Quantum Computing —> Quantum Machine Learning

06 July 2021 - less than 1 min read time
Tags: AI ML Quantum Computing

Links: https://www.ibm.com/quantum-computing/learn/what-is-quantum-computing/

https://www.tensorflow.org/quantum

Quantum Computing

We experience the benefits of classical computing every day. However, there are challenges that today’s systems will never be able to solve. For problems above a certain size and complexity, we don’t have enough computational power on Earth to tackle them.

To stand a chance at solving some of these problems, we need a new kind of computing. Universal quantum computers leverage the quantum mechanical phenomena of superposition and entanglement to create states that scale exponentially with number of qubits, or quantum bits.

Bit —> Qubit

Superposition

Entanglement

Quantum Machine Learning

Quantum machine learning is the integration of quantum algorithms within machine learning programs. most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. quantum-enhanced machine learning. While machine learning algorithms are used to compute immense quantities of data, quantum machine learning utilizes qubits and quantum operations or specialized quantum systems to improve computational speed and data storage done by algorithms in a program.This includes hybrid methods that involve both classical and quantum processing, where computationally difficult subroutines are outsourced to a quantum device

Some of the current implementation on Quantum enhanced Machine learning are:

1. Grover’s Search
2. Using the knowledge form grovers search to implementing K-Means , k- nearest neighbours algorithms.

Libraries:

Qiskit

Cirq (Primary Ones)