Study on the role of electromagnetic waves in quantum entanglement and quantum state transport based on constrained optimization algorithm
Bibliographic record
Abstract
In the current fields of quantum information processing and quantum computing, fast and accurate quantum state manipulation and preparation have been of keen interest to researchers, and their potential applications are mainly in quantum measurement, quantum information, quantum communication, and quantum sensing.In this paper, the Hilbert space of a bipartite state system is unfolded by four Bell state entanglement bases and the result is projected to the subsystem to obtain a mixed state.A quantum approximation algorithm is proposed to provide a solution to the combinatorial optimization problem, and based on the workflow of the quantum approximation optimization algorithm, an improvement is proposed to the quantum approximation optimization algorithm to solve the constrained problem using the quadratic unconstrained binary optimization method.Based on the theory of cavity magnetism, the hybrid quantum system model is constructed, and the calculation method of Hamiltonian quantity is proposed.Combined with the quantum entanglement optimal path calculation of UQAOA algorithm, the optimal value of time-microwave entanglement ac E is obtained at r=0.234, so the compression parameter r=0.2 is used in the calculation.Based on the UQAOA algorithm for the analysis of the transmission characteristics of the generated OMA wave in air and the transmission optimization problem, the simulation obtains the reflection coefficient is slightly lower than that of the test, and the maximal error error is controlled at ±7.5dB around, and the two results are basically in agreement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".