Generating and Testing a Many-qubit Entangled State for Controlled Quantum Teleportation
Bibliographic record
Abstract
Multipartite entangled states are essential for controlled quantum teleportation (CQT). CQT enables secure and conditional quantum information transfer among multiple parties. Recently, the six-qubit “tetrahedron” state was identified as a novel candidate for enabling CQT of a two-qubit state by Z. M. McIntyre and W. A. Coish (2024). Despite its theoretical promise, this state has yet to be experimentally realized. We will employ IBM Qiskit, a quantum computing software package, to perform a classical simulation of the tetrahedron state, modeling realistic conditions that include decoherence, state preparation errors, and measurement imperfections. By examining the influence of noise on entanglement and teleportation fidelity, we aim to gain insights into the state’s feasibility for near-term quantum hardware. This approach involves constructing the quantum circuit for the tetrahedron state, incorporating realistic noise models, and analyzing the cumulative effect of errors at each gate operation. Our ultimate goal is to identify critical noise thresholds that limit the state’s performance and inform future hardware implementations. The insights from these simulations will guide experimental efforts to realize the six-qubit tetrahedron state and advance controlled quantum teleportation protocols for multi-qubit systems.
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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 | 0.002 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".