible pipeline converting honeypot traffic into a format suitable for quantum computers, specifically utilising a weighted MaxCut problem; this allows evaluation of how different quantum processor architectures and routing impact both solution quality and associated runtime costs.

Benchmark graphs were created with 16, 32, 66, and 110 event nodes, the largest instance comprising 181 edges and executed across three distinct IBM quantum backends: ibm_rensselaer, ibm_miami, and ibm_boston. These backends allowed for comparison across varying qubit connectivity and coherence times, crucial factors influencing quantum algorithm performance. The weighted MaxCut problem is particularly well-suited to this application because it models the network as a bipartite graph, where nodes represent network entities (e.g., IP addresses) and edges represent connections between them. The weights assigned to each edge reflect the volume of traffic or the severity of the detected malicious activity. The goal of the MaxCut algorithm is to partition the nodes into two sets such that the sum of the weights of the edges crossing the partition is maximised, effectively separating malicious traffic from benign traffic. Analysis revealed that processor architecture and operation routing sharply impacted solution quality and runtime, highlighting their importance in quantum cybersecurity applications. Specifically, the connectivity of the qubits on each processor dictated the complexity of mapping the logical network graph onto the physical qubit layout, introducing additional overhead and potentially reducing solution accuracy. A noiseless matrix product state reference demonstrated that much of the performance gap stemmed from the limited depth of the quantum algorithm used, rather than inherent hardware noise. This suggests that improvements in quantum circuit design and algorithm optimisation could significantly enhance performance on near-term quantum devices. Future improvements in algorithm design could yield substantial performance gains on existing hardware, suggesting a promising path forward. Further investigation will focus on optimising the quantum circuit and exploring more sophisticated algorithms to address current limitations and unlock the full potential of quantum computing for network security.

Quantum algorithm benchmarking aids network traffic separation feasibility studies

Ever more sophisticated methods of traffic separation are demanded to protect networks from denial-of-service attacks, a challenge that quantum computing now addresses. The increasing sophistication and volume of DDoS attacks pose a significant threat to online services and critical infrastructure. Traditional mitigation techniques, such as firewalls and intrusion detection systems, are often overwhelmed by the sheer scale of these attacks. Quantum computing offers a potential avenue for developing more effective and scalable mitigation strategies by leveraging the principles of quantum mechanics to solve complex optimisation problems. This research successfully demonstrates partitioning real network traffic using a quantum algorithm, representing a significant step towards utilising quantum processors for cybersecurity. Establishing a benchmark framework for quantum cybersecurity tools remains valuable, even though current benchmark problems are readily solved by conventional computers.

Deliberately simple quantum algorithms were utilised in these experiments to allow direct comparison of performance across varying problem sizes and different quantum hardware architectures. The Quantum Approximate Optimisation Algorithm (QAOA) was chosen for its relative simplicity and suitability for solving combinatorial optimisation problems like MaxCut. While classical heuristics can currently achieve comparable results on the tested graph sizes (16, 32, 66, and 110 nodes), the primary goal of this work is not to demonstrate immediate quantum supremacy, but rather to establish a reproducible methodology for benchmarking quantum hardware and algorithms in the context of cybersecurity. Realistic network traffic partitioning, separating harmful data from legitimate users, has now been successfully demonstrated on IBM quantum hardware. Converting the problem into a weighted MaxCut, a mathematical representation of network connections, allowed performance to be benchmarked across different quantum processors and assessed the impact of data routing. The process involved mapping the honeypot traffic data into a quadratic unconstrained binary optimisation (QUBO) problem, a standard format for quantum annealers and QAOA algorithms. Establishing a framework to measure MaxCut cost, security, routing efficiency, and processing time provides vital metrics for evaluating quantum optimisation in cybersecurity. MaxCut cost directly reflects the effectiveness of the traffic partitioning, while security quality assesses the ability to correctly identify and isolate malicious traffic. Routing overhead quantifies the complexity of mapping the problem onto the quantum hardware, and runtime measures the time required to obtain a solution. Classical computers currently match the results, but this work opens questions regarding the potential for quantum speedup with more advanced algorithms and larger, more complex network models. The researchers anticipate that as quantum hardware continues to improve in terms of qubit count, coherence, and connectivity, and as more sophisticated quantum algorithms are developed, quantum computing will play an increasingly important role in securing networks against evolving cyber threats.

This research successfully demonstrated realistic network traffic partitioning on IBM quantum hardware using graphs of up to 110 event nodes. Converting the problem into a weighted MaxCut allowed researchers to benchmark performance across different quantum processors and assess data routing impacts. While classical computers currently achieve similar results with these graph sizes, the work establishes a reproducible framework for evaluating quantum optimisation in cybersecurity, measuring metrics such as MaxCut cost, security quality, routing overhead, and runtime. The authors intend to continue refining this benchmarking process as quantum hardware and algorithms advance.