IBM has created quantum algorithms to perform machine learning on quantum computers to create artificial intelligences much more powerful than those created with conventional computers …
The “Feature Mapping” is to disassemble the information to access more aspects “purposes” of the data. Currently, Machine Learning already allows you to do this, for example by taking the pixels of an image to place them in a grid according to their color. The algorithms then map the color values non-linearly and break down the data according to their most useful characteristics.
However, thanks to quantum computers , IBM researchers have discovered a way to make Machine Learning significantly more efficient for feature mapping. In a paper published on arXiv, the research team announces having created a “quantum algorithm” allowing quantum computers to perform machine learning in a new scale.
As a reminder, quantum computers take advantage of the strange ability of subatomic particles to exist in more than one state at a time . Thanks to the way these particles behave, operations can be performed faster than on conventional computers, while consuming less energy.
In fact, unlike the bits of classical computers that can only exist in two states (1 or 0), the quantum bits (qubits) of quantum computers can exist in any superposition of these two values and thus store more data. information.
Thus, IBM’s new quantum algorithms make it possible to separate the aspects and characteristics of the data in an even greater degree than with a standard Machine Learning algorithm. In fact, data can be classified more precisely and Machine Learning systems will be more efficient.
IBM takes Machine Learning into the quantum dimension
The goal is to use quantum computers to create new classifiers that can generate more sophisticated data maps. In doing so, researchers will be able to develop more effective artificial intelligences that can for example identify invisible patterns for conventional computers.
For now, IBM says that these new algorithms have not yet surpassed the performance of conventional machines on quantum computers. However, this is mainly related to the fact that quantum computers are still limited by the current hardware constraints.
Indeed, current quantum computers have a computing capacity limited to only two qubits. However, this computing capacity can be simulated on conventional computers. It will therefore be necessary to wait for more efficient quantum computers to emerge for IBM algorithms to achieve “quantum advantage”.
In the meantime, these new algorithms are available in open-source via the Qiskit Aqua library at this address. Developers, researchers and other experts can use it as they see fit.