Black-Box Quantum State Preparation with Inverse Coefficients

Black-box quantum state preparation is a fundamental building block for many higher-level quantum algorithms, which is applied to transduce the data from computational basis into amplitude. Here we present a new algorithm for performing black-box state preparation with inverse coefficients based on the technique of inequality test. This algorithm can be used as a subroutine to perform the controlled rotation stage of the Harrow-Hassidim-Lloyd (HHL) algorithm and the associated matrix inversion algorithms with exceedingly low cost. Furthermore, we extend this approach to address the general black-box state preparation problem where the transduced coefficient is a general non-linear function. The present algorithm greatly relieves the need to do arithmetic and the error is only resulted from the truncated error of binary string. It is expected that our algorithm will find wide usage both in the NISQ and fault-tolerant quantum algorithms.Comment: 11 pages, 3 figure

Case study on quantum convolutional neural network scalability

One of the crucial tasks in computer science is the processing time reduction of various data types, i.e., images, which is important for different fields -- from medicine and logistics to virtual shopping. Compared to classical computers, quantum computers are capable of parallel data processing, which reduces the data processing time. This quality of quantum computers inspired intensive research of the potential of quantum technologies applicability to real-life tasks. Some progress has already revealed on a smaller volumes of the input data. In this research effort, I aimed to increase the amount of input data (I used images from 2 x 2 to 8 x 8), while reducing the processing time, by way of skipping intermediate measurement steps. The hypothesis was that, for increased input data, the omitting of intermediate measurement steps after each quantum convolution layer will improve output metric results and accelerate data processing. To test the hypothesis, I performed experiments to chose the best activation function and its derivative in each network. The hypothesis was partly confirmed in terms of output mean squared error (MSE) -- it dropped from 0.25 in the result of classical convolutional neural network (CNN) training to 0.23 in the result of quantum convolutional neural network (QCNN) training. In terms of the training time, however, which was 1.5 minutes for CNN and 4 hours 37 minutes in the least lengthy training iteration, the hypothesis was rejected.Comment: 11 pages (without references), 13 figure

Early Diagnosis of Mild Cognitive Impairment with 2-Dimensional Convolutional Neural Network Classification of Magnetic Resonance Images

We motivate and implement an Artificial Intelligence (AI) Computer Aided Diagnosis (CAD) framework, to assist clinicians in the early diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Our framework is based on a Convolutional Neural Network (CNN) trained and tested on functional Magnetic Resonance Images datasets. We contribute to the literature on AI-CAD frameworks for AD by using a 2D CNN for early diagnosis of MCI. Contrary to current efforts, we do not attempt to provide an AI-CAD framework that will replace clinicians, but one that can work in synergy with them. Our framework is cheaper and faster as it relies on small datasets without the need of high-performance computing infrastructures. Our work contributes to the literature on digital transformation of healthcare, health Information Systems, and NeuroIS, while it opens novel avenues for further research on the topic