Experimental Verification of Bell‐Type Inequalities Using Four‐Qubit Dicke States on Quantum Processors
Bibliographic record
Abstract
Abstract Testing for a violation of Bell‐type inequalities provides a standard approach to investigating nonlocal correlations in nonclassical (entangled) states. In this study, a custom measurement operator composed of a linear combination of Pauli matrices (, , and ) is constructed to examine such violations. Both theoretical and experimental analyses of Bell‐type inequality violations using two‐ and four‐qubit Dicke states implemented on quantum computers are presented. Specifically, two methods for preparing four‐qubit Dicke states—gate‐based and statevector‐based—are compared, and their performance is assessed on two IBM superconducting quantum processors, ibm_kyiv and ibm_sherbrooke . In the two‐qubit scenario, a clear violation of the CHSH inequality is observed, with a maximum Bell parameter of achieved using M3 error mitigation, closely approaching the theoretical upper bound of . For the four‐qubit case, a Dicke‐state‐specific Bell‐type inequality is applied and a maximal violation of is reported without additional mitigation using the statevector‐based approach. These findings show that while error mitigation improves outcomes in gate‐based methods, the statevector‐based approach naturally provides higher fidelity with reduced noise. This work underscores the importance of state preparation strategies and noise management in exploring quantum correlations on current quantum computing platforms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".