Quantum Supremacy Using a Programmable Superconducting Processor

Left: Artist's rendition of the Sycamore processor mounted in the cryostat. (Forest Stearns, Google AI Quantum Artist in Residence) Right: Photograph of the Sycamore processor. (Erik Lucero, Research Scientist and Lead Production Quantum Hardware)

Quantum computers can process massive and complex datasets more efficiently than classical computers.

They use the fundamentals of quantum mechanics to speed up the process of solving complex computations. Often those computations incorporate a seemingly unlimited number of variables, and the potential applications span industries from genomics to finance.

Quantum computers are certain computational tasks that might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space.

Whereas a classical computer depends on “bits” of information that can be set as either zero or one, a quantum computer employs qubits which can be set to zero, one, or—thanks to quantum mechanics—any combination of zero and one at the same time. Here the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253(about 1016).

Physicists have been talking about the power of quantum computing for over 30 years, but the questions have always been: will it ever do something useful and is it worth investing in? For such large-scale endeavors, it is good engineering practice to formulate decisive short-term goals that demonstrate whether the designs are going in the right direction. So, we devised an experiment as an important milestone to help answer these questions. This experiment, referred to as a quantum supremacy experiment, provided direction for our team to overcome the many technical challenges inherent in quantum systems engineering to make a computer that is both programmable and powerful.

To test the total system performance we selected a sensitive computational benchmark that fails if just a single component of the computer is not good enough.

Left: Artist’s rendition of the Sycamore processor mounted in the cryostat. (Forest Stearns, Google AI Quantum Artist in Residence) Right: Photograph of the Sycamore processor. (Erik Lucero, Research Scientist and Lead Production Quantum Hardware)

The Sycamore processor

Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical computers.

In Sycamore quantum computer is fully programmable and can run general-purpose quantum algorithms. As a consequence, the chip has enough connectivity that the qubit states quickly interact throughout the entire processor, making the overall state impossible to emulate efficiently with a classical computer.

Google and NASA Achieve Quantum Supremacy

Google and NASA expect their computational power will continue to grow at a double exponential rate the classical cost of simulating a quantum circuit increases exponentially with computational volume, and hardware improvements will likely follow a quantum-processor equivalent of Moore’s law, doubling this computational volume every few years.

The task Google set for its quantum computer is “a bit of a weird one”, says Christopher Monroe, a physicist at the University of Maryland in College Park. Google physicists first crafted the problem in 2016, and it was designed to be extremely difficult for an ordinary computer to solve. The team challenged its computer, known as Sycamore, to describe the likelihood of different outcomes from a quantum version of a random number generator.

Researchers from the Google AI research team see potential uses for quantum computing in fields such as machine learning, and materials science and chemistry. They admit, though, that still-greater accuracy will be needed to bring those use cases into the real world.

Applications

They also now have the first widely useful quantum algorithm for computer science applications. Randomness is an important resource in computer science, and quantum randomness is the gold standard, especially if the numbers can be self-checked to come from a quantum computer. Testing of this algorithm is ongoing, and in the coming months, they plan to implement it in a prototype that can provide certifiable random numbers.

Process for demonstrating quantum supremacy.

Each run of a random quantum circuit on a quantum computer produces a bitstring, for example, 0000101. Owing to quantum interference, some bitstrings are much more likely to occur than others when we repeat the experiment many times. However, finding the most likely bitstrings for a random quantum circuit on a classical computer becomes exponentially more difficult as the number of qubits (width) and a number of gate cycles (depth) grow.

In the experiment, we first ran random simplified circuits from 12 up to 53 qubits, keeping the circuit depth constant. We checked the performance of the quantum computer using classical simulations and compared with a theoretical model. Once we verified that the system was working, we ran random hard circuits with 53 qubits and increasing depth, until reaching the point where classical simulation became infeasible.

Estimate of the verification time for quantum supremacy circuits as a function of the number of qubits and number of cycles for the Schrödinger-Feynman algorithm. The red stars show the estimated verification time for the experimental circuits.

This result is the first experimental challenge against the extended Church-Turing thesis, which states that classical computers can efficiently implement any “reasonable” model of computation. With the first quantum computation that cannot reasonably be emulated on a classical computer, we have opened up a new realm of computing to be explored.

The Sycamore Processor

The quantum supremacy experiment was run on a fully programmable 54-qubit processor named “Sycamore.” It’s comprised of a two-dimensional grid where each qubit is connected to four other qubits. As a consequence, the chip has enough connectivity that the qubit states quickly interact throughout the entire processor, making the overall state impossible to emulate efficiently with a classical computer.

The success of the quantum supremacy experiment was due to our improved two-qubit gates with enhanced parallelism that reliably achieve record performance, even when operating many gates simultaneously. We achieved this performance using a new type of control knob that is able to turn off interactions between neighboring qubits. This greatly reduces the errors in such a multi-connected qubit system. We made further performance gains by optimizing the chip design to lower crosstalk, and by developing new control calibrations that avoid qubit defects.

Heat map showing single- (e1; crosses) and two-qubit (e2; bars) Pauli errors for all qubits operating simultaneously. The layout shown follows the distribution of the qubits on the processor. (Courtesy of Nature magazine.)

We designed the circuit in a two-dimensional square grid, with each qubit connected to four other qubits. This architecture is also forward compatible with the implementation of quantum error-correction. We see our 54-qubit Sycamore processor as the first in a series of ever more powerful quantum processors.

Testing Quantum Physics 

To ensure the future utility of quantum computers, we also needed to verify that there are no fundamental roadblocks coming from quantum mechanics. Physics has a long history of testing the limits of theory through experiments since new phenomena often emerge when one starts to explore new regimes characterized by very different physical parameters. Prior experiments showed that quantum mechanics works as expected up to a state-space dimension of about 1000. Here, we expanded this test to a size of 10 quadrillions and find that everything still works as expected. We also tested a fundamental quantum theory by measuring the errors of two-qubit gates and finding that this accurately predicts the benchmarking results of the full quantum supremacy circuits.

This shows that there is no unexpected physics that might degrade the performance of our quantum computer. Our experiment, therefore, provides evidence that more complex quantum computers should work according to theory and makes us feel confident in continuing our efforts to scale up.

Applications

The Sycamore quantum computer is fully programmable and can run general-purpose quantum algorithms. Since achieving quantum supremacy results last spring, our team has already been working on near-term applications, including quantum physics simulation and quantum chemistry, as well as new applications in generative machine learning, among other areas.

What’s Next?

Our team has two main objectives going forward, both towards finding valuable applications in quantum computing. First, in the future, we will make our supremacy-class processors available to collaborators and academic researchers, as well as companies that are interested in developing algorithms and searching for applications for today’s NISQ processors. Creative researchers are the most important resource for innovation — now that we have a new computational resource, we hope more researchers will enter the field motivated by trying to invent something useful.

Second, we’re investing in our team and technology to build a fault-tolerant quantum computer as quickly as possible. Such a device promises a number of valuable applications. For example, we can envision quantum computing helping to design new materials — lightweight batteries for cars and airplanes, new catalysts that can produce fertilizer more efficiently (a process that today produces over 2% of the world’s carbon emissions), and more effective medicines. Achieving the necessary computational capabilities will still require years of hard engineering and scientific work. But we see a path clearly now, and we’re eager to move ahead.

Acknowledgments

We’d like to thank our collaborators and contributors — the University of California Santa Barbara, NASA Ames Research Center, Oak Ridge National Laboratory, Forschungszentrum Jülich, and many others who helped along the way.

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