Microsoft’s Majorana 2 quantum chip is also a case study for agentic AI in R&D

Microsoft’s Majorana 2 quantum chiparrived this week, with numbers that are genuinely difficult to contextualise: qubits 1,000 times more reliable than those of the first generation models, a mean qubit lifetime of 20 seconds against an industry norm measured in microseconds, and a revised roadmap targeting a commercially scalable quantum computer by 2029. Behind those numbers is Microsoft Discovery agentic AI, and that platform is arguably the more consequential part of this announcement.

To put that in plain terms: most quantum chips today can hold their fragile computational state for a fraction of a second before losing it. Majorana 2 holds it for up to a minute. Microsoft’s own analogy is a phone battery that, instead of dying in a day, lasts nearly three years on a single charge.

Majorana 2 was developed with the help of Microsoft Discovery, the company’s agentic AI platform for scientific R&D, which also reached general availability this week. The timing is deliberate. The quantum chip is Microsoft’s proof that the platform works.

What Microsoft Discovery agentic AI did

The common read on this story is that AI designed the chip. The reality is more specific, and arguably more interesting. The decision to switch the superconducting material from aluminium to lead, which Microsoft says is the single change most responsible for the reliability improvement, came out of years of conventional materials research, not an AI recommendation.

What Microsoft Discovery’s agents did were things around that: managing fabrication workflows, automating measurements that previously took weeks each, breaking down nearly two decades of siloed research data, and surfacing correlations that no single researcher could hold in their head in that volume and variety of information.

“As you run AI agents on this data, they’re able to essentially resynthesize and make correlations that we as humans cannot see because no single individual has that much vision in that much data,” said Zulfi Alam, corporate vice president for quantum at Microsoft. “In the new world order, through simulations, you can see where the probable target is. And then with that knowledge, you ideally only have to experiment once,” Alam said.

The measurement problem

One of the more concrete wins the team describes involves qubit measurement; the process of detecting quantum states by determining whether there’s an even or odd number of billions of electrons on a semiconductor wire. When done manually, this takes weeks. Microsoft tried to automate it a few years ago using earlier machine learning and couldn’t.

Built on Microsoft Discovery, the team have been building three-dimensional maps of qubit conditions. “Using agentic AI to automate the measurements was a game changer,” Alam said. The agent handles parallel voltage adjustments in hundreds of parameters simultaneously, something human researchers, thinking linearly and structurally, cannot do.

Microsoft Discovery goes general

The platform that underpinned all of this is now available to enterprise customers. Microsoft Discovery combines specialised AI agents for scientific research, a Discovery Engine for research and reasoning workflows, and enterprise-level security and governance. A free Microsoft Discovery app, usable locally with a GitHub Copilot account, is also in early preview.

Microsoft’s revised quantum timeline, moving from 2033 to 2029, was based on Majorana 2’s progress, which is a acceleration, but quantum road-maps have a history of optimistic compression. The 1,000x reliability figure refers specifically to improvements over Majorana 1’s qubits, not a direct benchmark against competing approaches from IBM or Google, which use fundamentally different architectures.

Nayak said: “Where are we relative to last year? We’re 1,000 times better.”

See also: UK and Germany plan to commercialise quantum supercomputing

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