We will discuss the qubit, a quantum bit, and what data processing and machine learning in the quantum computing era might look like using python based open-source tools.
The fundamental unit of information, a bit, will be replaced by a qubit, or a quantum bit in the age of quantum computing. The use of a quantum state to store, manipulate and process information can have far reaching effects for large-scale data analysis. An N qubit quantum computer can simultaneously exist in a superposition of all 2^N states. Google's latest 72 qubit chip already begins to challenge the limits of classical supercomputers for quantum simulations. On the other hand, efforts towards developing such technologies which are capable of handling the large amount of information in a quantum system has led to a new possibility. The possibility of big data analysis with quantum computers. Rigetti, IBM, Google, Microsoft and many other organizations have already released open-source tools for writing quantum algorithms. pyQuil, a tool developed by Rigetti makes it easy to write unsupervised Machine Learning algorithms to be run on their cloud based quantum computer. Similarly, many of these tools are python-based and in this talk I will give a brief idea about some of these projects starting from QuTiP: the Quantum Toolbox in Python. The talk will focus on familiarizing the audience with the concept of a qubit, quantum algorithms and how open source tools are laying the foundation of future data processing methods with quantum computers.
The outline of the talk will be as follows:
Bit vs Qubit: A very short description of quantum weirdness.
Quantum circuits and algorithms: Manipulating quantum data.
QuTiP, ProjectQ, pyQuil, QISKIT: Python based tools for quantum simulation and algorithms.