Machine learning With Quantum Systems

The domain of Machine learning and Quantum computation are the next big leap in the general experience of computing. Using machine learning we want to take smart decisions and enlarge our solution space. In this era, we can see active research in Medical image processing, self driving cars, Content summarisation, sentiment analysis etc. All these can be obtained by using classical computers. Another domain of active research is Quantum information processing, here we try to use the principles of quantum mechanics to gain a leap in efficiency of information processing task. One such example is Shor's factoring algorithm which can solve the prime factorisation problem in O((log N)2(log log N)(log log log N) which is NP hard if we use classical computers. The primary reason which enables this the phenomena of superposition.
The confluence of Classical machine learning with Quantum information theory which gives rise to field of Quantum Machine Learning, which is still in its nascent stages, but never the less very interesting to study. It is interesting to ask two types of questions
a. How good would be a quantum computer in learning the the classical information. To give an example will a quantum computer be able to classify Apples and Oranges better than a classical computer.
b. Another interesting question is to ask will some of the problems which are hard in quantum world be learned using classical computers. One such example is to classify entangled vs separable states.
In this conference I will talk about how two domains are similar and different and what are some proposed solutions for the above stated problems.
 
 

Outline/Structure of the Talk

Basic kinds of problems in machine learning

How to extend it to domain of general sciences

extension to quantum mechanics

Learning Outcome

Exploring new problems in science to solve using machine learning.

New scopes and domains which can be explored using machine learning.

Target Audience

Academia, Students, Data scientist and Data engineers

Prerequisites for Attendees

A data science enthusiast with basic understanding of problems in machine learning. No basics of science or Quantum mechanics required

schedule Submitted 2 years ago

Public Feedback


    • Gunjan Juyal
      keyboard_arrow_down

      Gunjan Juyal - Building a Case for a Standardized Data Pipeline for All Your Organizational Data

      Gunjan Juyal
      Gunjan Juyal
      Sr. Consultant
      Xnsio
      schedule 2 years ago
      Sold Out!
      20 Mins
      Experience Report
      Beginner

      Organizations of all size and domains today face a data explosion problem, driven by a proliferation of data management tools and techniques. A very common scenario is creation of silos of data and data-products which increases the system’s complexity spread across the whole data lifecycle - right from data modeling to storage and processing infrastructure.

      High complexity = high system maintenance overheads = sluggish decision making. Another side-effect of this is divergence of the implemented system’s behaviour from high-level business objectives.

      In this talk we look at Zeta's experience as a case-study for reducing this complexity by defining and tackling various concerns at well-defined stages so as to prevent a build of complexity.

    • Rohit Gupta
      keyboard_arrow_down

      Rohit Gupta - Image Compression with Neural Networks

      Rohit Gupta
      Rohit Gupta
      Software engineer
      Zeta
      schedule 2 years ago
      Sold Out!
      20 Mins
      Talk
      Intermediate

      Nowadays many apps and social networking sites are dealing with large number of images which lead to huge storage cost and problem in surfacing those images on client machine in case of bad/low network speed. In this talk we will present how can we achieve low bpp using RNNs.

    help