Recent Advances in Quantum Machine Learning
Marvels of QLM!
We produce around 2.5 exabytes of data each day. This is analogous to the data on approximately 5 million laptops. How is it possible to process such big data? Well, here’s the answer!
Quantum computing is a branch that focuses on developing computer technology based on the principles of quantum theory. When this is linked to machine learning we get an interdisciplinary branch of science called Quantum Machine Learning.
Here are a few recent advances in the field of Quantum Machine Learning.
1. Quantum Dimensionality Reduction -
In QML, dimensionality reduction means to learn a mapping function f:x →y, where x represents the original data points and y represents the point after mapping. In other words, it means reducing the number of input data in training data. Dimensionality reduction is important in order to improve the accuracy of identification and to reduce errors.
There are various techniques to carry out dimensionality reduction. Some of these are:
a. Principal Component Analysis (PCA)
This algorithm aims at mapping high-dimensional data to low-dimensional space using linear projection. QPCA on the other hand involves the quantum phase estimation to deal with eigenvectors and eigenvalues providing exponential speedup over any classical algorithm.
b. Linear Discriminant Analysis (LDA)
This algorithm aims at maximizing the distance between classes and minimizing distance within classes. QLDA speeds up the functions of the LDA algorithm.
c. Generalized Discriminant Analysis (GDA)
This algorithm is used to extract non-linear features from data. It reduces the number of input features and also increases classification accuracy by selecting the most discriminating features.
2. Quantum support vector machine -
SVM algorithm is a machine learning algorithm that is used to solve problems involving binary classification. SVM uses a hyperplane to predict the classification of a particular observation, from a dataset, based on the training given to it. QSVM completes the task of SVM at a much faster rate. Moreover, QSVM is one of the algorithms which has already been implemented on quantum computing hardware.
3. Quantum K-means clustering -
This algorithm can be explained by dividing it into parts. In clustering, unlabeled data is automatically divided into classes based on similarities in their features. Euclidean distance is the measure of the distance between the centroids of the clusters and data points. This is used to determine the similarity between data, i.e., lesser the distance, more the similarity. In the K-means algorithm, the number of classes should be given before the classification begins. Thus, to sum up, the K-means clustering algorithm is used for the classification of data into a certain number of classes based on their similarities. When quantized, the process occurs at an exponential rate.
4. Quantum neural networks -
This is a machine learning algorithm that is a combination of artificial neural networks and quantum computing. Though the functions of quantum neural networks are not fully realized, the main aim is to compare their efficiency with the classical neural networks.
With the growing use of technology, it is quite clear that Quantum Machine Learning has a great scope for research. Though it is an upcoming branch, many applications have been established. It holds many startup opportunities for future engineers.
