.png){kind=icon}
.png){kind=icon}
Return
A kernel-based quantum random forest for improved classification
.png){kind=icon}
PRE
AI
DOI：10.1007/s42484-023-00131-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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 Nystrom 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 Nystrom-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 to obtain superior performance over QSVMs, while also requiring fewer kernel estimations.
Keywords:
Quantum machine learning
Quantum support vector machine
Quantum kernel estimation
Journal
Q
IF:
4.4
Papers:
433
Citations:
796