Ziyu Zhang, Zikang Jia, Xiaosong Li, Yulong Dong
We lifted 23 functions out of this paper's own repositories and ran 17 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| dongsnaq/Finite-Noise-Generalization-QML | canonical | 17 of 23 |
| Function | Status | Where it lives |
|---|---|---|
| config_tasks | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/run_checkpoint_sweep.py code served (permissive licence) · get_code("17880e440501c4df") |
| custom_collate_fn | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_molecular/model.py code served (permissive licence) · get_code("2ce2388871b24883") |
| file_sha256 | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/compute_molecular_deff.py code served (permissive licence) · get_code("970994898fa7a46d") |
| fit_geff | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/assemble_recomputed_results.py code served (permissive licence) · get_code("662f42aff1ab732f") |
| jacobian_rows | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/compute_molecular_deff.py code served (permissive licence) · get_code("ca145de1d739f72a") |
| kron_all | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_four_qubit/noise_order_dp.py code served (permissive licence) · get_code("6f5062331e95e2c7") |
| load_gaussian_results | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_four_qubit/noise_programming.py code served (permissive licence) · get_code("f060c09b14d1af37") |
| load_molecular_metrics | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/assemble_recomputed_results.py code served (permissive licence) · get_code("66bcdfafd9203c52") |
| load_result_bundle | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/result_bundle.py code served (permissive licence) · get_code("845db32893fa3ca7") |
| molecular_task | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/run_checkpoint_sweep.py code served (permissive licence) · get_code("394bb51670197362") |
| mse | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/evaluate_four_qubit_checkpoint.py code served (permissive licence) · get_code("e06f503cace0223b") |
| noise_colors | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/plot_style.py code served (permissive licence) · get_code("0f18c20dcc62ba1e") |
| quantize_angle_ste | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_molecular/model.py code served (permissive licence) · get_code("6ac3321e493591f2") |
| rx | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_four_qubit/noise_order_dp.py code served (permissive licence) · get_code("b3f824c4f1955dcd") |
| sha256 | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/evaluate_molecular_checkpoint.py code served (permissive licence) · get_code("6f2a533d0951e650") |
| soft_count | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/compute_molecular_deff.py code served (permissive licence) · get_code("e1390a36e53bff7e") |
| summarize | Ran | dongsnaq/Finite-Noise-Generalization-QML/code/plot_figures.py code served (permissive licence) · get_code("bf0c07b92ddcb985") |
| beta_x | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/plot_figures.py code served (permissive licence) · get_code("bc5582461119e4b0") |
| build_heatmap_arrays | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_four_qubit/noise_programming.py code served (permissive licence) · get_code("e2807313509bed37") |
| one_wire | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/qnir_four_qubit/noise_order_dp.py code served (permissive licence) · get_code("a03de0f3995024bc") |
| read_table | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/plot_figures.py code served (permissive licence) · get_code("4654b8eea9b271c2") |
| sector_purity | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/assemble_recomputed_results.py code served (permissive licence) · get_code("5e58fc0a4af6f6f6") |
| tasks | Not yet run | dongsnaq/Finite-Noise-Generalization-QML/code/run_checkpoint_sweep.py code served (permissive licence) · get_code("6ef98b44594cd464") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Quantum noise is expected to degrade quantum machine learning by driving circuits away from their noiseless implementations. Yet recent studies show moderate noise can reduce testing error, a behavior unexplained by weak-noise perturbative error accumulation or strong-noise trainability collapse. Here we develop a statistical learning theory connecting microscopic noise processes to macroscopic learning performance. At its heart is a noise-order purity parameter, derived from a surrogate model analysis, that predicts the noise-induced reduction in model complexity and the consequent reduction in the generalization gap. Noise simultaneously increases prediction bias. Their competition explains the intermediate-noise regime left open between these limits. It produces a finite-noise optimum whose location depends on the learning setup and can disappear in the largesample limit. Numerical experiments validate these predictions. Noise programming can move a model towards this optimum. These results make the non-monotonic effect of noise predictable and provide a route to harness it.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2608.24229")
get_code_for_paper("2608.24229")
have("2608.24229")
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