Bridging the critical gap between complex genomic data and actual clinical practice, this essential volume delivers the cutting-edge AI methodologies, expert bioinformatics insights, and practical case studies needed to unlock truly personalized medicine.
Modern biological computation requires both increasing speed and accuracy for applications ranging from genomics to personalized medicine. As the need for this technology grows, classical computing methods of tackling problems start to become unsustainable, both at a scalability and energy level. This book investigates the convergence of quantum computing, bioinformatics, and environmental sustainability for modern applications. It argues that bioinformatics and biomedical discovery will not just be quantum-fast, but ecologically sustainable and morally framed. The book contends that although quantum computing offers exponential acceleration of data analysis and modeling in life sciences, these advantages would need to be built with deliberate consideration of energy expenses, hardware sustainability, and broader impacts on society. Using an organized, cross-disciplinary framework of how quantum bioinformatics can be built and implemented sustainably, this volume examines how quantum computing’s unprecedented capabilities will revolutionize bioinformatics and biomedical innovation. The book serves as an invitation to action and an instruction manual for scientists, technologists, and policymakers poised to inform the future of medicine in an era of quantum possibility.
Table of Contents:
Preface xxi
1 Eco-Qubit Architectures: Energy-Optimized Quantum-Classical Hybrid Algorithms for Low-Carbon Genomic Variant Analysis 1
Kiran Siripuri, Rajanikanth Aluvalu and Mallellu Sai Prashanth
1.1 Introduction 2
1.2 Background and Motivation 3
1.3 Framework Overview: Eco-Qubit Architectures 5
1.4 Core Innovations and Techniques 7
1.5 Benchmarking and Results 10
1.6 Carbon Footprint Index (CFI): Environmental Metrics 12
1.7 Case Study: Eco-Qubit Variant Calling in Practice 14
1.8 Future Directions and Discussion 16
1.9 Conclusion 19
1.10 Acknowledgements 19
2 The Quantum Leap in Bioinformatics: Rethinking Biological Computation Beyond Classical Limits 21
Sanjeev Prakashrao Kaulgud, Abhinandan Shirahatti, Mrutyunjaya M. S., Madhusudhan M. V. and Afroz Pasha
2.1 Introduction 22
2.2 Limitations of Classical Bioinformatics 23
2.3 Basics of Quantum Computing for Bioinformatics 25
2.4 Applications of Quantum Computing in Bioinformatics 27
2.5 Challenges and Limitations of Quantum Bioinformatics 29
2.6 Future Directions and Opportunities in Quantum
2.7 Integrating Sustainability Into Quantum Bioinformatics 33
2.8 Case Study-1: Quantum Protein Folding with Minimal Energy Footprint 35
2.9 Case Study-2: Quantum-Assisted Drug Discovery Using Variation Quantum Eigensolver (VQE) 37
2.10 Conclusion 39
3 Green Algorithms for Biomedical Data Processing: Towards Sustainable Quantum Bioinformatics 43
Usha Rani R.E.
3.1 Introduction 44
3.2 Background Concepts 45
3.3 Green Algorithms: Concepts and Metrics 51
3.4 Quantum Algorithms for Biomedical Data 54
3.5 Case Studies and Applications 57
3.6 Challenges 61
3.7 Roadmap to Sustainable and Scalable Quantum Bioinformatics 63
3.8 Conclusion 69
4 Quantum Machine Learning in Systems Biology 73
Sanjeev Prakashrao Kaulgud, Prabhuraj Metipatil, Vishwanath Hulipalled, Siddanagouda Somanagouda Patil and Sonia Maria Dsouza
4.1 Introduction 74
4.2 Fundamentals of Quantum Machine Learning 78
4.3 Role of Quantum Machine Learning in Systems Biology 84
4.4 Case Study 1: Quantum Machine Learning for Cancer Subtype Classification 89
4.5 Case Study 2: Quantum Neural Networks for Protein Structure Prediction 91
4.6 Conclusion 93
5 Quantum-Guided Distillation of Biomedical Transformers: A Green Hybrid Framework for Sustainable Clinical NLP 97
Karthik B. U., Mrutyunjaya M. S. and Vishwanath Desai
5.1 Introduction 98
5.2 Literature Survey 100
5.3 Proposed Method 102
5.4 Results and Discussion 113
5.5 Conclusion and Future Work 121
6 Quantum Secure Healthcare Data: A Comprehensive Analysis of Emerging Threats and Advanced Protection Technologies 125
Johan Daniel M., Naresh Rathod and Tintu Vijayan
6.1 The Current Landscape of Healthcare Data Security 126
6.2 Emerging Security Technologies 128
6.3 AI Ethics and Bias Mitigation 133
6.4 Current Threat Landscape: 2025 Security Developments 134
6.5 Ransomware Pandemic in Healthcare: The 2025 Crisis Unprecedented Attack Escalation 135
6.6 Precision Medicine and Genomic Data Security 136
6.7 5G Networks and Healthcare Security Architecture 138
6.8 Supply Chain Security Crisis of Medical Devices 139
6.9 Regulatory Development Process and Compliance Diction 140
6.11 Health Information Exchange Vulnerabilities 143
6.12 Advanced Threat Intelligence and Detection Systems 144
6.13 Emerging Technologies and Future Security Paradigms 145
6.14 Regulatory Evolution and Global Governance 146
6.15 Conclusion 147
7 Energy-Optimized Quantum State Encoding Methodologies for High-Dimensional, Heterogeneous Biological Big Data 151
Pavithra G., Ashwini S.S., Hamsaveni M. and Salma Itagi
7.1 Introduction 152
7.2 Characteristics of Biological Big Data and Energy Constraints 154
7.3 Mathematical Intuition of Quantum Encoding Schemes 155
7.4 Comparative Energy Analysis: Classical vs. Quantum Approaches 159
7.5 Case Studies in Biological Big Data 162
7.6 Methods and Experimental Section 163
7.7 Challenges and Open Problems 166
7.8 Future Directions 167
7.9 Conclusion 167
8 Green Quantum Computing Approaches in Bioinformatics and Precision Medicine 171
Nikitha K. and Renushree S.
8.1 Introduction 172
8.2 Environmental Challenges in Classical Bioinformatics 174
8.3 Foundations of Green Quantum Computing 178
8.4 Green Quantum Architectures for Bioinformatics 181
8.5 Applications in Precision Medicine 186
8.6 Ethical, Policy, and Sustainability Considerations 187
8.7 Challenges and Implementation Barriers 189
8.8 Future Research Directions 191
8.9 Conclusion 193
9 Quantum-Enhanced Bioinformatics for Early Disease Detection and Personalized Treatment Planning 197
Nikitha K. and Renushree S.
9.1 Introduction 198
9.2 Classical Bioinformatics 201
9.3 Quantum-Enhanced Bioinformatics Framework 204
9.4 Applications in Early Disease Diagnosis 210
9.5 Quantum-Enabled Personalized Medicine 213
9.6 Sustainability and Responsible Innovation 216
9.7 Implementation Challenges 218
9.8 Future Research Directions 220
9.9 Conclusion 223
10 Blueprint for a Sustainable Quantum Bioinformatics Ecosystem 227
Sanjeev Prakashrao Kaulgud, Mrutyunjaya M.S., Sonia Maria D'Souza, Prabhuraj Metipatil, Vishwanath Hulipalled and Siddanagouda Somanagouda Patil
10.1 Introduction 228
10.2 Foundations of Quantum Bioinformatics 230
10.3 Sustainability Principles in Quantum Bioinformatics 232
10.4 Architecture of a Sustainable Quantum Bioinformatics Ecosystem 235
10.5 Quantum Algorithms and Computational Workflows 237
10.6 Data Management, Security, and Ethical Governance 239
10.7 Applications and Case Studies in Sustainable Life Sciences 241
10.8 Challenges, Workforce Development, and Policy Considerations 243
10.9 Policy, Regulation, and Risk Mitigation Strategies 245
10.10 Future Roadmap 246
10.11 Conclusion 247
11 Quantum-Enhanced Protein Folding and Drug Discovery with Energy-Aware Algorithms 251
Nikitha K., Renushree S. and Ajithkumar M.
11.1 Introduction 252
11.2 Fundamentals of Protein Folding and Drug Discovery 259
11.3 Quantum Computing Foundations for Biomolecular Problems 262
11.4 Quantum Algorithms for Protein Folding 265
11.5 Quantum-Enhanced Drug Discovery Pipelines 269
11.6 Energy-Aware Quantum Algorithms 271
11.7 Sustainability Implications in Drug Discovery 274
11.8 Case Studies and Experimental Implementations 276
11.9 Challenges and Open Research Issues 278
11.10 Future Research Directions 279
11.11 Conclusion 281
12 Ethics, Equity, and Ecological Intelligence in Quantum Bioinformatics: A Theoretical Framework for Responsible Life Science Innovation 285
Anubhab Parashar, Tintu Vijayan and Jayanthi Kamalasekaran
12.1 Introduction 286
12.2 Conceptual Foundations of Quantum Bioinformatics 287
12.3 Ethical Dimensions of Quantum Bioinformatics 289
12.4 Equity and Justice in Quantum Bioinformatics 290
12.5 Ecological Intelligence and Sustainability Considerations 294
12.6 An Integrated Ethical–Equitable–Ecological Framework for Quantum Bioinformatics 296
12.7 Conclusion 299
12.8 Future Directions and Open Theoretical Challenges 300
Bibliography 301
13 Quantum-Secure and Sustainable Health Data: Integrating Privacy, Robustness, and Long-Term Viability in Bioinformatics-Driven AI Systems 303
Vineetha B.
13.1 Introduction 304
13.2 Background Study 306
13.3 Proposed Research: An Integrated Framework Based on the PRV Triad 311
13.4 Test and Result Analysis 322
13.5 Conclusion 331
References 333
Index 335
About the Author :
Raghavendra M. Devadas, PhD is an Assistant Professor at the Manipal Institute of Technology at the Manipal Academy of Higher Education, Bengaluru, Karnataka, India. He has published two books, filed two patents, and received two grants. His areas of interest include machine learning, software engineering, fuzzy logic, and databases.
Preethi, PhD is an Assistant Professor in the Department of Information Technology at the Manipal Institute of Technology with more than 17 years of teaching experience. She has more than 35 publications in international journals and conferences of repute. Her research interests include computer architecture, IoT, cybersecurity, and image processing.
Praveen Gujjar, PhD is an Associate Professor and Area Head of Business Analytics in the CMS Business School at Jain University with more than 16 years of teaching experience. He has authored numerous publications in reputed journals, holds 86 patents, and has secured major research grants from multiple entities. He specializes in data visualization, predictive analytics, and prescriptive analytics.
Sowmya T., PhD is an Assistant Professor at the Manipal Institute of Technology. She received her Ph.D. from Christ University in Bengaluru. Her current research interests include network security.
Yashaswini K.A., PhD is an experienced academician with more than 16 years of teaching experience in Computer Science and Engineering. She serves as an Assistant Professor at the Manipal Institute of Technology in Bengaluru under the Manipal Academy of Higher Education. Her areas of expertise include artificial intelligence, machine learning, and data analytics.
Bharti Jagwani Motwani, PhD is an Academic Director of the Online Master of Science and Business Analytics and a Clinical Associate Professor in the Robert Smith College of Business at the University of Maryland. She is the sole author of many books related to machine learning and artificial intelligence. Her research focuses on machine and deep learning.
