IEEE ATPSS-R0014903-Lecture Series on Quantum Computing-Part-4 Quantum Machine Learning
The lecture series is planned for the benefit of the research scholars, PG scholars, faculty of various academic institutions and Industry professionals across the world.
Quantum computing lecture series offer foundational and advanced insights into quantum mechanics, algorithms, and real-world applications using platforms like Qiskit and IBM Quantum.
The fourth part is related to Quantum Machine Learning
Quantum Machine Learning (QML) combines principles of quantum computing with machine learning to address computational challenges beyond the capabilities of classical systems. By exploiting quantum phenomena such as superposition and entanglement, QML aims to improve learning efficiency, optimization, and data analysis for complex problems. This lecture introduces the fundamentals of Quantum Machine Learning, including quantum data encoding, hybrid quantum–classical algorithms, and variational models. It also discusses the opportunities and limitations of implementing QML on current noisy intermediate-scale quantum (NISQ) devices. The session provides a concise overview of ongoing research, practical applications, and future prospects of QML.
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- Starts 09 February 2026 06:30 AM UTC
- Ends 13 February 2026 06:30 PM UTC
- No Admission Charge
Speakers
Guncha Malik of IBM corporation, Bangalore
Quantum Computing - Part-4 Quantum Machine Learning
Abstract: Quantum Machine Learning (QML) combines principles of quantum computing with machine learning to address computational challenges beyond the capabilities of classical systems. By exploiting quantum phenomena such as superposition and entanglement, QML aims to improve learning efficiency, optimization, and data analysis for complex problems. This lecture introduces the fundamentals of Quantum Machine Learning, including quantum data encoding, hybrid quantum–classical algorithms, and variational models. It also discusses the opportunities and limitations of implementing QML on current noisy intermediate-scale quantum (NISQ) devices. The session provides a concise overview of ongoing research, practical applications, and future prospects of QML.
Biography:
At IBM Cloud Platform, Her role is as a Senior Technical Staff Member (STSM) blends deep technical expertise with a strategic vision for protecting complex cloud ecosystems. With a Certified Information Systems Security Professional (CISSP) and Certified Cloud Security Professional (CCSP) credentials, she is specialized in secure software development practices, operational security and compliance management, ensuring that our clients' assets are safeguarded against evolving threats.
Her tenure at IBM is marked by commitment to information security, ethical AI and quantum computing, steering the solutions towards responsible and secure technological integration, while upholding the integrity of data usage standards. Building on the skills obtained with the IBM Quantum Ambassador credential, she is driving Cloud strategy for Quantum Safe Cryptography adoption.
Email:
Address:Bangalore
Sathish Krishna of IBM corporation, USA
Quantum Computing - Part-4 Quantum Algorithms
Abstract: Quantum Machine Learning (QML) combines principles of quantum computing with machine learning to address computational challenges beyond the capabilities of classical systems. By exploiting quantum phenomena such as superposition and entanglement, QML aims to improve learning efficiency, optimization, and data analysis for complex problems. This lecture introduces the fundamentals of Quantum Machine Learning, including quantum data encoding, hybrid quantum–classical algorithms, and variational models. It also discusses the opportunities and limitations of implementing QML on current noisy intermediate-scale quantum (NISQ) devices. The session provides a concise overview of ongoing research, practical applications, and future prospects of QML.
Biography:
Sathish Krishna Anumula working for IBM corporation USA, is a seasoned Digital Transformation Architect and Technology Strategist with over 22 years of experience driving innovation in Closed Loop Manufacturing (Product Lifecycle Management, Supply Chain Management, Manufacturing) across automotive, medical devices, semiconductors, and hi-tech electronics. With advanced degrees in Electronics Engineering and IT & Operations, he blends deep technical expertise with strategic business insight. Having worked with industry leaders like Siemens, Microsoft and IBM, Sathish has led the Architecture, Design and Deployment of complex enterprise systems, championed sustainable manufacturing and supply chain practices, and delivered successful commercial solutions. His core expertise spans enterprise architecture, Technology Strategy Hybrid Cloud, IIoT, AI/ML for Manufacturing and also Emerging Technologies such as Quantum and Neuromorphic computing. He is also a published author of multiple textbooks and research articles, a peer reviewer for top journals, and a frequent keynote speaker.
Core Skills & Capabilities
- Enterprise Architecture & Business Strategy
- Cloud Platforms - IBM, RedHat, AWS, Azure, GCP
- IIoT & Smart Manufacturing Platforms for Industry 4.0 +
- Closed Loop Manufacturing (Product Lifecycle Management, Manufacturing & Supply Chain Trio)
- AI/ML, Generative AI and Agentic AI Applications
- Quantum & Neuromorphic Computing (Research & Advisory)
- Blockchain, Tokenization and DeFi
- Systems Integration, API Management & IBM Cloud Paks (Data, Automation, Security, Observability)
- Microservices, DevOps, CI/CD, Containerization (Docker, Kubernetes, RedHat OCP)
- Data Architecture, Analytics & BI (Oracle, MSSQL, SAP BI/HANA)
- Cybersecurity, Compliance & Governance
Email:
Address:Detrioit, United States
Agenda
- Welcome address by ATPP Subsection Chair
- Introduction of speakers
- Session by speakers
- Concluding Remarks
- Virtual Memento distribution
- Vote of thanks
IEEE Ananthapuramu Subsection : ieeehyd.org
IEEE Hyderabad Section : ieeehyd.org