Quantum Computing: What I'm Seeing

Stanford's latest Emerging Technology Review landed on my desk last week. The funding numbers caught my attention: Quantum computing VC went from $1.13B in 2022 to $2.88B in 2025. Broader quantum tech, including sensing and communications, jumped from $2.9B to nearly $16B in the same period.

That's not "science project" money anymore. That's "we think this is real" money.

The question I keep asking: Are we seeing genuine technical progress, or is this another cycle of hype meeting capital? After digging into the Stanford report, I think we're seeing something real.

Here's what didn't make headlines but probably should have. In 2025, three different quantum hardware approaches, trapped ions, neutral atoms, and superconducting circuits, all crossed what researchers call the "break-even threshold" for error correction.

Why does this matter? Quantum computers are incredibly fragile. A tiny vibration, a slight temperature change, and your calculation is garbage. For decades, the errors accumulated faster than we could correct them. Now, for the first time, error correction actually works well enough to scale.

Stanford compares this to the moment transistors became viable for classical computing. I think that's apt. We're not at the iPhone yet, we're at the transistor radio. But the transistor radio was the hard part.

What fascinates me about quantum computing right now is that we don't know which approach will win. Classical computing had a clear victor, transistors beat vacuum tubes, silicon beat germanium, and we built an entire industry on that foundation.

Quantum doesn't have its winner yet.

Each approach has tradeoffs. Each has genuine technical merit. And each has raised serious capital. I find this uncertainty interesting rather than concerning. It reminds me of the early days of any platform shift, multiple architectures competing until the market reveals what actually works at scale.

The honest truth about quantum computing is that most of the promised applications are still speculative. Quantum machine learning? Probably overhyped. Quantum advantage in finance? The math doesn't support it yet.

But two applications look genuinely transformational:

For those of us watching the Indian ecosystem, there's an interesting dynamic emerging. The National Quantum Mission has committed ₹6,000 crore. IITs and IISc are building research programs. Indian IT services companies, the Infosys and TCS cohort, will need quantum capabilities as their global clients start exploring the technology.

I'm not predicting India will lead in quantum hardware. That race belongs to the US, China, and parts of Europe. But there's a talent angle here that's worth watching. Quantum computing needs people who understand both physics and engineering. India produces a lot of those people.

The question is whether the ecosystem can keep them and build around them. That's not a technology question, it's an institutional one.

I'll leave you with the frame I'm using to think about quantum:

Quantum computing in 2026 feels like AI around 2018. The technical foundations are solid. The commercial applications are emerging but not yet mainstream. The hype cycle has begun, which means both opportunity and noise.

The winners won't be whoever builds the most qubits. They'll be whoever solves real problems first, probably in security and simulation before anywhere else.

For now, I'm paying attention to the application layer and the enabling infrastructure. The platform wars will take a decade to resolve. The problems being solved on top of those platforms? Those are visible now.