Browsing by Author "Manjunath, T.D."

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Quantum learning and its related applications for the future
(IGI Global, 2023) Bhowmik, B.; Manjunath, T.D.
Recent advances in high-performance computing have been rapid. On the contrary, experts also know that the Moore's Law prediction of the number of transistors on microchips that would double every 18 months is almost saturated. This calls for new techniques to enhance computational power. Quantum computing is a possible solution that uses quantum mechanical phenomena and employs quantum algorithms to improve performance (accuracy, speed). The emerging technology has many interesting potential applications, including quantum machine learning, quantum computational chemistry, post quantum cryptography, etc. The complexity of applications is ever-increasing. Quantum computing amalgamates various classical machine and reinforcement learning in multiple ways to address different challenges of many complex applications. The state-of-the-art reviews on existing works in the domain show that new learning methods can enhance the achieved performance by quantum computing. The chapter thus provides an overview of quantum learning, its applications, research challenges, and future trends. © 2023, IGI Global. All rights reserved.
Quantum Machine Learning and Recent Advancements
(Institute of Electrical and Electronics Engineers Inc., 2023) Manjunath, T.D.; Bhowmik, B.
Quantum Computing is a fastly growing area with many applications, including quantum machine learning (QML). Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning enhances the performance in multiple ways. This paper studies different aspects of quantum machine learning. It introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area. We look at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc. Finally, we present the literature survey to encourage researchers and academicians. Â© 2023 IEEE.
Quantum-Enhanced Deep Q Learning with Parametrized Quantum Circuit
(Institute of Electrical and Electronics Engineers Inc., 2024) Manjunath, T.D.; Bhowmik, B.
Quantum Computing (QC) is fastly growing to replace current computing systems for many high-performance applications. Due to the rapid increase of computational power, machine learning models based on artificial neural networks (ANN) have become highly effective. Even though classical machine learning models have been performing well, quantum computing with machine learning, i.e., Quantum Machine Learning (QML), will enhance the performance in multiple ways. Subsequently, Deep Q learning is a prominent approach to reinforcement learning used in solving complex applications for a desired performance. But this performance can be improved using a quantum computer. This paper proposes a quantum-enhanced Deep Q Learning algorithm to see its advantages over its classical counterparts. We implement our approach using parametrized quantum circuit (PQC). The evaluation of the proposed method achieves the metrics rewards and episodes up to 500 points and 1200, respectively. The performance shows that it collects roughly four times more rewards in a given time and takes significantly fewer episodes to converge compared to the classical approach. Â© 2024 IEEE.
QuSAF: A Fast ATPG for SAFs in VLSI Circuits Using a Quantum Computing Algorithm
(Institute of Electrical and Electronics Engineers Inc., 2022) Manjunath, T.D.; Bhowmik, B.R.
The gate count in semiconductor chips is overgrowing. On the contrary, the feature size of the chip is continuously decreasing, resulting in higher design complexity. Consequently, circuit components in chips are exposed to various faults. A stuck-at fault is mostly addressed in circuits. We need good test patterns that trigger the defects to test the stuck-at marks. In large combinational circuits, the search space for test patterns grows exponentially with the number of inputs. Therefore an efficient test pattern generation technique is needed. An ATPG method discussed in the literature provides a time complexity of O(n) or more with the search space size n. This paper presents a fast ATPG technique named "QuSAF"that employs a Quantum Computing algorithm (QCA). The proposed QuSAF technique converts ATPG into Boolean Satisfiability (SAT) and then solves the resultant SAT expression using Grover's Search Algorithm (GSA). The proposed approach generates the test patterns for a circuit by O(âˆšn) time. Experiments are performed with various basic logic gates to show the effectiveness of the proposed technique. Â© 2022 IEEE.