Short-Depth Circuits and Error Mitigation for Large-Scale GHZ-State Preparation, and Benchmarking on IBM's 127-Qubit System
Bibliographic record
Abstract
This paper conducts an evaluation of two IBM quantum systems: Quantum Eagle r3 (Sherbrooke, 127 qubits) and Falcon r8 (Peekskill, 27 qubits), with an emphasis on benchmarking these systems and their differing approaches to generating Greenberger-Horne-Zeilinger (GHZ) states, a specific type of multi-partite entangled quantum state. Our primary objective is to augment quantum fidelity via depth-reduction circuit designs. Sherbrooke's larger qubit capacity presents significant opportunities for implementing more complex algorithms, thus benefiting quantum cryptography [4], measurement-based quantum computing (MBQC) [5] and quantum simulation [6]. We introduce the Tree-based and Centred-tree-based approaches, enabling the exploitation of entangled states. Our strategies demonstrate promising potential for increasing quantum fidelity and broadening quantum applications. This work lays a firm foundation for subsequent advancements in quantum computing, highlighting the potential for heightened efficiency and versatility in future quantum systems.
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 | 0.003 |
| 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 | 0.001 | 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".