Why Quantum Coherence Is Not Important in the Fenna–Matthews–Olsen Complex
Bibliographic record
Abstract
We develop and present an improvement to the conventional technique for solving the Hierarchical Equations of Motion (HEOM), which can reduce the memory cost by up to 75% while retaining the same (or even better) convergence rate and accuracy. This allows for a full calculation of the population dynamics of the 24-site FMO trimer for long time scales with very little effort, and we present the first fully converged, exact results for the 7-site subsystem of the monomer, and for the full 24-site trimer. We then show where our exact 7-site results deviate from the approximation of Ishizaki and Fleming [A. Ishizaki and G. R. Fleming, Proc. Natl. Acad. Sci. U.S.A., 2009, 106, 17255]. Our exact results are then compared to calculations using the incoherent Förster theory, and it is found that the time scale of energy transfer is roughly the same, regardless of whether or not coherence is considered. This means that coherence is not likely to improve the efficiency of the transfer. In fact, the incoherent theory often tends to overpredict the rates of energy transfer, suggesting that, in some cases, quantum coherence may actually slow the photosynthetic process.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".