Boltzmann Law: From Physics to Quantum Computing
Explore the connections between statistical mechanics, neural networks, and quantum computing through the lens of Boltzmann's principles.
Explore the connections between statistical mechanics, neural networks, and quantum computing through the lens of Boltzmann's principles.
This advanced course provides a unified perspective on three diverse fields through the concept of 2^N dimensional state-space defined by binary bits. Starting with fundamental principles of statistical mechanics including entropy and free energy, the course progresses to Boltzmann machines in machine learning and engineered quantum systems. Students explore quantum interference and its computational applications, connecting classical physics principles to modern computing paradigms. The curriculum bridges theoretical physics with practical applications in quantum computing and machine learning.
Instructors:
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What you'll learn
Master the principles of Boltzmann Law and statistical mechanics
Understand the application of Boltzmann machines in machine learning
Explore quantum computing fundamentals and gate operations
Analyze state-space concepts across different computational paradigms
Implement quantum algorithms including Grover search and Shor's algorithm
Connect classical physics principles to modern computing methods
Skills you'll gain
This course includes:
Live video
Graded assignments, exams
Access on Mobile, Tablet, Desktop
Limited Access access
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There are 5 modules in this course
This advanced course unifies concepts from statistical mechanics, machine learning, and quantum computing. The curriculum progresses from fundamental statistical mechanics principles to advanced topics in quantum computing. Students learn about state-space analysis, Boltzmann machines, quantum interference, and practical applications in computing. The course emphasizes both theoretical understanding and practical applications, bridging classical physics with modern computational methods.
Boltzmann Law
Module 1
Boltzmann Machines
Module 2
Transition Matrix
Module 3
Quantum Boltzmann Law
Module 4
Quantum Transition Matrix
Module 5
Fee Structure
Instructors
8 Courses
Pioneering Nanoscale Electronics Innovator
Supriyo Datta is the Thomas Duncan Distinguished Professor of Electrical and Computer Engineering at Purdue University, whose groundbreaking contributions have transformed the field of nanoelectronics. Initially starting his career in ultrasonics, he shifted his focus to nanoscale electronic devices in 1985, where he developed revolutionary approaches to quantum transport. His pioneering work combines the non-equilibrium Green function (NEGF) formalism with the Landauer formalism, which has become a cornerstone in nanoelectronics research. His influential books, including "Electronic Transport in Mesoscopic Systems" (1995), "Quantum Transport: Atom to Transistor" (2005), and "Lessons from Nanoelectronics" (2012), have shaped the field's theoretical foundation. His innovative theoretical proposals have sparked new research areas in molecular thermoelectricity, negative capacitance devices, and spintronics, leading to his election to the National Academy of Engineering (NAE)
8 Courses
Emerging Computer Architecture and Machine Learning Researcher
Risi Jaiswal (Rishi) is a PhD candidate in Electrical and Computer Engineering at Purdue University, bringing valuable industry experience to his academic research. After completing his Bachelor of Technology in Electronics and Communication from IIIT Allahabad in 2014, he spent five years (2014-2019) working in digital design-verification and validation at NXP Semiconductor Noida. His current doctoral research focuses on cutting-edge areas including probabilistic computing, quantum machine learning, optimization, and approximate computing. His work combines both hardware architecture design and algorithm development, particularly in accelerator architectures. His unique background bridges the gap between industry experience in semiconductor design and academic research in emerging computing paradigms.
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