Artificial intelligence and quantum computing are one of the most powerful technologies and soon both of them are going to revolutionize our old way of computing information.
Though the certain aspects of their mathematical foundation are little different, the combination of both the technologies shows a promising boost in many different areas such as accessing more computationally complex feature spaces.
IBM recently released its new research, IBM researchers presents a new developed and tested quantum algorithms that could sort and classify complex data sets that algorithms running on classical computers struggle to handle.
“We’ve shown a particular way of moving data into higher dimensions that would be hard for a classical computer but manageable for a quantum computer,” says Antonio Córcoles at IBM in New York.
If we talk about ordinary computers, they perform machine learning by comparing mathematical representations of data. The more precisely that data can be classified according to specific characteristics, or features, the better the AI will perform.
IBM quantum algorithms demonstrate how quantum computing can be used to classify data with the use of short-depth circuits, that also dealt with the expected decoherence (loss of state) in a quantum computer.
The classification was done by a machine learning algorithm that was optimized for quantum computing. The classification showed no errors, even though IBM’s quantum computers experienced decoherence.
According to IBM, classical computers cannot be used to obtain the results of quantum computation. The company said the more powerful quantum computers can be paired with machine learning algorithms, the better the result willl be.
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In its release, IBM explained that both classical and quantum machine learning algorithms can break down a picture, for example, by pixels and place them in a grid based on each pixel’s color value.
This technique is called “feature mapping”. In this technique more precisely the data get classified, as per the specific characteristics, the better the machine learning system will perform.
BIM’s quantum algorithm, map the individual data points non-linearly to a high-dimensional space, breaking the data down according to its most essential features.
In the much larger quantum state space, we can separate aspects and features of that data better than we could in a feature map created by a classical machine-learning algorithm.
With quantum computer and algorithm’s (like IBM’s quantum algorithm) we can create new classifiers that generate more sophisticated data maps which ultimately help us to develop more effective AI that can do wonders, for example, identify patterns in data that are invisible to classical computers.
The feature-mapping algorithms IBM developed for quantum computers was only tested on a simulation of a two-qubit quantum computer, but it still showed that there is a promising path forward for machine learning algorithms that run on quantum computers.
IBM’s algorithm promises to separate larger and more diverse data set into meaningful classes for training a machine learning algorithm.
IBM believes its machine learning algorithms could soon classify far more complex data sets than any classical computer could handle.
IBM’s algorithms also demonstrate how entanglement can improve AI classification accuracy. The feature of the algorithm will be available as a part of IBM’s Qiskit Aqua.
Qiskit Aqua is an open-source library of quantum algorithms that developers, researchers, and industry experts can use to access quantum computers via classical applications or common programming languages such as Python.
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