Current Trends in Quantum Computing Development

We may be standing at a moment in time for Quantum Computing that mirrors the 2017 breakthrough on transformers – a spark that ignited the generative AI revolution 5 years later. With recent advancements from Google, Microsoft, IBM and Amazon in developing more powerful and stable quantum chips, the trajectory of QC is accelerating faster than many of us expected.   Google’s Sycamore and next gen Willow chips are demonstrating increasing fidelity. Microsoft’s pursuit of topological qubits using Majorana particles promises longer coherence times and IBM’s roadmap is pushing towards modular error corrected systems. These aren’t just incremental steps, they are setting the stage for scalable, fault tolerant quantum machines.   Quantum systems excel at simulating the behavior of molecules and materials at atomic scale, solving optimization problems with exponentially large solution spaces and modeling complex probabilistic systems – tasks that could take classical supercomputers millennia. For example, accurately simulating protein folding or discovering new catalysts for carbon capture are well within quantum’s potential reach.   If scalable QC is just five years away, now is the time to ask : What would you do differently today, if quantum was real tomorrow ?. That question isn’t hypothetical – it’s an invitation to start rethinking foundational problems in chemistry, logistics, finance, AI and cryptography.   Of course building quantum systems is notoriously hard. Fragile qubits, error correction and decoherence remain formidable challenges. But globally public and private institutions are pouring resources into cracking these problems. I was in LA today visiting the famous USC Information Sciences Institute where cutting edge work on QC is underway and the energy is palpable.   This feels like a pivotal moment. One where future shaping ideas are being tested in real labs. Just as with AI, the future belongs to those preparing for it now. QC Is an area of emphasis at Visa Research and I hope it is part of how other organizations are thinking about the future too.

IBM to Launch the Largest Quantum Computer Yet in 2025 Overview: IBM plans to build the largest quantum computer to date by linking multiple smaller quantum chips in parallel. The project, set for 2025, aims to shatter existing records for qubit count, marking a significant leap in quantum computing capabilities. IBM’s goal is to more than triple the size of the largest current quantum machine while advancing practical quantum computing applications. Key Details: 1. IBM’s Quantum Roadmap: • IBM’s largest current quantum chip, Condor, contains 1,121 qubits. • By 2025, IBM plans to interconnect multiple chips to exceed this number, ultimately aiming to triple the largest existing system. 2. Milestone Achievements: • The company has successfully demonstrated linking two quantum chips, a key step toward building larger, interconnected systems. • This modular approach allows IBM to scale quantum systems beyond the physical and error-correction limits of single chips. 3. Quantum Computing Use Cases: • IBM provides cloud access to its quantum systems, with most users currently utilizing about 100 qubits for practical tasks. • The expansion to larger systems will enable more complex computations in fields like drug discovery, materials science, and logistics optimization. The Significance of More Qubits: 1. Increased Computational Power: • More qubits enable quantum systems to solve problems exponentially faster than classical computers. 2. Error Correction: • Scaling qubits allows for improved quantum error correction, a critical barrier to achieving reliable quantum computations. 3. Broader Accessibility: • Larger systems will allow more researchers and industries to access practical quantum applications via IBM’s cloud platform. IBM’s Competition in Quantum Computing: 1. Atom Computing Holds Current Record: • Start-up Atom Computing currently holds the record for the largest quantum system, slightly surpassing IBM. 2. Tech Industry Quantum Race: • Competitors like Google, Rigetti, and IonQ are also racing to scale up their quantum systems. 3. IBM’s Modular Strategy: • IBM’s approach focuses on scaling through chip interconnection, which could sidestep the limitations of monolithic single-chip systems. The Takeaway: IBM’s 2025 quantum computer project aims to break new ground by creating the largest quantum system ever built, leveraging interconnected quantum chips to scale qubit counts. While significant technical challenges remain—particularly around error correction and chip interconnectivity—the initiative marks a critical step toward practical, large-scale quantum computing. With competitors like Atom Computing and Google also advancing rapidly, the race for quantum supremacy intensifies, promising transformative impacts across science, industry, and technology in the near future.

The last two days have seen two extremely interesting breakthroughs announced in quantum computing. There is a long path ahead, but these both point to the potential for dramatically upscaling ambitions for what's possible in relatively short timeframes. The most prominent advance was Microsoft's announcement of Majorana 1, a chip powered by "topological qubits" using a new material. This enables hardware-protected qubits that are more stable and fault-tolerant. The chip currently contains 8 topologic qubits, but it is designed to house one million. This is many orders of dimension larger than current systems. DARPA has selected the system for its utility-scale quantum computing program. Microsoft believes they can create a fault-tolerant quantum computer prototype in years. The other breakthrough is extraordinary: quantum gate teleportation, linking two quantum processes using quantum teleportation. Instead of packing millions of qubits into a single machine—which is exceptionally challenging—this approach allows smaller quantum devices to be connected via optical fibers, working together as one system. Oxford University researchers proved that distributed quantum computing can perform powerful calculations more efficiently than classical systems. This could not only create a pathway to workable quantum computers, but also a quantum internet, enabling ultra-secure communication and advanced computational capabilities. It certainly seems that the pace of scientific progress is increasing. Some of the applications - such as in quantum computing - could have massive implications, including in turn accelerating science across domains.

Google and IBM believe first workable quantum computer is in sight - meanwhile Europe offers a more collaborative vision Yesterday, both Google and IBM signalled that quantum computing is entering its engineering phase: Google’s Willow chip, introduced in December 2024, demonstrated scalable error correction: as more qubits were added, error rates dropped exponentially. It completed a benchmark task in under five minutes - one that would take today’s fastest supercomputer an unimaginable 10⁻²⁵ years (i.e., 10 septillion years). IBM revealed a detailed blueprint for industrial-scale quantum, outlining a path to building a fault-tolerant quantum supercomputer by late 2029. Meanwhile, real-world applications are already emerging: IBM and Moderna have collaborated to simulate the longest mRNA sequence (60 nucleotides) ever modelled on a quantum computer, using 80 of the 156 qubits on IBM’s Heron chip. They applied a clever algorithm (CVaR-based VQA) that has made earlier attempts at 42 nucleotides seem modest. Now contrast that with Europe’s collaborative approach. Instead of centralised lab efforts, Europe is deploying nine quantum systems across at least seven countries - spanning superconducting, ion-trap, and annealing technologies - integrated with national supercomputing centers for shared access and resilience. I recently visited the Poznań Supercomputing Centre in Poland to witness one of these systems in action. Europe’s model is about collective strength, diversity, and building long-term quantum infrastructure - demonstrating that the race isn’t just about breakthroughs, but also how you organise for scale and inclusivity.

NVIDIA CEO Jensen Huang recently claimed that practical quantum computing is still 15 to 30 years away and will require NVIDIA #GPUs to build hybrid quantum/classical supercomputers. But both the timeline and the hardware assumption are off the mark. Quantum computing is progressing much faster than many realize. Google’s #Willow device has demonstrated that scaling up quantum systems can exponentially reduce errors, and it achieved a benchmark in minutes that would take classical supercomputers countless billions of years. While not yet commercially useful, it shows that both quantum supremacy and fault tolerance are possible. PsiQuantum, a company building large-scale photonic quantum computers, plans to bring two commercial machines online well before the end of the decade. These will be 10,000 times larger than Willow and will not use GPUs, but rather custom high-speed hardware specifically designed for error correction. Meanwhile, quantum algorithms are advancing rapidly. PsiQuantum recently collaborated with Boehringer Ingelheim to achieve over a 200-fold improvement in simulating molecular systems. Phasecraft, the leading quantum algorithms company, has developed quantum-enhanced algorithms for simulating materials, publishing results that threaten to outperform classical methods even on current quantum hardware. Algorithms are improving 1000s of times faster than hardware, and with huge leaps in hardware from PsiQuantum, useful quantum computing is inevitable and increasingly imminent. This progress is essential because our existing tools for simulating nature, particularly in chemistry and materials science, are limited. Density Functional Theory, or DFT, is widely used to model the electronic structure of materials but fails on many of the most interesting highly correlated quantum systems. When researchers tried to evaluate the purported room-temperature superconductor LK-99, #DFT failed entirely, and researchers were forced to revert to cook-and-look to get answers. Even cutting-edge #AI models like DeepMind’s GNoME depend on DFT for training data, which limits their usefulness in domains where DFT breaks down. Without more accurate quantum simulations, AI cannot meaningfully explore the full complexity of quantum systems. To overcome these barriers, we need large-scale quantum computers. Building machines with millions of qubits is a significant undertaking, requiring advances in photonics, cryogenics, and systems engineering. But the transition is already underway, moving from theoretical possibility to construction. Quantum computing offers a path from discovery to design. It will allow us to understand and engineer materials and molecules that are currently beyond our reach. Like the transition from the stone age to ages of metal, electricity, and semiconductors, the arrival of quantum computing will mark a new chapter in our mastery of the physical world.

Google has made significant strides in quantum computing with the development of its latest quantum chip, Willow. This chip represents a major advancement toward building practical, large-scale quantum computers capable of solving complex problems far beyond the reach of classical supercomputers. Key Features of Willow: (1) Enhanced Qubit Count: Willow boasts 105 qubits, nearly doubling the count from its predecessor, the Sycamore chip. This increase enables more complex computations and improved error correction capabilities. (2) Error Correction Breakthrough: A notable achievement with Willow is its ability to reduce errors exponentially as the system scales. This addresses a fundamental challenge in quantum computing, where qubits are highly sensitive and prone to errors. By effectively managing these errors, Willow paves the way for more reliable quantum computations. (3) Unprecedented Computational Speed: In benchmark tests, Willow completed a complex computation in under five minutes—a task that would take the most advanced classical supercomputers an estimated 10 septillion years. This dramatic speedup underscores the potential of quantum computing to tackle problems currently deemed intractable. Implications and Future Prospects: The advancements demonstrated by Willow have profound implications across various fields: (4) Cryptography: The immense processing power of quantum computers like Willow could potentially break current cryptographic systems, prompting a reevaluation of data security measures. However, experts note that while Willow's 105 qubits are impressive, breaking encryption such as that used by Bitcoin would require a quantum computer with around 13 million qubits. Therefore, while the threat is not immediate, it is a consideration for the future. (5) Scientific Research: Quantum computing can revolutionize fields like drug discovery, materials science, and complex system modeling by performing simulations and calculations at unprecedented speeds. Artificial Intelligence: The ability to process vast datasets and perform complex optimizations rapidly could significantly enhance AI development and deployment. While Willow marks a significant milestone, the journey toward fully functional, large-scale quantum computers continues. Ongoing research focuses on further increasing qubit counts, enhancing error correction methods, and developing practical applications for this transformative technology.

𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴: 𝗔 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝘁𝗵𝗲 𝗛𝗼𝗿𝗶𝘇𝗼𝗻 🚀 Quantum computing represents a paradigm shift in how we approach computation. Unlike classical computers that use bits (0 or 1), quantum computers leverage qubits. Qubits can exist in multiple states simultaneously due to superposition, allowing quantum computers to explore countless possibilities and solve complex problems exponentially faster. This opens doors to breakthroughs in fields ranging from medicine and materials science to finance and artificial intelligence. 𝗪𝗶𝗹𝗹𝗼𝘄 (𝗚𝗼𝗼𝗴𝗹𝗲) Google's "Willow" chip showcases substantial progress in both quantum error correction and performance. Willow has achieved "below threshold" error rates, meaning that as the number of qubits scales up, errors decrease exponentially. It also achieved a standard benchmark computation in under five minutes that would take one of today's fastest supercomputers an unfathomable amount of time. Google's strategy revolves around improving qubit quality and error correction to achieve practical quantum advantage, with a clear focus on demonstrating real-world applications. 𝗠𝗮𝗷𝗼𝗿𝗮𝗻𝗮 𝟭 (𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁) Microsoft is taking a bold step with its "Majorana 1" chip, built upon a Topological Core architecture. This innovative design harnesses topoconductors to control Majorana particles, creating more stable and scalable qubits. Microsoft envisions this as the "transistor for the quantum age," paving the way for million-qubit systems capable of tackling industrial-scale challenges like breaking down microplastics or designing self-healing materials. Their strategy is to focus on creating inherently stable qubits that require less error correction, a significant hurdle in quantum computing. 𝗢𝗰𝗲𝗹𝗼𝘁 (𝗔𝗺𝗮𝘇𝗼𝗻 𝗪𝗲𝗯 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀) Amazon Web Services (AWS) is addressing quantum error correction directly with their "Ocelot" chip. Ocelot employs a novel architecture utilizing 'cat qubits' that are designed to reduce error correction costs significantly. This is a crucial advancement as quantum computers are incredibly sensitive to noise, and error correction is essential for reliable computation. AWS's strategy is to lower the barrier to entry for quantum computing through its Amazon Braket service, providing access to diverse quantum hardware and tools while focusing on making quantum computing more cost-effective and accessible. 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝗜: 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗟𝗶𝗺𝗶𝘁𝘀 𝗼𝗳 𝗚𝗣𝗨𝘀 While GPUs have revolutionized AI by accelerating the training of complex models, quantum computing offers the potential for an even greater leap in AI capabilities. Quantum computers, by harnessing superposition and entanglement, can potentially solve optimization, machine learning, and simulation problems that are intractable for even the most powerful GPUs. #QuantumComputing #AI #GPU

Boards face a stark choice: prepare for quantum computing now or risk being outpaced by competitors and adversaries alike. A revolution is brewing in the shadows of today’s AI frenzy, and leaders can’t afford to ignore it. Headlines celebrated OpenAI’s jaw-dropping $300B, five-year deal with Oracle, demanding 4.5 gigawatts of power...more than two Hoover Dams. Oracle shares surged 42%. Its chairman’s fortune jumped $100B overnight. But this wasn’t just a triumph of scale. It was a warning. AI’s projected $2.9T market by 2028 (Morgan Stanley) is straining classical infrastructure, ballooning energy demands, persistent compute shortages, and unsustainable economics. OpenAI alone is projected to lose $44B before profitability in 2029. Bigger data centers are no longer a strategy; they’re a liability. Enter quantum computing, the quieter, truly transformative force. Unlike classical bits (0 or 1), qubits leverage superposition and entanglement, unlocking problems once considered impossible: • Real-time global supply-chain optimization • Atomic-level drug discovery • AI training compressed from years to hours, with orders-of-magnitude less energy This isn’t hype. It’s happening now: China has made quantum a national imperative, investing billions. Origin Quantum’s Tianji 4.0 and SpinQ’s planned 100-qubit system underscore why China is a leader in quantum communication (Belfer Center). The U.S. still holds an edge in computing and sensing. Google’s 105-qubit Willow chip achieved a 10% error-rate reduction in 2025. Startups are accelerating: • Rigetti: 36-qubit multi-chip system at 99.5% fidelity • D-Wave: 42% revenue growth to $3.1M • IonQ: $20.7M Q2 revenue, guidance raised to $82–100M Microsoft, MIT, and Nvidia are actively weaving quantum into AI workflows. Europe is closing the gap through aggressive public-private programs. The upside is enormous: quantum-AI hybrids could revolutionize drug discovery, climate modeling, and critical-infrastructure security. The risks are just as real: broken encryption, amplified surveillance, and weaponized technologies in the wrong hands. It’s no accident the UN designated 2025 as the International Year of Quantum Science and Technology. Boards should take note. The Oracle–OpenAI deal proves we’re pushing classical systems to the brink. True leadership won’t come from trillion-dollar buildouts on strained power grids. It will come from bold U.S. investment in quantum R&D, education, and talent, paired with regulation that accelerates innovation rather than stifles it. Hesitation isn’t prudence. It’s surrender. Quantum won’t just speed up AI. It will redefine it—and the rules of tomorrow.

🚀 Quantum Computing: Transitioning from Lab Theory to Operational Reality 2025 marks a definitive shift as the UN celebrates the International Year of Quantum Science and Technology. We are moving past speculative demos toward productive and operational utility, integrating quantum into core workflows to solve "intractable" problems. 🏆 Top 10 Quantum Achievements of 2025: 1. Verifiable Quantum Advantage: Google’s Willow chip achieved a 13,000x speedup over the world’s fastest supercomputers using the "Quantum Echoes" algorithm to model real physical experiments. 2. Topological Stability: Microsoft unveiled Majorana 1, achieving a 1,000-fold reduction in error rates using hardware-protected topological qubits. 3. The "Four-Nines" Barrier: IonQ reached a world-record 99.99% two-qubit gate fidelity, dramatically reducing the physical qubits needed for fault-tolerant operations. 4. Operational Scaling: Caltech researchers assembled a 6,100-qubit neutral-atom array, maintaining superposition for 13 seconds without compromising quality. 5. Extended Coherence: Alice & Bob created "cat qubits" that resisted bit-flip errors for more than one hour, essential for long-running operational algorithms. 6. Quantum Internet Breakthrough: T-Labs demonstrated high-fidelity (99%) transmission of entangled photons across 30km of commercial fiber for 17 days. 7. GPS-Denied Navigation: Q-CTRL achieved the first commercial advantage in sensing, using quantum magnetometers for navigation 100x more accurate than conventional systems without GPS. 8. Continuous Operation: Harvard and QuEra ran a 3,000-qubit array for over two hours by replenishing atoms mid-computation. 9. Standard Hardware Integration: IBM successfully ran quantum error correction algorithms on commercially available AMD chips, accelerating practical scalability. 10. Modular Interconnects: Oxford University achieved quantum gate teleportation between separate modules, proving that distributed quantum computing is viable. 🛠️ How to Prepare for the Operational Transition: • Operational Resilience: The timeline for Cyber-Resilience has accelerated; research shows that 1 million physical qubits could break RSA-2048 encryption in just one week. Experts recommend deprecating vulnerable systems by 2030, making the migration to Post-Quantum Cryptography (PQC) a current operational priority. • Infrastructure Integration: Utilize hybrid cloud-quantum architectures via Amazon Braket or Azure Quantum to test readiness without heavy capital investment. • Logistics Optimization: Organizations like D-Wave are already delivering an 80% reduction in scheduling efforts for complex supply chains. Quantum is no longer a "future" tech; it is an operational differentiator for the next decade. #QuantumComputing #Innovation #SupplyChain #CyberSecurity #CloudComputing #FutureOfTech

Great conversation this afternoon with John Furrier at theCube + NYSE ahead of NVIDIA GTC on 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗟𝗲𝗮𝗽: 𝗧𝗵𝗲 𝗡𝗲𝘅𝘁 𝗙𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗼𝗳 𝗔𝗜 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴. Quantum computing is rapidly moving from theory to practical systems. It was fun to hold a 1,600-qubit neutral-atom quantum core and discuss quantum and GPU co-processing with Pranav Gokhale (Infleqtion). These hybrid architectures could unlock enormous compute power for AI Factories, accelerating inference across physics, biology, medicine, and material science. Adam Lewis (SandboxAQ) shared exciting perspectives on the breakthroughs already emerging in this space. The conversation quickly turned to the security implications. As quantum capabilities scale, they will fundamentally challenge today’s cryptographic foundations. Anuj Jaiswal (Fortanix) discussed the growing role of PQC-ready HSMs and confidential computing in preparing infrastructure for this transition. At DigiCert, we are working closely with customers to 𝗠𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗲 𝗣𝗞𝗜 and build crypto-agility, preparing for the massive shift to quantum-safe authentication and encryption across machines, software, devices, content, and now AI models and autonomous agents. Full interview here: https://lnkd.in/g8GhAjsB #QuantumComputing #AI #PostQuantumCryptography #IntelligentTrust #PKI #NVIDIAGTC