Insurance provider Allstate and technology developer IBM have demonstrated that quantum computing can optimize risk portfolios and resolve severe computational challenges within the underwriting sector. Published as a pre-print in mid-2026, the joint investigation addresses the chance-constrained knapsack problem, an notoriously difficult class of combinatorial optimization tasks in computer science. The operational objective mirrors the primary core task of insurance underwriting: identifying the most profitable combination of policies to pack into a corporate portfolio without exceeding a maximum allowable risk and loss limit. While standard knapsack problems are difficult for classical systems to solve at scale, the problem becomes exponentially harder when individual policy variables represent unpredictable, highly correlated real-world risks.
Unlike independent underwriting categories like automobile coverage, where individual driver accidents have minimal bearing on broader pool probabilities, homeowners insurance is governed by deeply interconnected environmental risks. Widespread natural disasters—such as a localized tornado, a regional wildfire, or a major hurricane—frequently strike an entire geography simultaneously, triggering widespread claims that can impact thousands of neighboring policies at once. To evaluate this extreme tail risk today, insurance teams rely on power-intensive classical simulations, executing up to one hundred thousand scenarios to map potential future losses. However, this empirical approximation becomes highly uncertain when calculating rare disaster parameters across vast geographies, rendering traditional mixed-integer mathematical programs and worst-case scenario modeling structurally inefficient
To navigate this computational barrier, the research team developed a hybrid quantum-classical optimization framework that combines gate-based quantum hardware with predictive classical post-processing layers. The quantum computing phase runs a variational program constructed around a knapsack-specific Quantum Approximate Optimization Algorithm (QAOA) circuit designed to embed probabilistic chance constraints directly into the quantum state. Operating on an IBM Quantum Heron processor, the circuit maps out the complex, non-convex parameter spaces to generate an initial pool of high-quality candidate bitstrings that prioritize high underwriting values while respecting a target risk level.
Because current intermediate-scale quantum hardware operates under physical noise constraints, the framework integrates a novel self-consistent classical recovery scheme to refine the raw quantum samples. The classical post-processing layer cleans the candidate bitstring pool by systematically repairing solutions that break the designated risk budget and learning which policy variables appear most frequently in successful portfolios. This knowledge is iteratively fed back to guide the next sequence of quantum calculations, generating a virtuous optimization cycle. To overcome the learning signal degradation common in variational circuits as a problem scales up, the team introduced a parameter transfer strategy based on constraint alignment, which trains the circuit on a smaller problem instance before transferring those learned optimization parameters directly onto larger data scales.
The joint methodology was rigorously benchmarked on IBM Heron processors using problem sizes ranging from 20 to 150 items, utilizing deep quantum circuits containing up to 177 layers and 3,443 active gates. When evaluated against standard classical approximation heuristics—including parallel tempering, tabu search, simulated annealing, and genetic algorithms—the quantum-classical workflow delivered comparable solution quality, matching the provably exact classical answer on problems containing up to 75 items. While current hardware noise levels restrict the immediate operational scale of the framework, the experiment demonstrates a scalable enterprise template. As physical gate errors decrease, the processing burden shifts seamlessly from the classical correction layer to the quantum processor, establishing a definitive path toward real-world quantum advantage for high-stakes financial and underwriting applications.
The complete peer-reviewed pre-print manuscript detailing the variational circuit designs, parameter transfer protocols, and stochastic benchmarks can be reviewed in the full text available on arXiv here. Corporate methodology summaries and institutional commentary on underlying insurance policy use cases remain hosted via the IBM Quantum Intelligence Blog here, with collaborative industry announcements accessible through the IBM Quantum Network updates here.
June 23, 2026
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