Close Menu
  • Home
  • AI Models
    • DeepSeek
    • xAI
    • OpenAI
    • Meta AI Llama
    • Google DeepMind
    • Amazon AWS AI
    • Microsoft AI
    • Anthropic (Claude)
    • NVIDIA AI
    • IBM WatsonX Granite 3.1
    • Adobe Sensi
    • Hugging Face
    • Alibaba Cloud (Qwen)
    • Baidu (ERNIE)
    • C3 AI
    • DataRobot
    • Mistral AI
    • Moonshot AI (Kimi)
    • Google Gemma
    • xAI
    • Stability AI
    • H20.ai
  • AI Research
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Microsoft Research
    • Meta AI Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding & Startups
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • Expert Insights & Videos
    • Google DeepMind
    • Lex Fridman
    • Matt Wolfe AI
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • Matt Wolfe AI
    • The TechLead
    • Andrew Ng
    • OpenAI
  • Expert Blogs
    • François Chollet
    • Gary Marcus
    • IBM
    • Jack Clark
    • Jeremy Howard
    • Melanie Mitchell
    • Andrew Ng
    • Andrej Karpathy
    • Sebastian Ruder
    • Rachel Thomas
    • IBM
  • AI Policy & Ethics
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
    • EFF AI
    • European Commission AI
    • Partnership on AI
    • Stanford HAI Policy
    • Mozilla Foundation AI
    • Future of Life Institute
    • Center for AI Safety
    • World Economic Forum AI
  • AI Tools & Product Releases
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
    • Image Generation
    • Video Generation
    • Writing Tools
    • AI for Recruitment
    • Voice/Audio Generation
  • Industry Applications
    • Finance AI
    • Healthcare AI
    • Legal AI
    • Manufacturing AI
    • Media & Entertainment
    • Transportation AI
    • Education AI
    • Retail AI
    • Agriculture AI
    • Energy AI
  • AI Art & Entertainment
    • AI Art News Blog
    • Artvy Blog » AI Art Blog
    • Weird Wonderful AI Art Blog
    • The Chainsaw » AI Art
    • Artvy Blog » AI Art Blog
What's Hot

Paper page – Chat with AI: The Surprising Turn of Real-time Video Communication from Human to AI

China is turning AI into a commodity – Charles Ormond

MIT researchers warn of ‘PACMAN’ M1 flaw that can’t be patched

Facebook X (Twitter) Instagram
Advanced AI News
  • Home
  • AI Models
    • OpenAI (GPT-4 / GPT-4o)
    • Anthropic (Claude 3)
    • Google DeepMind (Gemini)
    • Meta (LLaMA)
    • Cohere (Command R)
    • Amazon (Titan)
    • IBM (Watsonx)
    • Inflection AI (Pi)
  • AI Research
    • Allen Institue for AI
    • arXiv AI
    • Berkeley AI Research
    • CMU AI
    • Google Research
    • Meta AI Research
    • Microsoft Research
    • OpenAI Research
    • Stanford HAI
    • MIT CSAIL
    • Harvard AI
  • AI Funding
    • AI Funding Database
    • CBInsights AI
    • Crunchbase AI
    • Data Robot Blog
    • TechCrunch AI
    • VentureBeat AI
    • The Information AI
    • Sifted AI
    • WIRED AI
    • Fortune AI
    • PitchBook
    • TechRepublic
    • SiliconANGLE – Big Data
    • MIT News
    • Data Robot Blog
  • AI Experts
    • Google DeepMind
    • Lex Fridman
    • Meta AI Llama
    • Yannic Kilcher
    • Two Minute Papers
    • AI Explained
    • TheAIEdge
    • The TechLead
    • Matt Wolfe AI
    • Andrew Ng
    • OpenAI
    • Expert Blogs
      • François Chollet
      • Gary Marcus
      • IBM
      • Jack Clark
      • Jeremy Howard
      • Melanie Mitchell
      • Andrew Ng
      • Andrej Karpathy
      • Sebastian Ruder
      • Rachel Thomas
      • IBM
  • AI Tools
    • AI Assistants
    • AI for Recruitment
    • AI Search
    • Coding Assistants
    • Customer Service AI
  • AI Policy
    • ACLU AI
    • AI Now Institute
    • Center for AI Safety
  • Industry AI
    • Finance AI
    • Healthcare AI
    • Education AI
    • Energy AI
    • Legal AI
LinkedIn Instagram YouTube Threads X (Twitter)
Advanced AI News
IBM

IBM discloses plans to build first large-scale fault-tolerant quantum computer

By Advanced AI EditorJune 11, 2025No Comments5 Mins Read
Share Facebook Twitter Pinterest Copy Link Telegram LinkedIn Tumblr Email
Share
Facebook Twitter LinkedIn Pinterest Email


IBM unveiled its path to build the world’s first large-scale, fault-tolerant quantum computer, setting the stage for practical and scalable quantum computing.

Delivered by 2029, IBM Quantum Starling will be built in a new IBM Quantum Data Center in Poughkeepsie, New York and is expected to perform 20,000 times more operations than today’s quantum computers.

To represent the state of an IBM Starling would require the memory of more than a quindecillion (10^48) of the world’s most powerful supercomputers.

With Starling, users will be able to fully explore the complexity of its quantum states, which are beyond the limited properties able to be accessed by current quantum computers.

IBM, which already operates a large, global fleet of quantum computers, is releasing a new Quantum Development Roadmap that outlines a viable and definitive plan to build out a practical, fault-tolerant quantum computer.

“IBM is charting the next frontier in quantum computing,” said Arvind Krishna, Chairman and CEO OF IBM, in a statement. “Our expertise across mathematics, physics and engineering is paving the way for a large-scale, fault-tolerant quantum computer — one that will solve real-world challenges and unlock immense possibilities for business.”

A large-scale, fault-tolerant quantum computer with hundreds or thousands of logical qubits could run hundreds of millions to billions of operations, which could accelerate time and cost efficiencies in fields such as drug development, materials discovery, chemistry, and optimization.

Starling will be able to access the computational power required for these problems by running 100 million quantum operations using 200 logical qubits. It will be the foundation for IBM Blue Jay, which will be capable of executing 1 billion quantum operations over 2,000 logical qubits.

A logical qubit is a unit of an error-corrected quantum computer tasked with storing one qubit’s worth of quantum information. It can be made from multiple physical qubits working together to store this information and monitor each other for errors.

Like classical computers, quantum computers need to be error corrected to run large workloads without faults. To do so, clusters of physical qubits are used to create a smaller number of logical qubits with lower error rates than the underlying physical qubits. Logical qubit error rates are suppressed exponentially with the size of the cluster, enabling them to run greater numbers of operations.

Creating increasing numbers of logical qubits capable of executing quantum circuits, with as few physical qubits as possible, is critical to quantum computing at scale. Until today, a clear path to building such a fault-tolerant system without unrealistic engineering overhead has not been published.

The path to large-scale fault tolerance

The success of executing an efficient fault-tolerant architecture is dependent on the choice of its error-correcting code, and how the system is designed and built to enable this code to scale, IBM said.

Alternative and previous gold-standard error-correcting codes present fundamental engineering challenges. To scale, they would require an unfeasible number of physical qubits to create enough logical qubits to perform complex operations – necessitating impractical amounts of infrastructure and control electronics. This renders them unlikely to be able to be implemented beyond small-scale experiments and devices, IBM said.

A large-scale, fault-tolerant quantum computer requires an architecture that is:

Fault-tolerant to suppress enough errors for useful algorithms to succeed.

Able to prepare and measure logical qubits through computation.

Capable of applying universal instructions to these logical qubits.

Able to decode measurements from logical qubits in real-time and can alter
subsequent instructions.

Modular to scale to hundreds or thousands of logical qubits to run more complex
algorithms.

Efficient enough to execute meaningful algorithms with realistic physical resources,
such as energy and infrastructure.

Today, IBM is introducing two new technical papers that detail how it will solve remaining criteria to build a large-scale, fault-tolerant architecture.

One paper unveils how such a system will process instructions and run operations effectively with qLDPC codes. This work builds on a groundbreaking approach to error correction featured on the cover of Nature that introduced quantum low-density parity check (qLDPC) codes. This code drastically reduces the number of physical qubits needed for error correction and cuts required overhead by approximately 90 percent, compared to other leading codes. Additionally, it lays out the resources required to reliably run large-scale quantum programs to prove the efficiency of such an architecture over others.

The second paper describes how to efficiently decode the information from the physical qubits, and charts a path to identify and correct errors in real-time with conventional computing resources.

From roadmap to reality

The new IBM Quantum Roadmap outlines the key technology milestones that will demonstrate and execute the criteria for fault tolerance. Each new processor in the roadmap addresses specific challenges to build quantum systems that are modular, scalable, and error-corrected.

IBM Quantum Loon, expected in 2025, is designed to test architecture components for the qLDPC code, including “c-couplers” that connect qubits over longer distances within the same chip.

IBM Quantum Kookaburra, expected in 2026, will be IBM’s first modular processor designed to store and process encoded information. It will combine quantum memory with logic operations — the basic building block for scaling fault-tolerant systems beyond a single chip.

IBM Quantum Cockatoo, expected in 2027, will entangle two Kookaburra modules using “L-couplers.” This architecture will link quantum chips together like nodes in a larger system, avoiding the need to build impractically large chips.

Together, these advancements are being designed to culminate in Starling in 2029.

Daily insights on business use cases with VB Daily

If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI.

Read our Privacy Policy

Thanks for subscribing. Check out more VB newsletters here.

An error occured.



Source link

Follow on Google News Follow on Flipboard
Share. Facebook Twitter Pinterest LinkedIn Tumblr Email Copy Link
Previous ArticleMIT Turns Soda Cans and Sea Water Into Green Hydrogen
Next Article Pittsburgh weekly roundup: Axios-OpenAI partnership; Buttigieg visits CMU; AI ‘employees’ in the nonprofit industry
Advanced AI Editor
  • Website

Related Posts

For Now, AI Helps IBM’s Bottom Line More Than Its Top Line

July 27, 2025

Earnings Shock: Why IBM, Chipotle, and American Airlines Tumbled—and What Comes Next

July 25, 2025

IBM raises 2025 free cash flow but stock dips

July 25, 2025
Leave A Reply

Latest Posts

David Geffen Sued By Estranged Husband for Breach of Contract

Auction House Will Sell Egyptian Artifact Despite Concern From Experts

Anish Kapoor Lists New York Apartment for $17.75 M.

Street Fighter 6 Community Rocked by AI Art Controversy

Latest Posts

Paper page – Chat with AI: The Surprising Turn of Real-time Video Communication from Human to AI

July 28, 2025

China is turning AI into a commodity – Charles Ormond

July 28, 2025

MIT researchers warn of ‘PACMAN’ M1 flaw that can’t be patched

July 28, 2025

Subscribe to News

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

Recent Posts

  • Paper page – Chat with AI: The Surprising Turn of Real-time Video Communication from Human to AI
  • China is turning AI into a commodity – Charles Ormond
  • MIT researchers warn of ‘PACMAN’ M1 flaw that can’t be patched
  • Chinese AI firms form alliances to build domestic ecosystem amid US curbs
  • AI startup Cohere in talks to raise funding at $6B plus valuation

Recent Comments

  1. binance推薦獎金 on [2407.11104] Exploring the Potentials and Challenges of Deep Generative Models in Product Design Conception
  2. психолог онлайн индивидуально on 1-800-CHAT-GPT—12 Days of OpenAI: Day 10
  3. GeraldDes on 1-800-CHAT-GPT—12 Days of OpenAI: Day 10
  4. binance sign up on Inclusion Strategies in Workplace | Recruiting News Network
  5. Rejestracja on Online Education – How I Make My Videos

Welcome to Advanced AI News—your ultimate destination for the latest advancements, insights, and breakthroughs in artificial intelligence.

At Advanced AI News, we are passionate about keeping you informed on the cutting edge of AI technology, from groundbreaking research to emerging startups, expert insights, and real-world applications. Our mission is to deliver high-quality, up-to-date, and insightful content that empowers AI enthusiasts, professionals, and businesses to stay ahead in this fast-evolving field.

Subscribe to Updates

Subscribe to our newsletter and never miss our latest news

Subscribe my Newsletter for New Posts & tips Let's stay updated!

LinkedIn Instagram YouTube Threads X (Twitter)
  • Home
  • About Us
  • Advertise With Us
  • Contact Us
  • DMCA
  • Privacy Policy
  • Terms & Conditions
© 2025 advancedainews. Designed by advancedainews.

Type above and press Enter to search. Press Esc to cancel.