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Ensembles of quantum classifiers (pp0181-0209)
Emiliano Tolotti, Enrico Zardini, Enrico Blanzieri, and
Davide Pastorello
doi:
https://doi.org/10.26421/QIC24.3-4-1
Abstracts:
In the current era, known as Noisy Intermediate-Scale Quantum (NISQ),
encoding large amounts of data in the quantum devices is challenging and
the impact of noise significantly affects the quality of the obtained
results. A viable approach for the execution of quantum classification
algorithms is the introduction of a well-known machine learning
paradigm, namely, the ensemble methods. Indeed, the ensembles combine
multiple internal classifiers, which are characterized by compact sizes
due to the smaller data subsets used for training, to achieve more
accurate and robust prediction performance. In this way, it is possible
to reduce the
qubit
requirements with respect to a single larger classifier while achieving
comparable or improved performance. In this work, we present an
implementation and an extensive empirical evaluation of ensembles of
quantum instance-based classifiers for binary classification, with the
purpose of providing insights into their effectiveness, limitations, and
potential for enhancing the performance of basic quantum models. In
particular, three classical ensemble methods and three quantum
instance-based classifiers have been taken into account here. Hence, the
scheme that has been implemented (in Python) has a hybrid nature. The
results (obtained on real-world
datasets)
have shown an accuracy advantage for the ensemble techniques with
respect to the single quantum classifiers, and also an improvement in
robustness. In fact, ensembles have proven effective not only in
mitigating unsuitable data
normalizations
but also in reducing the impact of noise on quantum classifiers,
enhancing their stability.
Key Words:
quantum computing, quantum
machine learning, ensemble methods, quantum classifiers, binary
classification |
