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QIO Algorithm Benchmark: 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 QIO on existing GPU systems enhances parallelization, increases search space exploration capabilities and achieves greater accuracy
  • QIO 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 QIO such as convergence rate, precision, robustness and general performance

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QIO Outperforms Genetic Algorithms

3.9x

Speed up Convergence

‍

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