English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Decision trees are widely used in machine learning due to their simplicity inconstruction and interpretability. However, as data sizes grow, traditionalmethods for constructing and retraining decision trees become increasinglyslow, scaling polynomially with the number of training examples. In this work,we introduce a novel quantum algorithm, named Des-q, for constructing andretraining decision trees in regression and binary classification tasks.Assuming the data stream produces small increments of new training examples, wedemonstrate that our Des-q algorithm significantly reduces the time requiredfor tree retraining, achieving a poly-logarithmic time complexity in the numberof training examples, even accounting for the time needed to load the newexamples into quantum-accessible memory. Our approach involves building adecision tree algorithm to perform k-piecewise linear tree splits at eachinternal node. These splits simultaneously generate multiple hyperplanes,dividing the feature space into k distinct regions. To determine the k suitableanchor points for these splits, we develop an efficient quantum-supervisedclustering method, building upon the q-means algorithm of Kerenidis et al.Des-q first efficiently estimates each feature weight using a novel quantumtechnique to estimate the Pearson correlation. Subsequently, we employ weighteddistance estimation to cluster the training examples in k disjoint regions andthen proceed to expand the tree using the same procedure. We benchmark theperformance of the simulated version of our algorithm against thestate-of-the-art classical decision tree for regression and binaryclassification on multiple data sets with numerical features. Further, weshowcase that the proposed algorithm exhibits similar performance to thestate-of-the-art decision tree while significantly speeding up the periodictree retraining.

Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification