Magnetic Lattice Design Under Uncertainty Using QIO
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Challenges
Magnetic materials are modeled using discrete spin states (up/down)
A lattice with n × n spins creates a high-dimensional combinatorial optimization problem Temperature and magnetic field uncertainty further increase complexity
Long-range spin interactions must be considered for accurate predictions
Classical optimization methods become increasingly difficult to scale as system size and uncertainty grow.
Results
Up to 2× faster optimization compared with Genetic Algorithms
Consistent solution quality across increasing lattice sizes
Significant reduction in computational cost for uncertainty-driven optimization
Improved scalability for large discrete systems
Accelerating Large-Scale Magnetic Lattice Optimization
Speed
Up to 2X faster than Classical Algorithms
Solution Quality
Achieved Better optimum free-energy solutions across all lattice sizes
Scalability
Successfully optimized systems up to 50 × 50 spin lattices and it is scalable
Optimized Airline Gate Allocation for Resilient Airport Operations
BQPhy’s QIO solver delivers up to 3X faster convergence than Classical Algorithms for dynamic airport gate assignments, while consistently producing conflict-free schedules.