en.Wedoany.com Reported - Allstate Insurance and technology developer IBM (International Business Machines Corporation) jointly released a research report stating that quantum computing can optimize risk investment portfolios and solve high-difficulty computational problems in the underwriting field. The study, published as a preprint in mid-2026, focuses on the chance-constrained knapsack problem—a class of combinatorial optimization tasks in computer science known for their difficulty in solving. The operational goal of this problem aligns with the core task of insurance underwriting: determining the most profitable combination of policies to include in a corporate portfolio while ensuring that maximum allowable risk and loss limits are not exceeded. Standard knapsack problems are already difficult to solve at scale for classical systems, and when a policy variable represents actual risks with unpredictability and high correlation, the problem complexity increases exponentially.
Unlike categories such as auto insurance, which can be underwritten independently—where a single driver's accident has minimal impact on the overall risk pool probability—home insurance is dominated by deeply interconnected environmental risks. Large-scale natural disasters, such as localized tornadoes, regional wildfires, or major hurricanes, often strike entire geographic areas simultaneously, triggering massive claims that can affect thousands of adjacent policies at once. To assess this extreme tail risk, insurance teams currently rely on intensive classical simulations, requiring the execution of up to one hundred thousand scenarios to map potential future losses. However, when calculating parameters for rare disasters covering vast geographic regions, this empirical approximation method has high uncertainty, leading to structurally inefficient traditional mixed-integer mathematical programming and worst-case scenario modeling.
To overcome this computational barrier, the research team developed a hybrid quantum-classical optimization framework that combines gate-based quantum hardware with a predictive classical post-processing layer. The quantum computing phase runs a variational program built around a Quantum Approximate Optimization Algorithm (QAOA) circuit tailored for the knapsack problem, designed to embed probabilistic chance constraints directly into the quantum state. When run on the IBM Quantum Heron processor, this circuit explores a complex non-convex parameter space to generate an initial pool of high-quality candidate bitstrings that prioritize high underwriting value while adhering to target risk levels.
Since current medium-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 those that violate specified risk budgets and learning which policy variables appear most frequently in successful portfolios. This knowledge is iteratively fed back to guide the next round of quantum computation, forming a virtuous optimization cycle. To overcome the common problem in variational circuits where the learning signal degrades as problem size increases, the research team introduced a constraint-aligned parameter transfer strategy, which first trains the circuit on smaller problem instances and then directly transfers the learned optimization parameters to larger data scales.
The joint method was rigorously benchmarked on the IBM Heron processor using problem sizes ranging from 20 to 150 items, employing deep quantum circuits with up to 177 layers and 3,443 effective gates. When compared with standard classical approximate heuristic algorithms—including parallel tempering, tabu search, simulated annealing, and genetic algorithms—the quantum-classical workflow provided comparable solution quality, matching provably exact classical answers for problems with up to 75 items. Although current hardware noise levels limit the immediate operational scale of the framework, the experiment demonstrated a scalable enterprise template. As physical gate errors decrease, the processing burden will seamlessly shift from the classical correction layer to the quantum processor, establishing a clear path toward achieving practical quantum advantage for high-risk financial and underwriting applications.
The full peer-reviewed preprint manuscript detailing the variational circuit design, parameter transfer protocol, and random benchmarking is available on the arXiv platform. A summary of the enterprise method and institutional commentary on related insurance policy use cases remain hosted on the IBM Quantum Intelligence blog, and collaborative industry announcements can be accessed via the IBM Quantum Network updates.
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