Back in 2019, Google proudly announced they had achieved what quantum computing researchers had sought for years: proof that the esoteric technique could outperform traditional ones. But this demonstration of "quantum supremacy" is being challenged by researchers claiming to have pulled ahead of Google on a relatively normal supercomputer.

To be clear, no one is saying Google lied or misrepresented its work — the painstaking and groundbreaking research that led to the quantum supremacy announcement in 2019 is still hugely important. But if this new paper is correct, the classical versus quantum computing competition is still anybody's game.

You can read the full story of how Google took quantum from theory to reality in the original article, but here's the very short version. Quantum computers like Sycamore are not better than classical computers at anything yet, with the possible exception of one task: simulating a quantum computer.

It sounds like a cop-out, but the point of quantum supremacy is to show the method's viability by finding even one highly specific and weird task that it can do better than even the fastest supercomputer. Because that gets the quantum foot in the door to expand that library of tasks. Perhaps in the end all tasks will be faster in quantum, but for Google's purposes in 2019, only one was, and they showed how and why in great detail.

Now, a team at the Chinese Academy of Sciences led by Pan Zhang has published a paper describing a new technique for simulating a quantum computer (specifically, certain noise patterns it puts out) that appears to take a tiny fraction of the time estimated for classical computation to do so in 2019.

Not being a quantum computing expert nor a statistical physics professor myself, I can only give a general idea of the technique Zhang et al. used. They cast the problem as a large 3D network of tensors, with the 53 qubits in Sycamore represented by a grid of nodes, extruded out 20 times to represented the 20 cycles the Sycamore gates went through in the simulated process. The mathematical relationships between these tensors (each its own set of interrelated vectors) was then calculated using a cluster of 512 GPUs.