ArticleScientific reports2018
Quantum ensembles of quantum classifiers.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Quantum ensembling methods for healthcare and life science.Briefings in bioinformatics · 2026Article
- Standardized quantum transistor block enables differentiable learning on gait dynamics.Scientific reports · 2026Article
- Scalable parameterized quantum circuits classifier.Scientific reports · 2024Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- On the Applicability of Quantum Machine Learning.Entropy (Basel, Switzerland) · 2023Article
- Quantum Neural Network Based Distinguisher on SPECK-32/64.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review.Archives of computational methods in engineering : state of the art reviews · 2023Review
- Predicting seismic-induced liquefaction through ensemble learning frameworks.Scientific reports · 2019Article
- A high-bias, low-variance introduction to Machine Learning for physicists.Physics reports · 2019Article
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Authors and funding
2 authors.
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Abstract
Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.