**But given the rapid development of the quantum computing landscape, this may not matter, as the company claims it's nearly impossible to copy on supercomputers. . It turns out that this was not entirely true, because Google neglected to consider the storage space available for supercomputers. If this is taken into account, the power of the quantum computer will be reduced to just a few days. **

However, adding a few more qubits re-establishes the tremendous supremacy of quantum computing. Recently, however, a draft has been placed on arXiv indicating an important fact: Google's claims relied on comparing a very specific approach to computing on standard computing devices. There are other ways to do calculations, and this article shows that one of them allows the supercomputer to virtually beat its quantum rival.

## More than one way to perform calculations

Since the laws of this quantum interference are understood, we can calculate the patterns we should see with the random numbers generated by the quantum interference. . plane tree. But performing these calculations is computationally very expensive and becomes more expensive with each additional qubit in the system. Google has estimated that it would take an unrealistic time to perform these calculations on the world's most advanced supercomputers. Advertising

Omit the following. Storage was connected to the world's largest supercomputer at that time, which greatly reduced Sycamore's progress. But the fact remains that these calculations were too difficult for classical computing devices.

The new manuscript focuses on a key aspect of the phrase: these accounts. Google has chosen a very specific way to calculate the expected behavior of its processor, but there are other ways to perform equivalent calculations. Over time, several options have been explored that work best. Now, Feng Pan, Keyang Chen, and Pan Zhang describe a special method that allows a GPU-based array to generate equivalent output in just 15 hours. Run it on a flagship supercomputer, and it's estimated to perform better than a Sycamore quantum processor. Chen Wuchang succeeded. We'll try three of them, gradually delving into the math.

The easiest way to find out is in terms of the output Sycamore provides. Single measurements of the qubit state in the sycamore processor created a series of truly random numbers and zeros, but if you make enough measurements of the processor's initial state, the patterns become apparent. If you set up a classic computation that sums up the physics of Sicamor, you'll get the same random level and the same patterns. However, CPUs get much faster computations in the process. In other words, the new calculations don't exactly sum up sycamore behavior, but they still produce basic, random patterns that can be completed faster. Advertising

This is one interpretation. Option 2 to understand this involves looking at how to convert the start position of the Sycamore Wizard to its scale point. There are many possible paths to getting there, and since this is a quantum system, it examines them all. To get an accurate model of the Sycamore processor output you also have to explore all the paths, which are computationally very compact. Pan, Chen, and Zhang found a tool to constrain the paths you're looking at that allows calculations to be performed while achieving equivalent output.

Those who want to avoid math, now you have to move on to the next section. Address. The actual computation method describes the interactions of a Sicamore qubit as a three-dimensional tensor network with tensors dictating the relationships between qubit properties. The algorithm then simplifies this by separating some of the network's connections - the researchers describe it as the equivalent of drilling 3D holes through the network.

Every hole you dig, the less accurate your calculations are. Half. But this makes loyalty completely customizable: you can be sure you have a good summary of sycamore behavior by simply limiting the number of holes you dig. The mathematics of where to drill these holes in the mesh is determined by the physical composition of the sycamore slide itself. which can be stored in the system they were working on. They used their algorithm to model the behavior of smaller qubit networks in sycamore and showed that they produce accurate results within their loyalty range.

Mathematics may be in line with Google's claims of quantum supremacy

mathematics-may-be-in-line-with-google-s-claims-of-quantum.html

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