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IBM releases ‘Qiskit’ modules that use quantum computers to improve machine learning

IBM is releasing Qiskit Machine Learning, a set of new application modules that’s part of its open source quantum software. The new feature is the latest expansion of the company’s broader effort to get more developers to begin experimenting with quantum computers.

According to a blog post by the Qiskit Applications Team, the machine learning modules promise to help optimize machine learning by using quantum computers for some parts of the process.

“Quantum computation offers another potential avenue to increase the power of machine learning models, and the corresponding literature is growing at an incredible pace,” the team wrote. “Quantum machine learning (QML) proposes new types of models that leverage quantum computers’ unique capabilities to, for example, work in exponentially higher-dimensional feature spaces to improve the accuracy of models.”

Rather than replacing current computer architectures, IBM is betting that quantum computers will gain traction in the coming years by taking on very specific tasks that are offloaded from a classic computing system to a quantum platform. AI and machine learning are among the areas where IBM has said it’s hopeful that quantum can make an impact.

To make quantum more accessible, last year IBM introduced an open source quantum programming framework called Qiskit. The company has said it has the potential to speed up some applications by 100 times.

In the case of machine learning, the hope is that a system that offloads tasks to a quantum system could accelerate the training time. However, challenges remain, such as how to get large data sets in and out of the quantum machine without adding time that would cancel out any gains by the quantum calculations.

Developers who use Qiskit to improve their algorithms will have access to test them on IBM’s cloud-based quantum computing platform.

Article: IBM releases ‘Qiskit’ modules that use quantum computers to improve machine learning

 

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