Performance Comparison for Quantum Approximate Optimization Algorithm (QAOA) across Noiseless Simulation, Experimentally Benchmarked Noisy Simulation, and Experimental Hardware Platforms
Bibliographic record
Abstract
We implement Quantum Approximate Optimization Algorithm (QAOA) on NP-Hard problem MaxCut (for 3-chain and 4-node chain graphs, and 4-node and 6node Mobius Ladder graphs) on a quantum simulator without noise, a simulator with experimentally bench-marked noise (fake back-end), and 5-qubit and 7-qubit processors. We use the following modes of operation: QAOA parameters updated through forward pass on the quantum circuit, modelled by the noiseless simulator as well as fake back-end, and the quantum circuit for final run with updated parameters implemented on the noiseless simulator, fake back-end, and real quantum processors. While QAOA yields higher approximation ratio compared to random guess for almost all graph instances, we also conclude that given the noise in existing quantum hardware, a quantum circuit with more than two stages is not suitable for experimental implementation of QAOA currently.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".