Towards a Distributed Quantum Computing Platform for Algorithm Experiments
Bibliographic record
Abstract
Distributed quantum computing (DQC) is a rapidly evolving field with its own unique challenges. Distributing a quantum algorithm involves several key steps and considerations. The steps involve decomposition at various levels of abstraction, given the underlying quantum stack and quantum network capabilities. In our DQC design explorations, we focus on the distribution at the algorithm and circuit levels. Algorithmic distribution involves distributing tasks before compilation, allowing different quantum processing units (QPUs) to receive distinct parts of an algorithm. Circuit distribution involves executing a quantum algorithm in a distributed manner at the circuit execution level using circuit and adaptive quantum technologies. If entanglement across QPUs is supported, then quantum states can be shared between qubits on remote quantum processors. This requires a specialized architecture with data and communication qubits with non-local gates such as telegates and teledata gates. This paper presents our progress towards a framework for exploring quantum distribution at the algorithm and circuit levels. Our implementation and case studies demonstrate the feasibility of our approach and show effective pathways for distributed quantum algorithm experiments.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".