Quantum Computing and its impact on Quantum Machine Learning  

Quantum Computing has been an area of active research for the past 3 decades starting in the early 1990s. It is a cross disciplinary area at the intersection of quantum mechanics, computer science and communications.  Quantum computing uses principles of quantum mechanics as its physical and mathematical foundation to process information. This results in fundamentally influencing basic sciences (physics and chemistry), and applied technical areas including digital communications (specifically the Internet), machine learning, cryptography and gaming, to name a few.

That quantum computing has the potential to fundamentally disrupt almost all current areas of science, engineering and technology was a known fact. It is only in the current decade (2011-20) that scientists and engineers have been able to build “reasonable sized” physical quantum computers. National governments (US, Europe, China, India), consortiums (NASA, Google, D-Wave), large private companies (Google, IBM, Intel, Microsoft), startups (Rigetti, D-Wave) and venture capital funds (Quantum X for example) have been providing huge funds in this area. They are therefore strong motivators and influencers in starting to seriously look at addressing “hard problems” which conventional classical computers take years to solve and developing industry grade systems and algorithms to effectively address all the areas that I mentioned earlier. Quantum Supremacy or Quantum Speedup is the measure, which helps one to categorically state that quantum computers are better than today’s best-in-class “classical” computers in solving these hard problems.

Keeping in view all the points that I mentioned above, the audience can expect the following takeaways from my talk. This is also the way my session is structured.

  • Fundamentals of Quantum Computing such as the qubit (the basic measure of quantum computing power), superposition, entanglement etc.

  • An overview of National Governments and national organizations of most countries including US, Europe, China and India and companies such as Google, IBM etc. which are focusing on and providing huge funds to develop quantum computers and associated spin offs in all the above areas.

  • Why is quantum computing getting increasingly important from the perspective of addressing real life hard problems in pure sciences and the computer industry which the current classical computers cannot address.

  • What is the meaning of “hard” problems from a quantitative or mathematical perspective.
  • A brief overview of the algorithms which took quantum computing from a pure scientific area of research to become a strong contender to equivalent classical algorithms.

  • What is the meaning of Quantum Speedup or Quantum Supremacy and how does this impact the performance of classical Machine learning algorithms.

  • A couple of real-life examples on the quantum speedup.
  • What are the software tools available in the market today to work on quantum computing problems?


Dr. Mandaar Pande


Dr. Mandaar Pande is currently a Professor of IT at the Symbiosis Centre for Information Technology, Pune (a constituent of Symbiosis International (Deemed) University). After completing a Ph.D. in Theoretical Physics from the University of Hyderabad on practical uses of nonlinear phenomena in nonlinear optics, he comes with around 25 years of varied experiences in industry and academia.


In his initial stint in academics, he was a faculty at BITS, Pilani, where he taught digital electronics and communications. For two decades in IT industry, he worked with Tech Mahindra and Wipro on multiple projects and global programs specifically addressing the non functional and performance engineering space across all industry verticals. He was globally heading the Performance Engineering Practice at Wipro from 2013-2017.


After moving back to academics in 2017, he has been teaching IT subjects with focus on Data Science and Data Analytics and using Design Thinking to bring in innovative approaches to problem solving. He is working on industry oriented and pure scientific research problems in Quantum Computing, Quantum Machine Learning and Quantum Communications. These are cross-disciplinary areas encompassing Quantum Physics, Computer Science and Communications, and are the future of computing.

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