Cybersecurity

Managing software patches across a vast telecom infrastructure is a complex task, requiring careful scheduling and prioritization to minimize security risk, downtime, and operational disruption. For telecom providers this becomes a large-scale, constraint-heavy optimization problem.

In a quantum computing use-case, a global telecommunications company explored the use of quantum algorithms to enhance patch management strategy. The challenge was modeled as a constrained optimization problem: selecting and scheduling patches based on criticality, compliance deadlines, resource availability, and system interdependencies.

Using quantum-inspired and hybrid quantum-classical algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) or QUBO formulations, the project investigated whether quantum techniques can outperform classical heuristics in identifying optimized patch rollout sequences.
Quantum computing offers routes to improved management of cybersecurity risks and operational overhead by enabling more adaptive, efficient, and scalable patch deployment strategies across complex IT landscapes.

Threat hunting involves proactive search for hidden, sophisticated cyber threats across complex IT environments. With data volumes growing and adversaries adopting stealthier techniques, detecting subtle patterns in large-scale logs is becoming increasingly difficult using classical tools.

A quantum computing proof-of-concept (PoC) in threat hunting investigates the potential of quantum algorithms, such as quantum-enhanced clustering or pattern recognition, to identify anomalies or correlations that signal malicious activity. These algorithms could assist in narrowing large search spaces or speeding up statistical analysis within massive datasets.

In a typical PoC, security logs or network flow data are pre-processed and structured into feature spaces where quantum algorithms can be applied. The focus is on evaluating detection accuracy, computational efficiency, and how quantum results compare to classical baselines.
Still exploratory, this approach aims to support more scalable and anticipatory cyber defense strategies, especially in environments where rapid detection and response are critical to containing emerging threats.