B. Venkatraman | Searcharxiv

arXiv2024

Parameter Estimation in Quantum Metrology Technique for Time Series Prediction

The paper investigates the techniques of quantum computation in metrological predictions, with a particular emphasis on enhancing prediction potential through variational parameter estimation. The applicability of quantum simulations and quantum metrology techniques for modelling complex physical systems and achieving high-resolution measurements are proposed. The impacts of various parameter distributions and learning rates on predictive accuracy are investigated. Modelling the time evolution of physical systems Hamiltonian simulation and the product formula procedure are adopted. The time block method is analyzed in order to reduce simulation errors, while the Schatten-infinite norm is used to evaluate the simulation precision. Methodology requires estimation of optimized parameters by minimizing loss functions and resource needs. For this purpose, the mathematical formulations of Cramer Rao Bound and Fischer Information are indispensable requirements. The impact of learning rates on regulating the loss function for various parameter values. Using parameterized quantum circuits, the article outlines a four-step procedure for extracting information. This method involves the preparation of input states, the evolution of parameterized quantum states, the measurement of outputs, and the estimation of parameters based on multiple measurements. The study analyses variational unitary circuits with optimized parameter estimation for more precise predictions. The findings shed light on the effects of normal parameter distributions and learning rates on attaining the most optimal state and comparison with classical Long Short Term Memory (LSTM) predictions, providing valuable insights for the development of more appropriate approaches in quantum computing.

quant-ph↗

arXiv2020

NukeBERT: A Pre-trained language model for Low Resource Nuclear Domain

Significant advances have been made in recent years on Natural Language Processing with machines surpassing human performance in many tasks, including but not limited to Question Answering. The majority of deep learning methods for Question Answering targets domains with large datasets and highly matured literature. The area of Nuclear and Atomic energy has largely remained unexplored in exploiting non-annotated data for driving industry viable applications. Due to lack of dataset, a new dataset was created from the 7000 research papers on nuclear domain. This paper contributes to research in understanding nuclear domain knowledge which is then evaluated on Nuclear Question Answering Dataset (NQuAD) created by nuclear domain experts as part of this research. NQuAD contains 612 questions developed on 181 paragraphs randomly selected from the IGCAR research paper corpus. In this paper, the Nuclear Bidirectional Encoder Representational Transformers (NukeBERT) is proposed, which incorporates a novel technique for building BERT vocabulary to make it suitable for tasks with less training data. The experiments evaluated on NQuAD revealed that NukeBERT was able to outperform BERT significantly, thus validating the adopted methodology. Training NukeBERT is computationally expensive and hence we will be open-sourcing the NukeBERT pretrained weights and NQuAD for fostering further research work in the nuclear domain.

cs.LG↗