# Google Unveils 72-Qubit Quantum Computer With Low Error Rates

Google's Bristlecone quantum computer

Google announced a 72-qubit universal quantum computer that promises the same low error rates the company saw in its first 9-qubit quantum computer. Google believes that this quantum computer, called Bristlecone, will be able to bring us to an age of quantum supremacy.

### Ready For Quantum Supremacy

Google has teased before that it would build a 49-qubit quantum computer to achieve “quantum supremacy.” This achievement would show that quantum computers can perform some well-defined science problems faster than the fastest supercomputers in the world can.

In a recent announcement, Google said:

If a quantum processor can be operated with low enough error, it would be able to outperform a classical supercomputer on a well-defined computer science problem, an achievement known as quantum supremacy. These random circuits must be large in both number of qubits as well as computational length (depth).

Although no one has achieved this goal yet, we calculate quantum supremacy can be comfortably demonstrated with 49 qubits, a circuit depth exceeding 40, and a two-qubit error below 0.5%. We believe the experimental demonstration of a quantum processor outperforming a supercomputer would be a watershed moment for our field, and remains one of our key objectives.

Not long after Google started talking about its 49-qubit quantum computer, IBM showed that for some specific quantum applications, 56 qubits or more may be needed to prove quantum supremacy. It seems Google wanted to remove all doubt, so now it’s experimenting with a 72-qubit quantum computer.

Don’t let the numbers fool you, though. Right now, the most powerful supercomputers can simulate only 46 qubits and for every new qubit that needs to be simulated, the memory requirements typically double (although some system-wide efficiency can be gained with new innovations).

Therefore, in order for us to simulate a 72-qubit quantum computer, we’d need millions of times more RAM (2^(72-46)). We probably won’t be able to use that much RAM in a supercomputer anytime soon, so if Bristlecone will be able to run any algorithm faster than our most powerful supercomputers, then the quantum supremacy era will have arrived.

### High Number Of Qubits Is Not Enough

A high number of qubits is not the only thing that’s needed to achieve quantum supremacy. You also need qubits with low error rates so they don’t mess-up the calculations. A useful quantum computer is a function of both number of qubits and error rate.

According to Google, a minimum error rate for quantum computers needs to be in the range of less than 1%, coupled with close to 100 qubits. Google seems to have achieved this so far with 72-qubit Bristlecone and its 1% error rate for readout, 0.1% for single-qubit gates, and 0.6% for two-qubit gates.

Google Quantum AI Lab's intended progress for quantum computers

Quantum computers will begin to become highly useful in solving real-world problems when we can achieve error rates of 0.1-1% coupled with hundreds of thousand to millions of qubits.

According to Google, an ideal quantum computer would have at least hundreds of millions of qubits and an error rate lower than 0.01%. That may take several decades to achieve, even if we assume a “Moore’s Law” of some kind for quantum computers (which so far seems to exist, seeing the progress of both Google and IBM in the past few years, as well as D-Wave).

That said, we may start seeing some "useful" applications of quantum computers well before that. For instance, breaking most existing cryptography may be possible when the quantum computers have only a few thousand qubits. If the current rate of progress for quantum computers holds, we may be able to reach that in about a decade.

Google is “cautiously optimistic” that the Bristlecone quantum computer will not only achieve quantum supremacy, but could also be used as a testbed for researching qubit scalability and error rates, as well as applications such as simulation, optimization, and machine learning.

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