Implementation of a quantum division circuit on noisy intermediate-scale quantum devices using dynamic circuits and approximate computing
Bibliographic record
Abstract
Contemporary quantum computers with tens to hundreds of physical quantum bits (qubits) are susceptible to quantum noise. As a result, running quantum circuits on these computers is error prone. In particular, deep quantum circuits with a large number of quantum gates and qubits are more likely to fail on quantum computers. In this work, we propose a design for the quantum arithmetic division operation which runs successfully on contemporary quantum computers. A quantum division circuit is needed for realization of quantum algorithms in scientific and image processing applications. While there have been a limited number of prior works on quantum division, none of them can be implemented on quantum computers due to excessive circuit complexity. We propose a different design that exploits dynamic circuits and approximate computing to deploy a quantum division circuit in quantum computers. The dynamic circuit is a new feature in recent quantum computers for midcircuit measurement in hardware. We exploit this feature to reduce the number of qubits and increase the fidelity of quantum division. We also exploit approximate computing to overcome noise in quantum hardware. We carefully tune the scope of approximation in our circuits to offer an acceptable level of accuracy. We run our division circuit on an IBM quantum computer and show that our circuit overcomes quantum noise and generates meaningful results.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".