Is Quantum the Next AI?

I stay ahead of major tech shifts for a living, but even I was stunned in November of 2022 when I tried ChatGPT’s first public release.

Within minutes, I told my team that it was a breakout moment for artificial intelligence.

Now, it appears quantum computing could soon achieve its own breakout moment.

I’ve been following quantum’s development for years. I’ve read every argument about how “practical quantum” is still decades away, and I’ve also heard plenty of overhyped announcements about how it’s actually just around the corner

But this time feels different.

Google CEO Sundar Pichai recently told the BBC that quantum computing is entering the same phase AI was in five years ago.

And when the head of one of the largest tech companies on the planet signals that quantum is about to accelerate, I pay attention. Because Big Tech usually only makes these kinds of predictions when it has the technology to back them up.

In Google’s case, I believe they do.

A Familiar Pattern

Google has been a leader in quantum breakthroughs.

In 2019, the company’s Sycamore processor completed a benchmark in 200 seconds that classical supercomputers would need thousands of years to reproduce.

At the time, it was dismissed as a narrow demonstration.

But last year, Google followed it with Willow, a more advanced machine that stunned researchers. On one benchmark, Willow completed a task so complex that the Frontier supercomputer at Oak Ridge National Laboratory would need more than 47 million years to match it.

This is why I’m paying keen attention to Pichai’s recent comments.

He said Google now has “state of the art” quantum systems in development and that the next five years will be a “very exciting phase.”

For someone known for understatement, that’s unusually strong rhetoric. It suggests that Google’s internal results are crossing thresholds the public isn’t privy to yet.

This lines up with what I told you back in February, when I wrote about Microsoft’s announcement of a quantum age transistor.

The company said it had built a device capable of creating more stable qubits. Qubits are the quantum version of your typical computer’s bits, and keeping them stable is the single hardest problem in quantum computing.

That kind of progress forced the rest of the industry to rethink its timelines. Within days, even Nvidia’s CEO had to soften his earlier claim that quantum machines were still many decades away.

Why the urgency?

Because the limits of classical computing are now bottlenecks for trillion-dollar industries.

Drug discovery is limited by how long it takes to simulate molecular interactions. Battery chemistry is slowed by the sheer number of atomic combinations that must be tested. Logistics networks, energy grids and advanced materials research all lean on models that hit hard computational walls.

Quantum doesn’t just make those tasks faster. It changes the scale of what’s computable.

That’s why the United States, China and the European Union are pouring billions into quantum programs.

Image: nextplatform.com

And it’s why a federal advisory panel recently urged Congress to establish a national quantum technology initiative aimed at staying ahead of China. Because just like with AI, quantum is quickly becoming a race for technological and economic advantage.

The country that reaches practical quantum computing first will gain advantages in security, intelligence, energy, materials and advanced manufacturing.

Now, that doesn’t mean that quantum is ready for mainstream use today.

“Quantum advantage” is the point when a quantum computer can solve useful, real-world problems that even our most powerful classical computers couldn’t solve in a reasonable timeframe.

And some companies — including Google — have claimed they’ve achieved this “quantum advantage.” But so far it has mostly meant beating classical computers on contrived benchmark tasks.

In other words, we’re not yet at the point where they can solve practical problems.

Error correction remains the defining engineering challenge, and large-scale systems will need thousands of stable qubits before they can tackle real commercial workloads.

But we’ve been here before.

In the years leading up to 2022, AI systems were impressive but unreliable. But as model size scaled and the hardware improved, everything seemed to change at once.

And suddenly, ChatGPT-3 was there to blow us away

Quantum appears to be entering this same pre-acceleration window.

Here’s My Take

Quantum is still in its early phase, but the recent comments from Google’s Pichai suggest that the next breakthrough could be closer than expected.

We are starting to see measurable results in quantum instead of theoretical projections. And I believe he’s looking at performance curves inside Google that the rest of the world hasn’t caught up to yet.

At the very least, the fact that he’s calling quantum “the next AI” is significant.

For investors, it could be a signal that the hype phase is ending and the build-out phase is beginning.

Over the next five years, I’m convinced we’ll start seeing major bets from Big Tech, governments and startups on quantum-native applications. And even if only some of them pay off, the winners could rewrite the rules of computing, pharma, energy, security and a host of other industries.

And once quantum crosses into commercial use, the pace of technological progress could accelerate far faster than anything we’ve seen in the AI boom.

Regards,

Ian KingChief Strategist, Banyan Hill Publishing

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