References

CheungModes

N. Cheung, Divergent mechanisms of antidepressant efficacy: a unified computational comparison of synaptogenesis, stabilization, and tonic inhibition in a model of depression. Cureus 18, no. 3 (2026), e105040. Published 11 March 2026. https://doi.org/10.7759/cureus.105040. Computational motivation; no clinical ranking is imported as a theorem.

Cheung068Code

N. Cheung, 068a_antidep_modes_v2.py. Version 2.0 source supplied in full for the present analysis, undated. The supplied snapshot defines the static audit. No executed reproduction or unexamined repository revision is asserted.

CheungPruning

N. Cheung, Phased pruning in neural networks recapitulates selectivity–fragility trade-offs in brain development. Scientific Reports (2026). Published online 13 July 2026. https://doi.org/10.1038/s41598-026-62244-5.

CheungCliff

N. Cheung, The synaptic pruning cliff: threshold-like network fragility under internal stress and efficient recovery in a computational model of depression. Preprints.org, version 1, posted 13 January 2026. Not peer reviewed. https://doi.org/10.20944/preprints202601.0805.v1.

CheungPsy01A

N. Cheung, Psy01-A: Phased magnitude pruning in task-gated ReLU networks: cue certificates, stability, and conditional selectivity–fragility trade-offs. Undated manuscript, Psy01-A.tex, supplied in full. Relevant source records: thm:globalstress, thm:parameterstability, prop:rescaling, prop:equaldensity, thm:nesting, and prop:harddiscontinuity. No model identity with the present architecture is presumed.

CheungPsy02A

N. Cheung, Psy02-A: Conditional scalar-carrier bounds and operational separation: a mathematical framework for evaluating Orch-OR hypotheses. Undated manuscript, Psy02-A.tex, supplied in full. Relevant source records: prop:dephasing, prop:Bell, prop:instrument, and thm:nonidentifiable. The scalar and reduction carriers are not imported here.

CheungPsy02B

N. Cheung, Psy02-B: Quantitative viability and identifiability of a branch-matched reduction pilot: complete records, collective mass energies, and a self-contained interface to the inherited analytic problem. Undated manuscript, Psy02-B-v2.tex, version 2, supplied in full. Relevant records: thm:process, thm:overlap, prop:dynamic, thm:matrixunits, thm:gap, and prop:nontransfer. The inherited I3/J2 boundary remains outside this paper.

CheungEm04A

N. Cheung, Manuscript Em04-A. Em04-A-v2.tex, version 2, undated. Supplied exact vacuum-plus-single-excitation reduction, thm:reduction, lines 345–381; instrument consistency and analytical release excerpts. Enter Calculus Companion; documentary source record. A different zero-temperature XY-chain carrier.

CheungEm06B

N. Cheung, Manuscript Em06-B. Em06-B-v1.tex, version 1, undated. Supplied concentration criterion, thm:states, lines 739–806, and fluctuation and input-control records. Enter Calculus Companion; documentary source record. No scalar-to-network correspondence is asserted.

CheungVIIJ

N. Cheung, Manuscript VII-J. VII-J.tex, version 1, undated. Supplied exact no-signalling criterion, thm:nosignal, lines 373–417, and scheduled-support records. Enter Calculus Companion; documentary source record.

CheungEn01B

N. Cheung, Manuscript En01-B. En01-B-v2.tex, version 2, undated. Supplied calibrated overlap and oriented contrast records, thm:width, lines 1215–1266, and thm:contrast, lines 1518–1562. The nuisance class must be re-established for the selected experiment.

CheungEm05A

N. Cheung, Manuscript Em05-A. Em05-A-v1.tex, version 1, undated. Supplied full-transfer dephasing comparison, thm:fullNoise, lines 966–1052, and diagnostic separation, lines 1257–1298. The signed extraction channel is not identified with the present node model.

EvciRigL

U. Evci, T. Gale, J. Menick, P. S. Castro, and E. Elsen, Rigging the lottery: making all tickets winners. In Proceedings of the 37th International Conference on Machine Learning, Proceedings of Machine Learning Research 119 (2020), 2943–2952. https://proceedings.mlr.press/v119/evci20a.html.

Tsuzuku

Y. Tsuzuku, I. Sato, and M. Sugiyama, Lipschitz-margin training: scalable certification of perturbation invariance for deep neural networks. Advances in Neural Information Processing Systems 31 (2018). https://arxiv.org/abs/1802.04034.

Whitfield

J. D. Whitfield, C. A. Rodríguez-Rosario, and A. Aspuru-Guzik, Quantum stochastic walks: a generalization of classical random walks and quantum walks. Physical Review A 81 (2010), 022323. https://doi.org/10.1103/PhysRevA.81.022323. https://arxiv.org/abs/0905.2942. The published article's author order is used.

Beer

K. Beer, D. Bondarenko, T. Farrelly, T. J. Osborne, R. Salzmann, D. Scheiermann, and R. Wolf, Training deep quantum neural networks. Nature Communications 11 (2020), 808. https://doi.org/10.1038/s41467-020-14454-2. The earlier author manuscript is arXiv:1902.10445.

GKS

V. Gorini, A. Kossakowski, and E. C. G. Sudarshan, Completely positive dynamical semigroups of $N$-level systems. Journal of Mathematical Physics 17 (1976), 821–825. https://doi.org/10.1063/1.522979.

Lindblad

G. Lindblad, On the generators of quantum dynamical semigroups. Communications in Mathematical Physics 48 (1976), 119–130. https://doi.org/10.1007/BF01608499.

Watrous

J. Watrous, The Theory of Quantum Information. Cambridge University Press, 2018. Author's manuscript: https://cs.uwaterloo.ca/ watrous/TQI/.

DaviesLewis

E. B. Davies and J. T. Lewis, An operational approach to quantum probability. Communications in Mathematical Physics 17 (1970), 239–260. https://doi.org/10.1007/BF01647093.

Hoeffding

W. Hoeffding, Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association 58 (1963), 13–30. https://doi.org/10.1080/01621459.1963.10500830.

PyTorchRandom

PyTorch Contributors, Reproducibility. Official PyTorch documentation. https://docs.pytorch.org/docs/main/notes/randomness.html. Environment- and version-specific software guidance.

PyTorchArgsort

PyTorch Contributors, torch.argsort. Official PyTorch documentation. https://docs.pytorch.org/docs/main/generated/torch.argsort.html. Stable sorting preserves the input order of equal values.

PyTorchModule

PyTorch Contributors, torch.nn.Module: state_dict and load_state_dict. Official PyTorch documentation. https://docs.pytorch.org/docs/main/generated/torch.nn.Module.html. Parameters and persistent buffers are distinct from unregistered Python attributes and external manager state.