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Quantum supremacy timeline: Google's 49-qubit target, IBM's 50-qubit counter

Google plans to reach quantum supremacy before the end of 2017, John Martinis said in April, while IBM announced in November that it had built and measured a 50-qubit processor prototype.

TechnologyNewsRachel NwosuPublished: 28 September 20264 min readSources 4
Quantum supremacy timeline: Google's 49-qubit target, IBM's 50-qubit counter

Google set a deadline for itself. By the end of 2017, John Martinis said, his team will build a device that achieves quantum supremacy, a calculation beyond the reach of any conventional computer. Proof will come from a drag race between Google's chip and one of the world's largest supercomputers, MIT Technology Review reported on 21 April 2017.

Martinis leads Google's quantum research group and remains a professor at the University of California, Santa Barbara. He joined Google in late 2014, according to the same report. "We think we're ready to do this experiment. It's something we can do now," he said.

The confidence rests on a chip with six qubits arranged two by three. Google has previously released results from a nine-qubit chip laid out in a line, but Martinis says the supremacy experiment needs a grid of 49 qubits. Designs for devices with 30 to 50 qubits are already in progress. The six-qubit chip also tests a manufacturing method in which qubits and their conventional control wiring are made on separate chips and later bump bonded together.

"That process is all working," Martinis said. "Now we're ready to kind of move fast."

Martinis flashed images of the chip at the IEEE TechIgnite conference in Burlingame, California, but his group has yet to formally disclose technical details, MIT Technology Review said. Google's roughly 25-strong team is one of several industrial research groups recently formed or expanded. The race includes Intel, Microsoft, IBM and startups.

Simon Gustavsson, a principal investigator in a quantum computing group at MIT, told the publication that Google is one of the leaders. "It's pretty comparable between Google and IBM," he said.

Chris Monroe, a University of Maryland professor and cofounder of the startup IonQ, was more guarded about what the milestone would mean. "It'll be an academic milestone," he said. "Afterward you still have to figure out how to make it more scalable and programmable." Martinis agrees much remains to be done, but argues the experiment could become a benchmark for anyone claiming a working quantum computer.

IBM moved first on the number. On 10 November 2017, the company said its scientists had built and measured a processor prototype with 50 qubits, according to the Associated Press. Dario Gil, who leads IBM's quantum computing and artificial intelligence research division, said it was the first time any company had built a quantum computer at that scale.

Seth Lloyd, an MIT mechanical engineering professor not involved in IBM's research, said IBM likely still had glitches to work out but called the 50-qubit announcement a sign of significant progress.

A month later, the emphasis shifted from qubit count to quality. "Press releases always talk about quantum space race in number of qubits," Martinis said at the Q2B conference at NASA Ames, according to Gizmodo. "It's about quantity and qubit quality, not only quantity."

Gizmodo reported on 8 December 2017 that Google was then fabricating its 49 or 50 qubit supremacy device and would begin testing in two weeks, not counting Christmas vacation delays. The test problem requires entangling qubits, letting the system evolve, then measuring outcomes whose probabilities are so complex that a classical machine must simulate the quantum computer to check them. Such a simulation might take weeks to do what the quantum computer does in minutes.

Not everyone treats the word as settled. Dario Gil of IBM later advised against using quantum supremacy as a progress metric. "The experiment and the 'supremacy' term will be misunderstood by nearly all," he told Fortune, describing it as a laboratory experiment with no practical applications. "Quantum computers will never reign 'supreme' over classical computers, but will rather work in concert with them, since each have their unique strengths."

Jim Clarke, Intel Labs' director of quantum hardware, called Google's update a notable mile marker and said a commercially viable quantum computer would require many research and development advances. "While development is still at mile one of this marathon, we strongly believe in the potential of this technology," he said.

Google's paper, obtained by Fortune after being posted to NASA.gov and taken down, said the Sycamore processor contained 53 qubits, scaled back from a 72-qubit device called Bristlecone. The researchers wrote that their processor took about 200 seconds to sample one instance of a quantum circuit 1 million times, while a state-of-the-art supercomputer would need approximately 10,000 years for the equivalent task.

Google's spokesperson declined to confirm the paper's authenticity, and NASA did not immediately respond to a request for comment. A Google source suggested NASA published it early, before peer review. For all the claims, the field's own language, the NISQ era of noisy, intermediate-scale quantum computers described by Caltech's John Preskill, still describes the hardware on the table.

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Sources

4
  1. 01Google's New Chip Is a Stepping Stone to Quantum Computing SupremacyEN
  2. 02IBM says it's reached milestone in quantum computingEN
  3. 03Why Google Is Poised to Hit the Next Critical Milestone in Quantum ComputingEN
  4. 04Google Claims 'Quantum Supremacy,' Marking a Major Milestone in ComputingEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Rachel Nwosu

Rachel Nwosu

AI, models and technology

Rachel Nwosu covers AI, models and technology for FLASH24, working from public model documentation, benchmark releases and repository histories rather than press summaries, and she skips announcements that arrive without reproducible numbers. She checks training-data claims against dataset cards and reruns reported metrics where code is available. She spends much of her week interviewing researchers and engineers, tracking model launch calendars, and comparing vendor benchmarks with independent evaluations. Outside the desk she runs 3D printers, restores old computers, and tests how models learn from internet junk. She does not publish benchmark figures she cannot trace to a source.

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