- The machine learning model plans to optimize machine learning by using quantum.
The new feature provides the latest expansion method for the company’s efforts to target more developers to begin experimenting with quantum computers.
According to a blog post provided by Qiskit Applications Team, the machine learning models promise to help optimise machine learning by using quantum computers for some parts of the process.
Statement from IBM Team:
“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.”
Instead of replacing the current computer architectures IBM bets that quantum computers are going to be gaining traction within the next few years by taking on specific tasks that can be offloaded from a classic computing system to a quantum platform. AI and machine learning are among the areas where IBM is hopeful that quantum can make a significant impact.
How will quantum prove to be beneficial:
To make Quantum even more accessible, last year IBM introduced an open-source Quantum programming framework called Qiskit. The company claims that this framework has the potential to speed up so many applications by hundred times.
As far as the concept of machine learning is concerned, the company is hopeful that a system which offloads tasks to the Quantum system can accelerate the training time.
There are challenges like getting large data sets in and out of the quantum machine. That too without adding time that would cancel out any gains by the quantum calculations.
Developers who use Quantum programming framework will have to test them on IBM’s cloud based Quantum computing platform.
The company’s full quantum stack enables other organisations to explore and work with next solutions. These solutions come with fidelity and scale which are unmatchable.
How the algorithms work:
The code quantum algorithms with Python can work to integrate quantum in the workflows with high level libraries. The company releases a roadmap which can take up the small scale devices to million qubit devices in the future. They have developed IBM Quantum which is going to be released by the end of 2023.
This system will have scalable and huge but better processors that can speed up other devices by a hundred times.
The team at IBM builds Quantum processors that rely on mathematics of elementary particles for the expansion of computational capabilities. It runs Quantum circuits instead of logic circuits of digital computers.
They represent the data through electronic Quantum states of artificial atoms with what they like to call them the superconducting transmon qubits. They connect and manipulate with a sequence of microwave pulses in order to run the circuits.