Bibliographic record
Abstract
We provide an efficient and general route for preparing non-trivial quantum states that are not adiabatically connected to unentangled product states.Our approach is a variant of the 'Quantum Approximate Optimization Algorithm' (QAOA) [E.Farhi et al., arXiv:1411.4028] and is experimentally realizable on near-term quantum simulators of synthetic quantum systems.As proof of concept, our approach yields explicit protocols which prepare (i) the Greenberger-Horne-Zeilinger (GHZ) state, (ii) a quantum critical state, and (iii) a topologically ordered state, all with perfect fidelity and time that scales as O(L), where L is the linear dimension of the system.The protocol is additionally able to prepare the ground states of antiferromagnetic Heisenberg chains with very good fidelities.Besides being practically useful, our results also illustrate the utility of QAOA-type circuits as variational wavefunctions for non-trivial states of matter.B Explicit nonuniform unitary circuit for preparing the GHZ state 10 C Energy optimization plot and optimal angles for preparing critical state at p = L/2 11 D A Conjecture and Numerical Support 12 E Numerical verification of preparation of toric code ground state 14 F Effect of errors on QAOA state preparation 16 References 16
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.922 | 0.886 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".