Investigation of Performance and Scalability of a Quantum-Inspired Evolutionary Optimizer (QIEO) on NVIDIA GPU
Bibliographic record
Abstract
Quantum inspired evolutionary optimization leverages quantum computing principles like superposition, interference, and probabilistic representation to enhance classical evolutionary algorithms with improved exploration and exploitation capabilities. Implemented on NVIDIA Tesla V100 SXM2 GPUs, this study systematically investigates the performance and scalability of a GPU-accelerated Quantum Inspired Evolutionary Optimizer applied to large scale 01 Knapsack problems. By exploiting CUDA`s parallel processing capabilities, particularly through optimized memory management and thread configuration, significant speedups and efficient utilization of GPU resources is demonstrated. The analysis covers various problem sizes, kernel launch configurations, and memory models including constant, shared, global, and pinned memory, alongside extensive scaling studies. The results reveal that careful tuning of memory strategies and kernel configurations is essential for maximizing throughput and efficiency, with constant memory providing superior performance up to hardware limits. Beyond these limits, global memory and strategic tiling become necessary, albeit with some performance trade offs. The findings highlight both the promise and the practical constraints of applying QIEO on GPUs for complex combinatorial optimization, offering actionable insights for future large scale metaheuristic implementations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".