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Intel Enters the Quantum Computing Horse Race With 12-Qubit Chip

But before quantum physics revolutionizes computing, Intel and rivals will have to learn how to make vastly more powerful machines.

Intel has built a quantum processor called Tunnel Falls that it will offer to research labs hoping to make the revolutionary computing technology practical.

The Tunnel Falls processor, announced Thursday, houses 12 of the fundamental data processing elements called qubits. It’s a major step in the chipmaker’s attempt to develop quantum computing hardware it hopes will eventually surpass rivals.

Intel, unlike most of its rivals, makes its qubits from individual electrons housed in computer chips that are cousins to those that power millions of PCs. The company is lagging behind. Rivals like IBM, Google, Quantinuum and IonQ have been offering quantum computers for years, but Intel believes tying its fortunes to conventional chip technology will ultimately enable faster progress.

«To me, it’s natural to use the tools already developed rather than having to develop new tools,» said Jim Clarke, director of quantum computing hardware at Intel Labs. Intel makes its own quantum computing chips at its D1 fab in Oregon.

You won’t buy your own quantum computer, but they could affect your life very directly. Among those investing in the technology are financial services companies seeking more profitable investments, materials science researchers hoping for better batteries, pharmaceutical companies trying to design better drugs and governments trying to crack adversaries’ encrypted communications.

Those challenges are out of reach of conventional computers, but quantum computing has the potential to tackle them by taking advantage of the weird physics of the ultrasmall. Today’s quantum computers aren’t generally practical, and the full promise of the technology remains years away, but physicists and engineers have made steady progress year after year.

Intel, an expert in large-scale manufacturing, hopes to help speed things along by building many quantum chips, which it calls quantum processing units, or QPUs. The University of Maryland, one of the centers benefiting from a US government program to accelerate quantum computing progress, will use Intel machines.

The quantum computing race

One notable feature of quantum computing is the tremendous variety of approaches. Intel is using electrons, storing data with a quantum mechanical property called spin that’s analogous to the two directions a top can spin. IBM and Google are using small electrical circuits of superconducting materials. IonQ and Quantinuum manipulate charged atoms stored in a trap. Other approaches involve neutral atoms and even that most fleeting of particles, the photon.

At a sufficiently small scale, quantum mechanics dominates physics and anything can become a qubit, quantum computing pioneer and MIT researcher Seth Lloyd said in an earlier interview. «It’s a question of whether you can massage them in the right way to convince them to compute.»

In other words, quantum computing isn’t a horse race like in the traditional computer chip market. It’s more like a horse pitted against a falcon, a motorcycle and an Olympic sprinter.

Intel likes its approach. Tunnel Falls is in manufacturing today, but the company very soon will «tape out» its successor, meaning the design is finished, and it’s begun designing the model after that, Clarke said. Twelve qubits is a tiny fraction of what’s needed for useful quantum computers, but Intel started with a simple approach designed for fast improvement and sustained progress over the years required to make serious quantum computers.

A tiny Intel Tunnel Falls quantum computer chip perched on a fingertip A tiny Intel Tunnel Falls quantum computer chip perched on a fingertip

Intel’s Tunnel Falls quantum computer test chip perched on a fingertip

Intel

«The next big milestone is when we have a few thousand qubits,» a quantity that will let quantum computer engineers correct the frequent errors that plague qubit operations, Clarke said. «That’s probably three, four years, maybe five years away,» Clarke said. «And it’s probably the early 2030s or mid-2030s before we have a million cubits that are going to change the world.»

And Intel is engineering not just the QPUs, but the crucial data links that link each qubit to the outside world. Today’s quantum computers often look like high-tech chandeliers, with gleaming metal communication conduits looping down toward the processor, but that bulky design won’t work with thousands or millions of qubits, and Intel believes its control chips and chip interconnect technology will be necessary parts of an overall system.

Plenty of competitors

Intel is unusual in selecting photons housed in computer circuits for its quantum computing foundation. One of its biggest rivals, IBM, already offers multiple 127-qubit quantum computers for research and commercial use, with a 433-qubit machine up and running.

«We have a plan to get this out to hundreds of thousands of qubits using superconducting qubits,» said Jerry Chow, leader of IBM’s quantum computing hardware effort. IBM is working on quantum computer chips with a flock of code names — Egret, Heron, Condor, Crossbill — that are designed to prove out new technologies to reduce errors and improve the qubit-to-qubit connections that are central to the machines.

And it’s making progress. On Wednesday, it secured a coveted spot on the cover of the journal Nature for research showing its 127-qubit Eagle quantum computing chip can surpass conventional machines in simulating the materials physics that produce effects like magnetism.

Intel tried and rejected the supercomputing qubit approach, Clarke said. Its spin qubits are a million times smaller than a superconducting circuit, letting the company fit 25,000 of them on each 300mm silicon wafer that transits through its chip fabrication plant, called a fab. When Intel finds a problem building quantum chips, it figures out how to adapt the qubit to traditional chip manufacturing, not vice versa.

Disagreement with Intel’s approach

Such arguments haven’t persuaded others. Google is sticking with superconducting qubits.

«Superconducting qubits lead in critical metrics. We are confident they are the leading technology for the future of quantum supercomputers,» Google said in a statement, pointing to their processing speed and progress toward error correction to keep calculations on track longer. «We see a clear path to scale our technology to large-scale, error-corrected machines of general use.»

And IonQ Chief Executive Peter Chapman believes Intel’s approach is too inflexible for practical, large-scale quantum computers. His company is developing ion trap machines that scoot charged atoms around, letting different qubits interact with each other for computation. Fixing qubits onto the surface of a chip drastically complicates computations, he said.

«What worked in computing in the past — silicon-based processors — is not the right solution for the age of quantum,» Chapman said.

The deep disagreements about the best approach will perhaps be resolved as the machines evolve and grow larger. Intel’s plans rely on its manufacturing advantage, tapping into its experience building some of the most complicated electronics devices on the planet.

«Not everybody has a fab like this in their back pocket,» Clarke said.

Technologies

Google races to put Gemini at the center of Android before Apple’s AI reboot

Google is using its latest Android rollout to position Gemini as the AI layer across phones, Chrome, laptops and cars.

Google is using its latest Android rollout to make Gemini less of a chatbot and more of an operating layer across the phone, browser, car and laptop, just weeks before Apple is expected to show its own Gemini-powered Apple Intelligence reboot at WWDC.
Ahead of its Google I/O developer conference next week, the company previewed a number of Android updates, including AI-powered app automation, a smarter version of Chrome on Android, new tools for creators, a redesigned Android Auto experience, and a sweeping set of new security features.
Alphabet is counting on Gemini to help Google compete directly with OpenAI and Anthropic in the market for artificial intelligence models and services, while also serving as the AI backbone across its expansive portfolio of products, including Android. Meanwhile, Gemini is powering part of Apple’s new AI strategy, giving Google a role in the iPhone maker’s reset even as it races to prove its own version of personal AI on the phone is further along.
Sameer Samat, who oversees Google’s Android ecosystem, told CNBC that Google is rebuilding parts of Android around Gemini Intelligence to help users complete everyday tasks more easily.
“We’re transitioning from an operating system to an intelligence system,” he said.
As part of Tuesday’s announcements. Google said Gemini Intelligence will be able to move across apps, understand what’s on the screen and complete tasks that would normally require a user to jump between multiple services. That means Android is moving beyond the traditional assistant model, where users ask a question and get an answer, and acting more like an agent.
For instance, Google says Gemini can pull relevant information from Gmail, build shopping carts and book reservations. Samat gave the example of asking Gemini to look at the guest list for a barbecue, build a menu, add ingredients to an Instacart list and return for approval before checkout.
A big concern surrounding agentic AI involves software taking action on a user’s behalf without permissions. Samat said Gemini will come back to the user before completing a transaction, adding, “the human is always in the loop.”
Four months after announcing its Gemini deal with Google, Apple is under pressure to show a more capable version of Apple Intelligence, which has been a relative laggard on the market. Apple has long framed privacy, hardware integration and control of the user experience as its advantages.
Google’s Android push is designed to show it can bring AI deeper into the device experience while still giving users control over what Gemini can see, where it can act and when it needs confirmation.
The app automation features will roll out in waves, starting with the latest Samsung Galaxy and Google Pixel phones this summer, before expanding across more Android devices, including watches, cars, glasses and laptops later this year.
The company is also redesigning Android Auto around Gemini, turning the car into another major surface for its assistant. Android Auto is in more than 250 million cars, and Google says the new release includes its biggest maps update in a decade and Gemini-powered help with tasks like ordering dinner while driving.
Alphabet’s AI strategy has been embraced by Wall Street, which has pushed the company’s stock price up more than 140% in the past year, compared to Apple’s roughly 40% gain. Investors now want to see how Gemini can become more central to the products people use every day.
WATCH: Alphabet briefly tops Nvidia after report of $200 billion Anthropic cloud deal

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Waymo recalls 3,800 robotaxis after glitch allowed some vehicles to ‘drive into standing water’

Waymo issued a voluntary recall of about 3,800 of its robotaxis to fix software issues that could allow them to drive into flooded roadways.

Waymo is recalling about 3,800 robotaxis in the U.S. to fix software issues that could allow them to “drive onto a flooded roadway,” according to a letter on the National Highway Traffic Safety Administration’s website.
The voluntary recall is for Waymo vehicles that use the company’s fifth and sixth generation automated driving systems (or ADS), the U.S. auto safety regulator said in the letter posted Tuesday.
Waymo autonomous vehicles in Austin, Texas, were seen on camera driving onto a flooded street and stalling, requiring other drivers to navigate around them. It’s the latest example of a safety-related issue for the Alphabet-owned AV unit that’s rapidly bolstering its fleet of vehicles and entering new U.S. markets.
Waymo has drawn criticism for its vehicles failing to yield to school buses in Austin, and for the performance of its vehicles during widespread power outages in San Francisco in December, when robotaxis halted in traffic, causing gridlock.
The company said in a statement on Tuesday that it’s “identified an area of improvement regarding untraversable flooded lanes specific to higher-speed roadways,” and opted to file a “voluntary software recall” with the NHTSA.
“Waymo provides over half a million trips every week in some of the most challenging driving environments across the U.S., and safety is our primary priority,” the company said.
Waymo added that it’s working on “additional software safeguards” and has put “mitigations” in place, limiting where its robotaxis operate during extreme weather, so that they avoid “areas where flash flooding might occur” in periods of intense rain.
WATCH: Waymo launches new autonomous system in Chinese-made vehicle

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Qualcomm tumbles 13% as semiconductor stocks retreat from historic AI-fueled surge

Semiconductor equities reversed sharply after a broad AI-driven advance, with Qualcomm suffering its worst day since 2020 amid inflation concerns and rising oil prices.

Semiconductor stocks fell sharply on Tuesday, reversing course after an extensive rally that had expanded the artificial intelligence investment theme well past Nvidia and driven the industry to unprecedented levels.

Qualcomm plunged 13% and was on track for its steepest single-day decline since 2020. Intel shed 8%, while On Semiconductor and Skyworks Solutions each lost more than 6%. The iShares Semiconductor ETF, which benchmarks the overall sector, fell 5%.

The sell-off came after a key gauge of consumer prices came in above forecasts, and as conflict in Iran pushed crude oil higher—prompting investors to shift away from riskier assets.

The preceding advance had widened the AI opportunity set beyond longtime industry leader Nvidia, which for much of the past several years had largely carried the market to new peaks on its own.

Explosive appetite for central processing units, along with the graphics processing units that power large language models, has sent chipmakers to all-time highs.

Market participants are wagering that the shift from AI model training to autonomous agents will lift demand for additional AI hardware. Among the beneficiaries are memory chip producers, which are raising prices as supply remains tight.

Micron Technology slid 6%, and Sandisk cratered 8%. Sandisk’s stock has surged more than six times over since January.

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