Lattice Problems Mapped to Quantum Optimization via QUBO Formulation

Researchers have established a systematic connection between Module Learning With Errors (MLWE) problems, a cornerstone of post-quantum cryptography, and Quadratic Unconstrained Binary Optimization (QUBO) models designed for quantum annealing. Ruturaj Khamitkar of D Y Patil International University and colleagues developed a framework encoding small MLWE instances into QUBOs, allowing for the simultaneous recovery of secret coefficients and error variables from a quantum annealer’s ground-state solution. The formulation jointly represents secret coefficients and explicit error variables within a unified binary optimization structure, enabling their simultaneous recovery from the ground-state solution. The admissible noise region forms a convex polytope representing the system’s tolerance…