Quantum-inspired Jaguar Algorithm for Real-World Financial Problems
Bibliographic record
Abstract
Quantum-inspired algorithms simulate quantum characteristics on classical computers to accelerate the solution of complex real-world optimization problems. In the era before general-purpose quantum computers become practical, they offer a promising and accessible alternative for tackling such challenges. Among optimization problems, combinatorial types are particularly challenging due to discrete spaces, large scales, and strong variable dependencies. This study proposes a novel quantum-inspired algorithm named the quantum-inspired jaguar algorithm (QJA), specifically designed to address highly discrete and structurally complex combinatorial optimization problems. QJA integrates the adaptive hunting mechanism of the jaguar algorithm with the core concept of quantum-inspired tabu search algorithm, which guides the search by moving toward the best solution while avoiding the worst one. QJA dynamically adjusts its parameters based on historical information to accelerate convergence and efficiently explore the solution neighborhood. In addition, the algorithm incorporates an entangled local search mechanism to further enhance solution quality. QJA is evaluated on real-world portfolio optimization using data from the Toronto Stock Exchange. Results show that it outperforms other quantum-inspired algorithms in both stability and solution quality, demonstrating superior convergence and practical applicability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".