Results
Optimization
Optimization

Magnetic Lattice Design Under Uncertainty Using QIO

Contact Us
Thank you! Your PDF will get downloaded automatically.
Oops! Something went wrong while submitting the form.
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

‍

    • 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
    Go Beyond Classical Limits.
    Explore with a commitment-free Proof of Concept.
    Schedule Call
    Get in touch for a no obligation proof-of concept
    Schedule a Call
    More to Explore
    SEE ALL
    Optimization
    Magnetic Lattice Design Under Uncertainty Using QIO
    Optimize magnetic spin configurations up to 50×50 lattices with faster convergence and reduced computational cost.
    Check Out
    Optimization Solver
    Airfoil Shape Optimization Using Surrogate model & QIO
    Achieved high-accuracy drag prediction with R² = 0.996 and improved design convergence|| Faster exploration • Reduced evaluation cost • Stable optimization
    Check Out
    Optimization Solver
    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.
    Check Out
    USA Office
    Head Quarters : 235 Harrison St, Syracuse, NY 13202 USA
    India Office
    BHIVE Workspace, AKR Tech Park, B Block, 3rd Floor,
    Kudlu Gate, Hosur Road, Bengaluru – 560068, Karnataka
    Cookies
    Company
    Home
    Product
    About Us
    Blogs
    Career
    Privacy Policy
    General
    bizdev@bqpsim.com
    Career
    careers@bqpsim.com
    Join our newsletter
    Thank you! Your submission has been received!
    Oops! Something went wrong while submitting the form.
    © 2026 BQP. All rights reserved.