Optimization of the Qubit Coupled Cluster Ansatz on Classical Computers
Bibliographic record
Abstract
Immense interest in quantum computing has prompted the development of electronic structure methods that are suitable for quantum hardware. However, the slow pace at which quantum hardware progresses forces researchers to implement their ideas on classical computers despite the obvious loss of any “quantum advantage.” As a result, the so-called q uantum-inspired methods emerge. They allow one to look at the electronic structure problem from a different angle, yet to fully exploit their capacity, efficient implementations are highly desirable. Here, we report two schemes for improving the amplitude optimization in the iterative qubit coupled cluster (iQCC) method: a variational quantum eigensolver-type approach, which is based on the qubit coupled cluster (QCC) Ansatz. Our first scheme approximates the QCC unitary as a sum of symmetrical polynomials of generators up to a given order. The resulting energy expression allows for flexible control of computational complexity via the order parameter. It also guarantees smoothness of trial energies and their derivatives, which is important for gradient-based optimization strategies. The second scheme limits the size of the expansion space in which the QCC unitary is generated. It provides better control of memory requirements but, in general, may lead to the nonsmooth variation of energy estimates upon changes in amplitudes. It can be used, however, to extrapolate energies for a given set of amplitudes toward the exact QCC value. Both schemes allow for a larger number of generators to be included in the QCC form compared to the exact formulation. This reduces the number of iterations in the iQCC method and/or leads to higher accuracy. We assess the capabilities of the new schemes to perform QCC amplitudes optimization for a few molecular systems: dinitrogen (N 2, 16 qubits), water (H 2 O, 36 qubits), and tris(2-(2,4-difluorophenyl)pyridine) iridium(III), (Ir(F 2 ppy) 3, 80 qubits).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".