publications

I present a fully index-free variant of Grover’s search algorithm that identifies target values in an unstructured database solely by their content, without requiring any position-specific oracle or prior knowledge of their indices. The method uses QROM state encoding to load an unstructured database into superposition and marks entries directly by comparing their encoded value against the target. I demonstrate the approach on two different data types: (1) unsorted numeric arrays and (2) single nucleotide search in DNA sequences. Exact state-vector simulations up to N=1024 elements on real human genomic data achieve success probabilities exceeding 99% after the optimal number of Grover iterations, confirming that the quadratic speedup is preserved despite the QROM overhead. The results demonstrate successful target identification on an arbitrarily large unsorted array. The complete gate-level construction is fully reproducible from this document, with code and datasets publicly available.

A child dies of pneumonia every 43 seconds. In 2017, Pneumonia killed more than 808 000 children under the age of 5, or around 2,000 every day, accounting for 15% of all deaths of children under 5 years. Pneumonia kills more children than any other infectious disease. People at-risk for pneumonia also include adults over the age of 65 and people with preexisting health problems. Globally, an estimated 450 million cases of pneumonia are recorded each year, resulting in nearly 4 million deaths. Almost all of these deaths are preventable. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. This study proposes an efficient dual-path deep learning framework for automated pneumonia detection from chest X-ray images to support clinical decision-making. The model employs two parallel streams: one processing lung-segmented images and the other using full chest X-ray images. Each stream concatenates the results and goes to another pipeline consists of an ensemble of ResNet18, DenseNet121, and InceptionV3 architectures, with final predictions combined via ensemble averaging. Evaluated on the PneumoniaCXR dataset comprising 33,547 chest X-ray images across three classes (Normal, Pneumonia, and No Opacity/No Normal), the proposed dual-path model achieved a test accuracy of 96% on over 6,000 held-out images. These results demonstrate that the approach significantly outperforms individual baseline models and offers a reliable, interpretable tool to assist radiologists in improving diagnostic accuracy and speed, especially in high-burden and low-resource environments.