Bibliographic record
Abstract
Quantum computing uses fundamentally different ways of information processing compared to traditional computing systems such as the use of qubits (quantum bits) and the quantum properties of subatomic particles such as interference, entanglement and superposition to extend the computational capabilities to hitherto unprecedented levels. Although quantum computing systems promise to provide exponential performance benefits in processing, the field is still in an embryonic phase with active ongoing research and development. The efficacy of quantum computing for important verticals such as healthcare—where quantum computing can enable important breakthroughs such as developing drugs, quick DNA sequencing, processing big healthcare data, and performing other compute-intensive tasks—is not yet fully explored. Keeping in view, this article explores this area and analyzes the potential of quantum computing for healthcare systems. We explore various dimensions within healthcare ecosystem where quantum computing could introduce new possibilities through higher computational speed to perform complex healthcare computations. Implementations of quantum computing in the healthcare scenarios have their own unique set of requirements. And therefore, we not only identify those key elements but also present a taxonomy of existing literature around quantum-based healthcare ecosystem, distinguishing cryptography in classical vs modern era along the way. Finally, we explore current challenges, their causes, and future research directions in implementing quantum computing systems in healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".