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Optimization Solver
Optimization Solver

Benchmarking BQPhy®'s QIEO Algorithm: Up to 3.9x Faster Than Genetic Algorithms

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Challenges
  • High-dimensional engineering optimization in digital missions is computationally expensive for traditional classical, gradient, and meta-heuristics algorithms.
  • Numerous cost function evaluations are required for complex problems which leads to
    • Convergence at local minima
    • Suboptimal designs
    • Inefficient simulations

Results

  • Implementing QIEO on existing GPU systems enhances parallelization, increases search space exploration capabilities and achieves greater accuracy
  • QIEO achieved speedups of up to 3.9x across benchmark functions (2.9x for Ackley, 3.9x for Rosenbrock, and 3.84x for Rastrigin).

‍

  • It required significantly fewer function evaluations (up to 12x fewer for Ackley) and converged up to four times faster than GA (one-fourth the time for Rosenbrock and Rastrigin). Accurate identification of global minima (even if multiple) leading to More optimal design
  • Akley, Rastrigin, and Rosenbrock Test functions, known as artificial landscapes, were used to evaluate characteristics of BQPhy QIEO such as convergence rate, precision, robustness and general performance

‍

QIEO Outperforms Genetic Algorithms

3.9x

Speed up Convergence

‍

12x
Fewer Function Evaluations
4x
Faster
Go Beyond Classical Limits.
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