AI

Quantum K-nearest neighbors (QkNN) is an advanced variant of the classical KNN algorithm that utilizes quantum computing to enhance performance and accuracy. By leveraging quantum parallelism and Grover's search algorithm, QkNN reduces the time complexity from O(N) to O(√N) for finding nearest neighbors. This approach involves encoding data into quantum states and performing quantum searches, enabling faster and more accurate classification. QkNN is particularly promising in fields such as finance, cybersecurity, and artificial intelligence. As quantum computing technology advances, QkNN is poised to significantly impact machine learning and data analysis, offering a powerful tool for processing and analyzing large datasets efficiently. lg...

Image classification plays a crucial role in many machine-learning applications. Numerous classification techniques based on quantum machine learning have been reported recently. In this use case, we deploy the Quanvolutional Neural Network—a hybrid quantum-classical image classification technique inspired by the convolutional neural network, which has the potential to outperform current image processing techniques. Users can load the train/val/test folders and train/test the model similarly to classical machine learning models. This hybrid approach leverages quantum computing to enhance feature extraction and classification accuracy, paving the way for advanced image processing capabilities. lg...

K-mean is the popular unsupervised machine learning algorithm, given M points and k (number of clusters), this algorithm will find k centroids, and then predict the label's new points by comparing the distance between the new point with the centroids. The new points belong to the cluster whose centroid is nearest to the new point. The Quantum K-mean is an enhanced version of the K-mean by replacing the normal Euclidean distance function (O(N^2)) with the quantum Euclidean distance function (O(log(N)) (N is dimensional of point).lg...

Blueqat introduces a new AutoQAOA GUI tool which helps in solving certain optimization problems without having to write the code. A readily available graphical version takes some inputs and gives a wide range of outputs like the matrix, graph diagram, the probability distribution of the states, the optimization steps, the backend python code and the circuit in return.lg...