Postponing the orthogonality catastrophe: efficient state preparation\n for electronic structure simulations on quantum devices
Bibliographic record
Abstract
Despite significant work on resource estimation for quantum simulation of\nelectronic systems, the challenge of preparing states with sufficient ground\nstate support has so far been largely neglected. In this work we investigate\nthis issue in several systems of interest, including organic molecules,\ntransition metal complexes, the uniform electron gas, Hubbard models, and\nquantum impurity models arising from embedding formalisms such as dynamical\nmean-field theory. Our approach uses a state-of-the-art classical technique for\nhigh-fidelity ground state approximation. We find that easy-to-prepare single\nSlater determinants such as the Hartree-Fock state often have surprisingly\nrobust support on the ground state for many applications of interest. For the\nmost difficult systems, single-determinant reference states may be\ninsufficient, but low-complexity reference states may suffice. For this we\nintroduce a method for preparation of multi-determinant states on quantum\ncomputers.\n
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".