935 research outputs found
Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly
There has been considerable progress in the design and construction of
quantum annealing devices. However, a conclusive detection of quantum speedup
over traditional silicon-based machines remains elusive, despite multiple
careful studies. In this work we outline strategies to design hard tunable
benchmark instances based on insights from the study of spin glasses - the
archetypal random benchmark problem for novel algorithms and optimization
devices. We propose to complement head-to-head scaling studies that compare
quantum annealing machines to state-of-the-art classical codes with an approach
that compares the performance of different algorithms and/or computing
architectures on different classes of computationally hard tunable spin-glass
instances. The advantage of such an approach lies in having to only compare the
performance hit felt by a given algorithm and/or architecture when the instance
complexity is increased. Furthermore, we propose a methodology that might not
directly translate into the detection of quantum speedup, but might elucidate
whether quantum annealing has a "`quantum advantage" over corresponding
classical algorithms like simulated annealing. Our results on a 496 qubit
D-Wave Two quantum annealing device are compared to recently-used
state-of-the-art thermal simulated annealing codes.Comment: 14 pages, 8 figures, 3 tables, way too many reference
Benchmarking and analyzing iterative optimization heuristics with IOHprofiler
Algorithms and the Foundations of Software technolog
Parameter calibration of a system dynamics model. A comparison of three evolutionary algorithms
This research seeks to improve the parameter calibration process of a System Dynamics model. A movie release strategies" model has been developed in 2012 using a gradient-based optimization algorithm to estimate all the parameters. On this research, three modern optimization algorithms are initially compared using mathematical benchmark functions and then tested with the model to compare results. The tested algorithms are modifications of the Artificial Bee Colony algorithm, the Cuckoo Search and the Genetic Sampler. The results show that by using the Artificial Bee Colony algorithm, better performance is achieved in terms of speed and fitness. It is also shown how the optimization problem definition was improved resulting from a better optimization process.GEO-SD360JMASV-SYS
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
Nanophotonic structures have versatile applications including solar cells,
anti-reflective coatings, electromagnetic interference shielding, optical
filters, and light emitting diodes. To design and understand these nanophotonic
structures, electrodynamic simulations are essential. These simulations enable
us to model electromagnetic fields over time and calculate optical properties.
In this work, we introduce frameworks and benchmarks to evaluate nanophotonic
structures in the context of parametric structure design problems. The
benchmarks are instrumental in assessing the performance of optimization
algorithms and identifying an optimal structure based on target optical
properties. Moreover, we explore the impact of varying grid sizes in
electrodynamic simulations, shedding light on how evaluation fidelity can be
strategically leveraged in enhancing structure designs.Comment: 31 pages, 31 figures, 4 tables. Accepted at the 37th Conference on
Neural Information Processing Systems (NeurIPS 2023), Datasets and Benchmarks
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