Pre-Print: Risk-Aware Cybersecurity Remediation Planning as a Quadratic Unconstrained Binary Optimization Problem for Quantum Annealing - Feedback Please

I am pleased to share my new research preprint: “Risk-Aware Cybersecurity Remediation Planning as a Quadratic Unconstrained Binary Optimization Problem for Quantum Annealing” With the rapid emergence of AI-enabled vulnerability-scanning technologies, organizations are identifying vulnerabilities at an unprecedented scale. At the same time, modernization of enterprise technology stacks introduces more platforms, identities, services, integrations, and dependencies—expanding the attack surface and making remediation planning increasingly complex. Drawing on my 19 years of experience in cybersecurity, particularly Identity and Access Management and enterprise security, this paper explores how cybersecurity remediation planning can be formulated as a Quadratic Unconstrained Binary Optimization problem for quantum annealing and hybrid quantum-classical solvers. The proposed framework considers: • Residual cybersecurity risk • Attack-path relationships • Implementation cost • Business disruption and downtime • Remediation dependencies • Conflicting changes • Mandatory security controls • Limited engineering capacity The work provides a machine-readable input model, a deterministic QUBO-generation approach, and validation of the resulting energy formulation. This is a preprint and has not yet undergone peer review. I welcome feedback from researchers and practitioners working in quantum computing, cybersecurity, vulnerability management, and combinatorial optimization. DOI: 10.5281/zenodo.20922812 https://doi.org/10.5281/zenodo.20922812 #QuantumComputing #QuantumAnnealing #Cybersecurity #QUBO #DWave #VulnerabilityManagement #EnterpriseSecurity #IAM #Research submitted by /u/anirbanbhattacharya [link] [comments]