Quantum computing (QC) aims to solve classical computing’s problem efficiently. If led in a quantum-based approach, machine learning (ML) can smoothly lay in the course of existing quantum technology and take advantage by adapting classic algorithms to the new applied science.
Aside from an expanded store limit and speedup, you are free of setting heuristics to monitor computational time. Indeed, through following a quantum assumption, many advantages can be attained in time.
Data and mechanics
The abstraction that characterises QC is basically a unit of quantum information, called a qubit. It has two possible states which can form linear combinations of both called superpositions. This compares with a classical bit - it has a state of either 0 or 1. A classical bit hold less information than a qubit.
The QC assumption gives the numerical clarification of the movement and connection with energy at the atom’s level, storing data on a single atom; or at a molecule’s level, storing data in the three dimensions of the DNA molecule.
Why is this all so important? QC has such potential because society is becoming more complex and so is the information that it produces. To make sense of it all, more complex models are needed. Quantum solutions can be applied when big data is too big, like storing petabytes of data (millions of gigabytes).
Algorithms and performance
Artificial intelligence (AI) is the field of ML that concerns itself with discovering patterns in information extracted from a source of data. In this sense, data dimension and data volume dictate computational complexity for learning methods.
In a quantum scenario, ML algorithms like classification, regression, density estimation, multivariate querying, anomaly detection, dimension reduction and reinforcement learning, handle data in the condition of quantum states.
This allows a system to be in a few states simultaneously by accelerating special kinds of calculations through the achieved quantum superposition.
As you might imagine, this sort of computational parallelism can achieve great performance advantages. Building up a quantum state with high constancy remains a challenge.
If you conduct methods like principal components analysis, pattern recognition, or clustering, in an quantum way, challenges such as drug discovery, trajectory optimisation, gene sequencing, astronomy, time series, social networks, and image analysis will be made easier since you can get faster solutions to your problems.
This is due to the exponential number of states achieved. In a practical sense, a self-driven car can drive us safely in a city by taking advantage of quantum information processing speedups. ML is a search-based application of AI and exponential search spaces for large problems are suitable for quantum computing.
Here are some excellent resources for furthering your understanding of quantum AI.