Google’s Quantum Simulation Upends Understanding of Magnets

When Nobel laureate Richard Feynman first suggested the idea of quantum computers, he proposed they might perform the kind of complex quantum simulations that may yield insights into next-generation batteries or novel drugs. Now a new quantum simulator from Google has discovered that magnetism does not always work the way scientists think, suggesting that it has promise for unearthing more discoveries in the future.

The new research combines two kinds of quantum computing—analog and digital. In analog quantum computing, qubits can serve as analogues of other objects that display quantum behavior, such as molecules, atoms, and subatomic particles. Analog quantum computing is often used to simulate molecular interactions that are too complex for any classical computer to model within our lifetimes.

In contrast, digital quantum computers run sequences of elementary operations, called quantum logic gates, on a set of qubits. With enough qubits, a quantum computer could theoretically vastly outperform all classical computers on a number of applications. For instance, on quantum computers, Shor’s algorithm can crack modern cryptography, and Grover’s algorithm can search databases at staggering speeds.

Digital quantum computers can perform quantum simulations, but analog quantum computers are faster at this task. For instance, when simulating how three atoms might interact, a digital quantum computer would have to model the interactions between each combination of atoms one step at a time, whereas an analog quantum computer could model them all simultaneously. Speed is especially important, given the current error-prone nature of quantum hardware—the faster the operation, the more likely it will be successfully completed.

Still, digital quantum computers are more flexible at quantum simulation than analog quantum computers are. Analog quantum computers are designed to mimic whatever they are simulating as closely as possible, whereas digital quantum computers are more tunable in what they can simulate.

Google’s Analog-Digital Hybrid Quantum Simulation

Now Google “is launching a new analog-digital hybrid approach for quantum simulation to try and get the best of both worlds,” says Trond Andersen, a senior research scientist at Google Quantum AI in Mountain View, Calif. The researchers detailed their findings online 5 February in the journal Nature.

The new system possesses 69 superconducting qubits. It begins its simulations by applying gates to qubits to prepare the initial states of the model, and then lets the model quickly evolve in an analog manner. Finally, it returns to digital performance so that researchers can measure the results in an extensive way. “We get a combination of flexibility and speed,” Andersen says.

Previous research had explored analog-digital hybrid quantum simulation, but it often suffered from large errors during the analog evolution stage. The new system employed a high-fidelity calibration scheme that significantly reduced this problem, achieving a 0.1 percent error rate per qubit. “This was one of the breakthroughs that made this work possible,” Andersen says.

In benchmarking experiments, the scientists estimated that simulations with the level of fidelity seen with the new system would require more than 1 million years on the Frontier supercomputer at Oak Ridge National Laboratory, in Tennessee. “We’re excited about our new direction for discoveries and applications that we could not achieve on a classical computer,” Andersen says.

Moreover, the new simulator made an unexpected discovery. It found out that the widely used Kibble-Zurek mechanism—which can, for instance, predict the behavior of magnets during phase transitions—does not always hold.

“This was a big surprise—this is a mechanism very widely studied in quantum labs all over the world,” Andersen says. Understanding the dynamics associated with the Kibble-Zurek mechanism “is important for various types of quantum simulation.” he says.

Andersen notes that this discovery could have been made with a classical computer. “We’re now starting to use our approach for applications that would be impossible with a classical computer,” he says. This research was conducted with Google’s Sycamore quantum processors, and Andersen says the company “now has a new, advanced chip, Willow, that we are excited to try our approach on.”