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Quantum models predict lung cancer using limited training data in preliminary tests (opens in a new tab)
medicalxpress.com · 2026-11-02
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Quantum models predict lung cancer using limited training data in preliminary tests
medicalxpress.com · 2026-11-02
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Source study identified.
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Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques
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Claim 1 of 6Not checkedD-Wave's annealing quantum computers used in a quantum support vector machine framework achieved the highest reported accuracy in the main evaluation, reaching 94.31% in simulation and 91.90% on quantum hardware.View evidenceHide evidence
As stated94.31% in simulation; 91.90% on quantum hardware
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Claim 2 of 6Not checkedA research team led by Arslan Munir evaluated quantum machine learning approaches in simulation and on quantum hardware using symptom and risk-factor data for lung cancer prediction under limited training data.View evidenceHide evidence
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Claim 3 of 6Not checkedThe study, published in Discover Quantum Science, provided a common framework for evaluating different quantum machine learning methods under the same data preparation and testing conditions.View evidenceHide evidence
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Claim 4 of 6Not checkedThe researchers used a publicly available dataset with 309 records and, after generating synthetic examples to balance categories, produced an experimental dataset of 540 samples.View evidenceHide evidence
As stated309 records; 540 samples
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Claim 5 of 6Not checkedThey trained the models on 108 samples and tested them on the remaining 432 in each of five evaluation rounds.View evidenceHide evidence
As stated108 training samples; 432 test samples; five evaluation rounds
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Claim 6 of 6Not checkedThe researchers and article emphasize that the findings are preliminary, that the dataset used symptoms and risk factors rather than images, pathology, lab results, or cancer-stage information, and that future work should test larger multi-institution datasets and assess interpretability.View evidenceHide evidence
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Method layer
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Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques
Discover Quantum Science · 2026
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Papers considered
The selected paper, plus nearby candidates.
Crossref, Europe PMC, PubMed · 40 candidate papers
Inferring lung cancer under limited learning data through gate-based and annealing-based quantum techniques
Discover Quantum Science · 2026 · Crossref
Observation of a spin-textured nematic Kondo lattice.
2026 · Europe PMC
A migration framework for post quantum cryptography in 6G telecommunications
Discover Telecommunications · 2026 · Crossref
In-silico screening of anti-cancer natural compounds targeting NF-κB as identification of potential therapeutic inhibitor.
2026 · Europe PMC
Cross-material operating windows of electric-field programmable Rashba qubits for frequency allocation and leakage control
Discover Quantum Science · 2026 · Crossref
A comprehensive review on oxygen defect rich catalysts for low-temperature CO2 hydrogenation to methane and alcohols.
2026 · Europe PMC
And 34 more candidates considered.