Enhanced Machine Learning Applications using Automated Quantum Architecture Search: Employing Meta-Learning and Evolutionary Algorithms
Bibliographic record
Abstract
Quantum computing combined with machine learning, is a promising emerging field for tackling problems that are extremely expensive or fundamentally infeasible for classical computers. Nonetheless, design of the optimal quantum circuit for machine learning is a major challenge, which can be attributed to the exponential search space of the quantum gate arrangement and unmatched structured design methods. To resolve this, our work introduces a novel automated framework for quantum architecture search (AQAS) that adopts both meta-learning and evolutionary algorithms to automatically learn, transfer, and optimize quantum circuit architectures to suit different machine learning tasks. Our strategy relies on meta-learning to introduce a learning scheme, i.e. it allows the AQAS system to generalize across distributions by learning from a shared structural and functional prior from the distribution of machine learning problems. This enables the approach to quickly home in effective quantum circuit implementations for new tasks with low data or retraining. At the same time, we use evolutionary algorithms– mimicking natural selection– to breed populations of quantum circuit architectures. These circuits are represented in genotypes, are varied under mutation and crossover, and are selected according to a fitness function that blends quantum resource utilization (e.g., depth, qubits) with task-specific performance (e.g., classification accuracy or loss). The architecture is realized in a mixed quantum-classical environment, with quantum circuits simulated on quantum simulators and/or Noisy-Intermediate-Scale-Quantum (NISQ) devices. We verify the effectiveness of our approach on a number of benchmarked datasets and tasks which range from quantum-aided image recognition, time series prediction to medical diagnostics. We demonstrate that AQAS can discover compact, high-performing quantum models, which generalize better and are computationally more efficient than the custom-built quantum models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".