A kernel-based quantum random forest for improved classification
This work addresses limitations in quantum machine learning for classification tasks, offering a novel ensemble method that could benefit researchers in quantum computing and machine learning, though it appears incremental as it builds on existing quantum models.
The authors tackled the challenge of improving quantum machine learning models by developing a quantum random forest (QRF) classifier that extends quantum support vector machines with kernel functions and a low-rank approximation to limit overfitting. They achieved lower sampling complexity compared to quantum kernel estimation and demonstrated superior performance over quantum support vector machines while requiring fewer kernel estimations.
The emergence of Quantum Machine Learning (QML) to enhance traditional classical learning methods has seen various limitations to its realisation. There is therefore an imperative to develop quantum models with unique model hypotheses to attain expressional and computational advantage. In this work we extend the linear quantum support vector machine (QSVM) with kernel function computed through quantum kernel estimation (QKE), to form a decision tree classifier constructed from a decision directed acyclic graph of QSVM nodes - the ensemble of which we term the quantum random forest (QRF). To limit overfitting, we further extend the model to employ a low-rank Nyström approximation to the kernel matrix. We provide generalisation error bounds on the model and theoretical guarantees to limit errors due to finite sampling on the Nyström-QKE strategy. In doing so, we show that we can achieve lower sampling complexity when compared to QKE. We numerically illustrate the effect of varying model hyperparameters and finally demonstrate that the QRF is able obtain superior performance over QSVMs, while also requiring fewer kernel estimations.