Researchers show that classical computers can keep up with and outperform their quantum counterparts

Quantum computing has been hailed as a technology capable of surpassing classical computing in speed and memory usage, potentially paving the way for predictions of physical phenomena previously impossible.

Many see the advent of quantum computing as marking a paradigm shift from classical or conventional computing. Conventional computers process information in the form of digital bits (0 and 1), while quantum computers deploy quantum bits (qubits) to store quantum information in values ​​between 0 and 1.

Under certain conditions, this ability to process and store information in qubits can be used to design quantum algorithms that significantly outperform their classical counterparts. Notably, quantum’s ability to store information in values ​​between 0 and 1 makes it difficult for classical computers to perfectly emulate quantum computers.

However, quantum computers are finicky and tend to lose information. Furthermore, even if information loss can be avoided, it is difficult to translate it into conventional information, which is necessary to produce a useful calculation.

Classical computers suffer from neither of these two problems. Additionally, intelligently designed classical algorithms can further exploit the dual challenges of information loss and translation to mimic a quantum computer with far fewer resources than previously thought, as recently reported in a paper research published in the journal Quantum PRX.

The scientists’ results show that classical computing can be reconfigured to perform faster and more precise calculations than state-of-the-art quantum computers.

This major advance was achieved thanks to an algorithm that retains only part of the information stored in the quantum state, and just enough to be able to accurately calculate the final result.

“This work shows that there are many potential avenues for improving calculations, encompassing both classical and quantum approaches,” says Dries Sels, assistant professor in the physics department at New York University and one of the authors of the article. “Moreover, our work highlights how difficult it is to achieve quantum advantage with an error-prone quantum computer.”

In looking for ways to optimize classical computing, Sels and his colleagues at the Simons Foundation focused on a type of tensor network that faithfully represents the interactions between qubits. These types of networks are notoriously difficult to manage, but recent advances in the field now make it possible to optimize these networks with tools borrowed from statistical inference.

The authors compare the algorithm’s work to compressing an image into a JPEG file, which allows large images to be stored using less space by removing information with a barely perceptible loss in image quality. picture.

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