Quantum Computing as a General-Purpose Technology

IBM Successfully Links Two Quantum Chips to Operate as a Single Device Key Insights: • IBM has achieved a significant milestone by linking two quantum chips to function as a single, cohesive system, enabling them to perform calculations beyond the capability of either chip independently. • This accomplishment supports IBM’s modular approach to building scalable quantum computers, a strategy aimed at overcoming the limitations of single-chip architectures. • The linked chips demonstrated successful cooperation, marking a step closer to larger and more powerful quantum systems capable of addressing complex real-world problems. The Modular Quantum Computing Approach: • IBM employs superconducting quantum chips, manufactured using processes similar to traditional semiconductor technology, allowing scalability and integration with existing hardware infrastructure. • Modular quantum systems involve linking smaller quantum processors, rather than relying on a single massive chip, reducing fabrication challenges and improving scalability. • This architecture allows multiple chips to share quantum information seamlessly, paving the way for constructing larger quantum systems without exponentially increasing hardware complexity. Addressing Key Challenges in Quantum Computing: • Scalability: Connecting multiple chips is a critical step toward scaling quantum computers to thousands or even millions of qubits. • Error Reduction: Larger quantum systems increase susceptibility to errors. Modular architectures provide pathways for better error management and correction across linked processors. • Coherence Across Chips: Maintaining the delicate quantum states across separate chips is technically challenging, and IBM’s success suggests progress in solving this issue. Implications of IBM’s Achievement: • Enhanced Computational Power: Linked quantum chips unlock the potential for more complex simulations and problem-solving capabilities. • Practical Quantum Applications: Industries like pharmaceuticals, cryptography, and materials science may soon benefit from more robust and scalable quantum computing solutions. • Competitive Advantage: IBM’s progress underscores its leadership in modular quantum computing, positioning it strongly in the competitive quantum technology landscape. Future Outlook: IBM’s successful demonstration of inter-chip quantum communication validates the modular quantum computing strategy as a viable path to scaling up systems. Future advancements will likely focus on enhancing chip-to-chip communication fidelity, increasing the number of interconnected chips, and reducing overall error rates. This breakthrough brings us one step closer to practical, large-scale quantum computing systems capable of solving problems previously deemed unsolvable by classical computers.

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

Quantum readiness is less about sudden disruption and more about cultivating skills, forging collaborations, and aligning strategies with evolving standards, so that businesses can gradually integrate these technologies into their long-term transformation paths. We should see quantum computing as a journey that requires methodical preparation. Finance, logistics, chemistry, and cybersecurity are already experimenting with hybrid models that combine classical and quantum systems. These early steps show that the transition will not happen overnight, but through structured phases of learning and integration. The priority for leaders is to identify processes where quantum can create measurable improvements. This means feasibility studies, pilots, and a roadmap that integrates quantum into IT environments in a sustainable way. At the same time, teams need training in principles, tools, and algorithms, because without this foundation, the technology remains an abstract concept. Collaboration is another essential layer. Partnerships with research hubs, vendors, and cloud providers open access to quantum resources that would otherwise remain out of reach. Alongside this, governance and security must advance with post-quantum standards, ensuring compliance and ethics are never secondary. The real challenge is continuous adaptation. Regulations and technologies will evolve, and strategies must remain flexible. This long-term perspective will define the organizations that are prepared to grow with the next wave of innovation. #QuantumComputing #DigitalTransformation #FutureOfWork

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.

Over the years, quantum computing has been judged mostly by its limitations — especially the gap between what today’s hardware can achieve and what classical algorithms can simulate. But the truth is more subtle and more exciting: the classical tools we rely on to simulate accurately quantum systems, like chemical compounds and materials, also have deep, well-known limitations. At Algorithmiq, we have been exploring how to turn this tension into something useful: a way to design and control information flow in artificial quantum materials, and to map out where classical methods begin to break while quantum methods provide reliable information. Why does this matter beyond physics? Because these simulations lies at the heart of the key industries driving the next decade: - catalytic processes for decarbonisation, - solid-state battery interfaces, - complex energy materials, - high-coherence quantum devices, - and next-generation computational chemistry. The challenge is that classical simulation becomes unreliable in precisely the regimes where these systems become most interesting — where disorder, interference, and entanglement govern their behaviour. We show that by pushing both quantum processors and classical algorithms into these hard regimes, we are beginning to see how quantum hardware can reveal properties impossible to discover with classical methods. Our initial evidence of quantum advantage for a useful use case is not just a scientific milestone — it is the early evidence of a technology crossing into real-world relevance. And challenges matter. They inspire people, create accountability, and accelerate progress. This is why I believe the Quantum Advantage Tracker, launched yesterday together with IBM Quantum, represents a turning point. It introduces the transparency, verification, and community benchmarking that every emerging technology needs to mature — and that investors rightly expect before deploying large-scale capital. We have published a detailed technical blog post explaining why information-flow modeling in artificial materials may become one of quantum computing’s most powerful use cases. 🔗 Link in the comments #QuantumComputing #QuantumAdvantage #InvestingInScience #DeepTech #MaterialsInnovation #Benchmarking #QDC2025 #QuantumMaterials #OpenScience

A quantum computer recently solved a problem in just four minutes that would take even the most advanced classical supercomputer billions of years to complete. This breakthrough was achieved using a 76-qubit photon-based quantum computer prototype called Jiuzhang. Unlike traditional computers, which rely on electrical circuits, this quantum computer uses an intricate system of lasers, mirrors, prisms, and photon detectors to process information. It performs calculations using a technique known as Gaussian boson sampling, which detects and counts photons. With the ability to count 76 photons, this system far surpasses the five-photon limit of conventional supercomputers. Beyond being a scientific milestone, this technique has real-world potential. It could help solve highly complex problems in quantum chemistry, advanced mathematics, and even contribute to developing a large-scale quantum internet. For example, quantum computers could help scientists design new medicines by simulating how molecules interact at the quantum level—something that classical computers struggle to do efficiently. This could lead to faster discoveries of life-saving drugs and treatments. While both quantum and classical computers are used to solve problems, they function very differently. Quantum computers take advantage of the unique properties of quantum mechanics—such as superposition and entanglement—to perform calculations at incredible speeds. This makes them especially powerful for solving problems that would be nearly impossible for traditional computers, bringing exciting new possibilities for scientific and technological advancements. As the Gaelic saying goes, “Tús maith leath na hoibre”—“A good start is half the work.” Quantum computing is still in its early stages, but its potential to reshape science, medicine, and technology is already clear.

A quiet but profound milestone in quantum computing: researchers have demonstrated silicon spin qubits with fidelity exceeding 99.9%, fabricated using standard semiconductor processes. That’s not just a technical achievement, it’s a signal that quantum chips may soon be manufacturable at scale, using the same industrial infrastructure that powers classical computing. The implications for cost, reliability, and integration are enormous, especially as quantum systems inch closer to practical deployment. What’s especially interesting is how this breakthrough aligns with DARPA’s Quantum Benchmarking Initiative, which defines “utility scale” as the point where quantum processors deliver more commercial value than they cost to operate. Crossing that threshold would mark the beginning of a new era, not just for physics labs, but for industry, logistics, finance, and beyond. If you’re tracking the convergence of quantum theory and manufacturing reality, this might get you thinking. #QuantumComputing #Semiconductors #SpinQubits #TechInnovation #DARPA #UtilityScale #DeepTech

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

Imagine a technology that could radically transform how we compute, solve complex problems, and address global challenges. This is the promise of quantum computing. A striking example of its potential is transforming the fertilizer production industry, which significantly impacts global electricity consumption and greenhouse gas emissions, accounting for about 1% of the world's electricity use. Quantum computing, based on quantum mechanics principles, introduces systems capable of existing in multiple states simultaneously, dramatically speeding up complex computations. This revolutionary technology can redefine AI, cybersecurity, and research and development while tackling critical global issues like climate change. The emergence of quantum computing necessitates new programming languages, development tools, and data processing techniques. Quantum computing is crucial in designing energy storages for renewable energy systems supporting initiatives like the International Solar Alliance. By improving the efficiency of these systems, quantum computing aligns with global clean energy goals, aiding in the transition to sustainable energy sources. The impact of quantum computing on AI is profound. It promises new, interdisciplinary innovations, redefining problem-solving and technological development. Its ability to simulate complex systems, from molecular structures to environmental systems, is fascinating, enabling AI to predict the behaviour of molecules to the dynamics of ecosystems. In security, quantum computing presents both challenges and opportunities. It could render current cryptography systems obsolete, prompting concerns in digital security. Simultaneously, it's spurring the development of quantum-resistant algorithms, a key focus for entities prioritizing security, including national governments. In R&D, particularly in simulating complex physical and chemical processes quantum can be a game changer. This can significantly reduce the time and costs associated with innovation, leading to rapid advancements in pharmaceuticals, materials engineering, and environmental science. We must prioritize education and training in quantum computing principles and applications as we navigate this quantum leap. This is essential to ensure equitable access to quantum technology and avoid deepening global inequalities or Quantum colonization. As governments worldwide recognize the transformative potential of quantum technologies, they are formulating policies to guide their ethical development and use. These initiatives, aiming to foster research, promote industry collaboration, and build necessary quantum infrastructure, ensure that quantum advancements are secure, responsible, and beneficial for society. #BigIdeas2024 Note: I generated the Image using DALL-E

The Willow Chip Google’s newly unveiled quantum computing chip, Willow, represents a significant advancement in the field. With 105 qubits, Willow has demonstrated the capability to perform computations in under five minutes that would take classical supercomputers an impractical amount of time—estimates suggest up to 10 septillion years (A septillion equals a number with 1 followed by 24 zeros. 1,000,000,000,000,000,000,000,000) The Willow chip is one of the most significant achievements in the field of quantum computing, and it is expected to bring about a massive revolution in human life in ways that were previously unimaginable. Here’s how this chip could transform life across various domains: 1. Artificial Intelligence (AI): • Rapid Development of Intelligent Systems: Thanks to its immense data processing capabilities, quantum computing can accelerate the development of AI algorithms, making them more accurate and efficient. • Making Complex Decisions: Quantum-powered AI systems can analyze massive amounts of data in a very short time, opening doors to innovative solutions in medicine, cybersecurity, and urban planning. 2. Drug Discovery and Disease Treatment: • Unprecedented Drug Design Precision: With the ability to simulate molecules with incredible accuracy, the chip can accelerate the discovery and development of new drugs, reducing the cost and duration of clinical trials. • Treating Incurable Diseases: This technology can provide a better understanding of complex biological processes, leading to groundbreaking treatments for diseases like cancer and Alzheimer’s. 3. Energy and Environmental Sustainability: • Improving Battery Efficiency: Quantum computing can help design more efficient and powerful batteries, enhancing the adoption of electric vehicles and renewable energy storage technologies. • Clean Energy Solutions: The chip could accelerate the development of clean nuclear fusion reactions, opening new horizons for sustainable energy. 4. Economy and Industry: • Optimizing Supply Chains: With quantum computing power, global supply chains can be managed more efficiently, reducing costs and increasing productivity. • Developing Advanced Materials: The chip can help design new materials with unique properties, revolutionizing industries like aerospace, construction, and technology. 5. Space Exploration: • Enhancing Understanding of the Universe: Quantum computing can improve simulations in astrophysics, deepening our understanding of the universe and enabling the exploration of other planets. • Innovating New Technologies: Designing more efficient space propulsion systems to help humans reach distant planets. 6. Cybersecurity: • Unbreakable Encryption: Quantum computing will revolutionize encryption by creating electronic security systems that are virtually unbreakable. • Threat Detection: Quantum systems can analyze patterns of cyber threats at lightning speed, safeguarding digital infrastructure from attacks.