Majorana 2: Microsoft's new quantum chip

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How Microsoft’s new quantum chip was made 1,000x more reliable with the help of Microsoft Discovery’s agentic AI

The news #

Microsoft unveils Majorana 2, its next-generation topological quantum chip developed with the help of Microsoft Discovery’s agentic AI. - Majorana 2’s new features include a new materials stack enabling a 1,000-fold improvement in reliability over the prior generation of qubits, with a mean qubit lifetime of 20 seconds and instances lasting as long as one minute.
Microsoft now expects to achieve a scalable quantum computer by 2029, cutting its original timeline in half.
Microsoft Discovery is now generally available. The platform for Frontier R&D lets customers deploy AI agent teams, guided by human expertise, to speed up scientific discovery.
The new Microsoft Discovery app provides a local version of the platform’s core capabilities that individuals can download for free and use with a GitHub Copilot account.

Microsoft today unveiled Majorana 2, its newest topological quantum chip featuring a next-generation materials stack and qubits that are 1,000 times more reliable than their predecessors. With this progress, the team now expects to achieve a scalable quantum computer by 2029, cutting its original timeline in half.

By applying recent advances in agentic AI specially designed to speed the scientific process and accelerate collaboration, Microsoft’s quantum team is overcoming key barriers in reliability, speed and size that have limited the application of quantum computing to real-life scenarios.

For instance, the new chip’s qubits can maintain their quantum state 1,000 times longer than the first generation, enabling more reliable computation. While other common approaches measure a qubit’s “lifetime” in microseconds, Majorana 2 offers a mean qubit lifetime of 20 seconds, with some instances lasting as long as one minute. That improvement is roughly comparable to inventing a phone battery that instead of dying in a day could last for nearly three years on a single charge. This exceptional reliability, fast speed (one microsecond operations) and small qubit size (1/100th of a millimeter) have put the team on a path to achieve a scalable quantum computer that is commercially valuable by 2029. Such a machine could tackle intractable problems in global health, food supply, sustainability, energy production and more, the company said.

“We need to make improvements each year that will get us closer to delivering a computer that we believe will have massive commercial and societal value,” said Chetan Nayak, Microsoft technical fellow. “We’ve got to keep marching to that roadmap to accomplish that, but where are we relative to last year? We’re 1,000 times better.”

Now, others searching for scientific or engineering breakthroughs can leverage the same agentic AI expertise that Microsoft’s own quantum team is using in its Majorana program.

The company also announced today the general availability of Microsoft Discovery, its comprehensive platform for organizations to embrace Frontier R&D. This combines specialized AI agents for scientific research and development, a Discovery Engine that drives research and reasoning workflows, plus enterprise-level security, governance and transparency.

Microsoft also introduced in early preview a Microsoft Discovery app with core capabilities that individuals can download for free and run locally on their computers with a GitHub Copilot account, lowering the barrier to entry for advanced AI-driven research.

Microsoft Discovery allows researchers to deploy autonomous agent teams, guided by human expertise, that can reason over large amounts of knowledge, generate hypotheses, optimize experiments, validate theories and learn in a continuous loop. Built-in controls help ensure that the research remains aligned with priorities, security and compliance standards, and safety requirements.

“In the year since we launched, we’ve seen customers light up use cases across critical industries like life sciences, chemicals and materials, energy, manufacturing and consumer goods,” said Aseem Datar, corporate vice president, product innovation for Microsoft Discovery. “With companies like Syensqo developing next-generation fluids for semiconductor manufacturing, the opportunities for impact are vast.”

The quantum team’s own scientists and engineers have been using the agentic AI capabilities in Microsoft Discovery to manage workflows, automate measurements, optimize fabrication, pinpoint previously unnoticed flaws and propose new solutions.

“Agentic AI has permeated almost everything we do—it’s just become kind of a very natural part of our workflow,” Nayak said.

“The agents can really accelerate things as much or as little as you want. It can be as little as pulling information together and summarizing it, or it can go further down the road of synthesizing it more or generating an interesting hypothesis. I think that’s extremely powerful right now.”

Agentic AI can help find new materials #

Majorana 1, introduced just last year, was revolutionary because it employed a topological superconductor, a special category of material that can create an entirely new state of matter that allows for more stable quantum computing. To improve on the original proof of concept, the team revisited the materials stack.

The original Majorana superconductor used aluminum, but Majorana 2 uses lead, which is commonly used to shield people and equipment from radiation in hospitals and industrial settings. In a quantum computer, a lead superconductor helps shield fragile qubits from cosmic disturbances that can make them unstable—but it took years to figure out how to overcome other tradeoffs. “That was actually a fairly large change, and it led to big, big improvements in device quality,” Nayak said.

While this line of materials research began long before the advent of agentic AI, the team used it to help manage the manufacturing of the new device, and Microsoft Discovery is being used more extensively for future Majorana materials work. Critical parts of the Majorana quantum devices are designed atom by atom. To keep each atom in its correct spot, another material, an impurity, may be added to the crystalline structure. But adding too much or in the wrong way disturbs it, so it’s a difficult balance to strike, said Zulfi Alam, corporate vice president for quantum at Microsoft.

“Finding the exact recipe, the right amount to put to get the desired energy structure, requires a lot of experimentation in the old world order. In the new world order, through simulations, you can see where the highly probable target is. And then with that knowledge, you ideally only have to experiment once,” he said.

Agentic AI can analyze information at scale #

The quantum computing project has many moving parts—software, architecture, design, the materials stack, fabrication processes, measurements and so on. A change in one area has ramifications that may require compensating elsewhere. AI agents help the team keep track of such complex, interrelated connections, Nayak said.

The quantum project also has huge quantities of data—nearly two decades’ worth, in many different formats. Before AI, the data was stuck in silos. “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 across that much data,” Alam said.

In addition, the quantum team is spread across multiple countries, with very different specialties, such as physics, mechanical engineering and process engineering. It’s impossible for any one person to be expert in everything. It’s a common problem in interdisciplinary scientific research, which is why Microsoft’s quantum team created an AI agent for organizing and analyzing information and making it easier for others to find.

“The AI is able to synthesize knowledge from all these different disciplines,” Alam said, saving everyone the time and hassle of interviewing the specialists or of reading up on another subject. The agentic AI can “parallel process so much information in super short time to give you a recommendation,” he said. The AI only offers guidance; it doesn’t decide. “It’s always ‘scientist in the loop’.”

Agentic AI can speed experiments #

Creating a topological state requires setting hundreds of parameters. Then measurement, which is the key to performing quantum computations, can start. When done by a person, these processes each take weeks. In fact, measurement is so difficult and time consuming that the team had tried to automate it a few years ago using earlier forms of machine learning, but it wasn’t possible, Alam said.

Using agentic capabilities available in Microsoft Discovery, the team was able to create an AI agent specialized for this job, which cut the cycle time by orders of magnitude, he said. AI’s pattern-recognition abilities helped with the difficult task of measuring what state the qubit is in and detecting whether there’s an even or odd number of billions of electrons on a semiconductor wire. AI agents run the process automatically and continuously, building a 3D map of the conditions that a single scientist would never be able to do in the same way, Alam said.

“Using agentic AI to automate the measurements was a game changer,” he said. “It goes through some math and starts saying, ‘Hey, where do I find the lowest point where everything sort of works?’ And it can do all these voltage adjustments in parallel, which a human cannot do. The way our minds work, we are more linear.”

Agentic AI can quiet the noise #

Data isn’t information—it needs to be filtered, analyzed and put into context to have meaning. For example, the team developed an AI agent that was able to combine physics, device and institutional knowledge to filter raw data from the quantum team’s fabrication process and sniff out an uncalibrated temperature sensor reading that was throwing things off.

Alam compares the process to the AI summary of a Teams call, which skips over friendly banter to list the three or four key points. “That’s exactly what the AI is doing on a grander scale when science is involved,” he said.

Microsoft Discovery was built as a platform that pairs AI with the scientific method, and many of the agentic AI tools that the quantum team is using are transferable and relevant to scientific exploration in other domains.

This fundamentally new type of Frontier R&D lets a scientist “be the anchor point and look at many, many different disciplines all at the same time with a very high fidelity and be able to draw correlations from that,” Alam said. “It is the essence of what every single high-performance, cutting-edge team wants to do.”

Related links:

Learn more: [Microsoft’s Majorana 2 is here](https://news.microsoft.com/azure-quantum/)

Read more: [Majorana 2 – Microsoft’s scalable quantum processor with reliable, long-lasting qubits](https://aka.ms/m2blog)

Read more: [20 Second Parity Lifetime in an InAs-Pb Device](https://aka.ms/m2-paper)

Download: [Microsoft Discovery app](https://aka.ms/MicrosoftDiscoveryApp)

Learn more: [Microsoft Discovery](https://azure.microsoft.com/en-us/solutions/discovery?msockid=0d710b8d313360371e1f1f27301e6148)

Lead photo: Majorana 2, a next-generation quantum chip built with the help of Microsoft Discovery’s agentic AI. Photo by John Brecher for Microsoft.

Catherine Bolgar writes about AI and innovation at Microsoft, from advances in quantum computing to how AI is helping ordinary people. Previously, Catherine wrote about technology and business for a number of publications, and she was an editor at the Wall Street Journal in New York and Brussels. She taught high school math in Kenya, where she learned Swahili. She currently lives in France. You can contact Catherine on LinkedIn.

source & further reading

news.microsoft.com — original article