Generating Sparse Representations Using Quantum Annealing: Comparison to Classical Algorithms
Bibliographic record
Abstract
We use a quantum annealing D-Wave 2X (1,152-qubit) computer to generate sparse representations of Canny-filtered, center-cropped 30×30 CIFAR-10 images. Each binary neuron (qubit) represents a feature kernel obtained initially by imprinting on a randomly chosen 5×5 image patch and then adapted via an off-line Hebbian learning protocol using the sparse solutions generated by the D-Wave. When using binary neurons, the energy function is non-convex (multiple local-minima) and finding a global minimum is NP-hard. Quantum annealing provides a strategy for finding sparse representations that correspond to good local minima of a non-convex cost function. To overcome the severe coupling restrictions between physical qubits on the D-Wave Chimera graph, we use embedding tools to achieve approximately all-to-all connectivity across a reduced number of logical qubits. We assess the sparse representations generated by the D-Wave using both the total energy as well as classification accuracy on a subset of the CIFAR-10 database. The D-Wave 2X outperforms two classical state-of-the-art binary solvers, GUROBI and Chimera-inspired algorithm Hamze-Freitas-Selby (HFS). Specifically, the D-Wave 2X yields lower energy sparse solutions within seconds while the largest problems take over 10 hours for both GUROBI and HFS. We obtained cross-validation classification of 31.02% for the first 4K images using 47 features on the quantum D- Wave 2X.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".