Researchers are using an innovative method to increase the speed and accuracy of traditional calculations. Quantum computing is touted as a technology that surpasses classical computing in both speed and memory usage, potentially paving the way for predicting physical events that were previously impossible.

Many people see the emergence of quantum computing as a paradigm shift from classical or traditional computing. While traditional computers process information in the form of digital bits (0s and 1s), quantum computers use quantum bits (qubits) to store quantum information in values from from 0 to 1. Under certain conditions, the ability to process and store information in qubits can be used to develop quantum algorithms that are far superior to their classical counterparts. It is noteworthy that a quantum’s ability to store information in values ​​from 0 to 1 makes it difficult for classical computers to perfectly emulate quantum computers.

Problems and solutions in quantum computing

But quantum computers are finicky and tend to lose information. Moreover, even if information loss can be prevented, it is difficult to translate it into the classical information necessary to obtain useful calculations.

Classic computers do not suffer from either of these problems. What’s more, cleverly designed classical algorithms can further exploit the dual problem of information loss and translation to simulate a quantum computer with far fewer resources than previously thought, as reported in a recent research paper in the journal Science. PRX Quantum .

The scientists’ results show that classical computing can be reconfigured to perform faster and more accurate calculations than modern quantum computers.

This breakthrough was achieved using an algorithm that stores only a fraction of the information stored in the quantum state, and enough to accurately calculate the final result.

Combination of classical and quantum computing

“This work shows that there are many potential ways to improve computation, encompassing both classical and quantum approaches,” explains Dries Sels, an associate professor of physics at NYU and one of the paper’s authors. “Moreover, our work highlights how difficult it is to achieve quantum supremacy with an error-prone quantum computer.”

Looking for ways to optimize classical computing, Sells and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents interactions between qubits. Such networks are notoriously difficult to deal with, but recent advances in this field now make it possible to optimize these networks using tools taken from statistical inference.

The authors compare the algorithm’s operation to compressing an image into a JPEG file; This allows you to store large images using less space and remove information with a barely perceptible loss of image quality.

“Choosing different structures for a tensor network corresponds to choosing different forms of compression, like different formats, for your image,” says Joseph Tyndall of the Flatiron Institute, who led the project. “We have been successfully developing tools for working with a wide variety of different tensor networks. This work reflects this, and we are confident that we will soon raise the bar for quantum computing even further.”