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IBM’s 1,121-Qubit Condor Chip Pushes Quantum Computing Past Error Correction Milestone

IBM’s 1,121-qubit Condor processor breaks the 1,000-qubit barrier while keeping error rates below 0.1% per gate operation, bringing practical quantum computing applications within reach by 2026.

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Marcus Chen
1h ago•2 min read
IBM’s 1,121-Qubit Condor Chip Pushes Quantum Computing Past Error Correction Milestone
24.5KTrending

IBM unveiled its 1,121-qubit Condor processor in December 2023, marking the first quantum chip to exceed 1,000 qubits while maintaining error rates below 0.1% per gate operation. This breakthrough addresses the primary obstacle preventing quantum computers from solving real-world problems: keeping qubits stable long enough to complete calculations.

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What Makes This Different From Previous Quantum Processors?

Condor uses a new hexagonal qubit layout that reduces crosstalk between neighboring qubits by 40% compared to IBM’s 433-qubit Osprey chip from 2022. The architecture connects qubits through tunable couplers, allowing researchers to isolate specific qubits during operations. Google’s 70-qubit Sycamore processor, by contrast, uses fixed coupling that limits error correction options.

When Will Businesses Actually Use This Technology?

IBM expects organizations to run practical quantum applications by late 2026. Current partners including JPMorgan Chase and ExxonMobil are testing molecular simulation and optimization algorithms on 127-qubit systems. The company’s roadmap includes a 4,000-qubit system by 2025 before commercial deployment.

Which Industries Benefit First?

Pharmaceutical companies will likely see initial returns through drug discovery simulations. Materials science applications follow closely, with battery chemistry optimization showing promise in early tests. Financial modeling remains 3-5 years out, according to MIT quantum researcher Dr. William Oliver, due to algorithm development delays.

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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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IBM’s 1,121-Qubit Condor Chip Pushes Quantum Computing Past Error Correction Milestone

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