Risk-Aware Cybersecurity Remediation Planning as a Quadratic Unconstrained Binary Optimization Problem for Quantum Annealing

This preprint presents a risk-aware framework for enterprise cybersecurity remediation planning using Quadratic Unconstrained Binary Optimization (QUBO) for quantum annealing and hybrid quantum-classical solvers. The rapid emergence of AI-enabled vulnerability scanners has significantly increased the speed and volume at which vulnerabilities are identified. Technology companies can no longer afford to leave critical vulnerabilities unresolved for extended periods. At the same time, modernization of enterprise technology stacks introduces additional platforms, services, identities, cloud resources, integrations, and dependencies, thereby expanding the overall attack surface and increasing the complexity of remediation planning. The proposed framework selects an optimized portfolio of heterogeneous security actions—including vulnerability patching, multifactor authentication enablement, privileged-credential rotation, service restriction, network segmentation, and access remediation—while jointly considering residual cyber risk, attack relationships, implementation cost, operational downtime, remediation dependencies, conflicting changes, mandatory controls, and limited engineering capacity. The work provides a machine-readable business-input structure, a deterministic process for constructing the corresponding QUBO coefficients, a worked enterprise-security example, and an automated validation framework. The implementation verifies that the generated QUBO energy agrees with the original business-energy formulation across the tested problem instances and configurations. This work builds upon prior research involving quantum-assisted kill-chain interruption, cybersecurity strategy optimization, patch prioritization, and cyber-risk scoring. It does not claim the first use of QUBO or quantum annealing in cybersecurity and does not claim an established quantum computational advantage. Its primary contribution is a unified, auditable, and reproducible formulation for operationally constrained enterprise remediation portfolios. The record contains the research paper, LaTeX source, example remediation input data, Python-based QUBO construction and validation code, validation output, and citation metadata. DOI: 10.5281/zenodo.20922812 Status: Preprint; not peer reviewed.