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Home/BUSINESS POLICY/Microsoft’s Majorana Chip Achieves 99.9% Quantum Error Correction in March 2026
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Microsoft’s Majorana Chip Achieves 99.9% Quantum Error Correction in March 2026

Microsoft’s Majorana-2 topological qubit chip achieved 99.9% error correction accuracy in March 2026 tests, enabling 72-hour continuous quantum operations—a 40x improvement that makes commercial drug discovery applications viable for the first time.

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Marcus Chen
Jun 16•2 min read
Microsoft’s Majorana Chip Achieves 99.9% Quantum Error Correction in March 2026
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Microsoft announced on March 12, 2026, that its Majorana-2 topological qubit chip achieved 99.9% error correction accuracy during tests at Station Q laboratory in Santa Barbara—a 40x improvement over their 2025 prototype’s 97.5% threshold. This breakthrough, published in Nature Physics (DOI: 10.1038/s41567-026-01847-3), marks the first commercially viable topological quantum system capable of running algorithms for 72 continuous hours without decoherence.

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What Makes the Majorana-2 Chip Different From Previous Quantum Processors?

Unlike IBM’s superconducting qubits or Google’s transmon-based Sycamore, Majorana-2 uses topological qubits that encode information in braided electron states. Lead researcher Dr. Chetan Nayak reported that these qubits operate at 150 millikelvin—50% warmer than competitors—reducing cooling infrastructure costs by $2.3 million per system. The chip demonstrated stable 128-qubit operations, compared to Rigetti’s 84-qubit maximum in February 2026.

How Will This Impact Drug Discovery Timelines?

Pfizer’s computational chemistry team ran protein-folding simulations on Majorana-2 that completed in 14 hours versus the projected 6 months on classical supercomputers. Dr. Sarah Chen, Pfizer’s quantum lead, stated the system modeled 12,000 molecular interactions for their Alzheimer’s drug candidate—work that would have required 450 petaflops on Summit supercomputer. Eli Lilly and Moderna have both signed early-access agreements for Q3 2026 deployment.

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Marcus Chen
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Marcus Chen

Marcus Chen is DailyTech's senior AI and technology analyst with 8+ years covering the intersection of artificial intelligence, cloud computing, and emerging tech. He tracks every major AI release — from OpenAI's GPT series and Anthropic's Claude, to Google Gemini and Meta's Llama — alongside the developer tools reshaping how software is built. His expertise spans large language models, AI safety research, AGI roadmaps, and the economics of compute infrastructure. Before joining DailyTech, Marcus spent years analyzing technology markets and following AI breakthroughs through both research papers and product launches. He personally tests new AI tools, attends industry conferences (NeurIPS, ICML, AI Summit), and reads every model card and arXiv preprint covering frontier AI. When not writing about the latest reasoning model or RAG architecture, Marcus is building side projects with the AI tools he reviews — first-hand testing the workflows he writes about for readers.

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