Quantum Vision Clustering

Unsupervised visual clustering has recently received considerable attention. It aims to explain distributions of unlabeled visual images by clustering them via a parameterized appearance model. From a different perspective, the clustering algorithms can be treated as assignment problems, often NP-hard. They can be solved precisely for small instances on current hardware. Adiabatic quantum computing (AQC) offers a solution, as it can soon provide a considerable speedup on a range of NP-hard optimization problems. However, current clustering formulations are unsuitable for quantum computing due to their scaling properties. Consequently, in this work, we propose the first clustering formulation designed to be solved with AQC. We employ an Ising model representing the quantum mechanical system implemented on the AQC. Our approach is competitive compared to state-of-the-art optimization-based approaches, even using of-the-shelf integer programming solvers. Finally, we demonstrate that our clustering problem is already solvable on the current generation of real quantum computers for small examples and analyze the properties of the measured solutions.

Two-Dimensional Quantum Material Identification via Self-Attention and Soft-labeling in Deep Learning

In quantum machine field, detecting two-dimensional (2D) materials in Silicon chips is one of the most critical problems. Instance segmentation can be considered as a potential approach to solve this problem. However, similar to other deep learning methods, the instance segmentation requires a large scale training dataset and high quality annotation in order to achieve a considerable performance. In practice, preparing the training dataset is a challenge since annotators have to deal with a large image, e.g 2K resolution, and extremely dense objects in this problem. In this work, we present a novel method to tackle the problem of missing annotation in instance segmentation in 2D quantum material identification. We propose a new mechanism for automatically detecting false negative objects and an attention based loss strategy to reduce the negative impact of these objects contributing to the overall loss function. We experiment on the 2D material detection datasets, and the experiments show our method outperforms previous works.

Defining Quantum Neural Networks via Quantum Time Evolution

This work presents a novel fundamental algorithm for for defining and training Neural Networks in Quantum Information based on time evolution and the Hamiltonian. Classical Neural Network algorithms (ANN) are computationally expensive. For example, in image classification, representing an image pixel by pixel using classical information requires an enormous amount of computational memory resources. Hence, exploring methods to represent images in a different paradigm of information is important. Quantum Neural Networks (QNNs) have been explored for over 20 years. The current forefront work based on Variational Quantum Circuits is specifically defined for the Continuous Variable (CV) Model of quantum computers. In this work, a model is proposed which is defined at a more fundamental level and hence can be inherited by any variants of quantum computing models. This work also presents a quantum backpropagation algorithm to train our QNN model and validate this algorithm on the MNIST dataset on a quantum computer simulation.