Industries Using Quantum-Classical Computing

Breaking Quantum News: Real algorithms, real data, real quantum machines HSBC, in partnership with IBM, has delivered the world’s first quantum-enabled algorithmic trading trial. Using live, production-scale data from the European corporate bond market, HSBC integrated IBM’s quantum processors with classical systems—achieving up to a 34% improvement in predicting the probability of winning trades compared with classical methods alone. Why it matters: - Bond trading is one of the most complex, data-heavy challenges in finance. - Classical models struggle to capture hidden pricing signals in noisy markets. - By augmenting workflows with IBM Quantum Heron, HSBC uncovered insights classical systems could not. As Philip Intallura Ph.D, HSBC’s Global Head of Quantum Technologies, put it: “This is a tangible example of how today’s quantum computers could solve a real-world business problem at scale and offer a competitive edge.” And as IBM’s Jay Gambetta emphasized: breakthroughs come from combining deep financial expertise with cutting-edge quantum algorithms—demonstrating what becomes possible as quantum advances. This is not hype. It’s not distant. Quantum is entering the market—today. #QuantumComputing #Finance #Innovation #PQC #QuantumReady

Most enterprises treat quantum computing as a nerdy R&D curiosity. A mistake. Critical business problems, which are fundamentally constrained by classical computing today, are likely to be solved by 2030. With a hybrid combination of high performance computing and quantum approaches. Three sectors stand out: Pharma, Life & Material Sciences: Drug discovery is essentially a molecular simulation challenge. Classical systems approximate. Quantum systems are designed around quantum mechanics itself. Thus, it is not just about faster research, but the ability to model molecular interactions with higher fidelity. For protein folding, compound optimization, personalized therapeutics. Reaching quantum advantage first in pharma won’t merely accelerate pipelines — it will redefine them. Financial Services: Banks, insurers, stock exchanges operate enormous optimization, transaction or probability engines. E.g., for risk simulations, or fraud detections. Many of these problems scale exponentially in complexity. Quantum algorithms are particularly promising where classical Monte Carlo simulations hit practical limits. And, quantum computing is becoming a cybersecurity challenge. Post-quantum cryptography migration will likely be one of the largest infrastructure transitions the financial sector has seen for decades. Complex Logistics & Supply Chains: Airlines, shipping companies, manufacturers, energy grids, and global retailers all face combinatorial optimization problems. These systems already operate at scales where small efficiency gains create major business impact. Enterprises operating in these segments should get „quantum-ready“ now: • Identify quantum-relevant business problems • Work with quantum partners who advocate an open approach • Build internal quantum literacy • Develop hybrid workflows • Prepare your security stack for the post-quantum era. Additionally we need quantum computing companies delivering at production scale. IQM Quantum Computers calls this Production Quantum. Which is the delivery of a production-ready full stack solution rather than just a scientific solution for a specific problem. This is the same pattern we saw with #AI. The competitive gap formed before the technology fully matured. #Quantum readiness is becoming a strategic capability and critical timing question. For an increasing number of enterprises. Not only for R&D departments.

China’s Photonic Quantum Chip Delivers a 1,000-Fold Speed Boost for AI and Supercomputing Introduction China has unveiled a photonic quantum chip that delivers more than a thousandfold acceleration in complex computation, marking a major leap in AI data center performance and quantum-classical hybrid computing. Honored with the Leading Technology Award at the 2025 World Internet Conference, the technology positions China at the forefront of quantum-enabled high-performance computing. Breakthrough Capabilities • The chip, developed by CHIPX and Shanghai-based Turing Quantum, integrates over 1,000 optical components onto a 6-inch wafer using monolithic photonic integration. • It combines photon–electronics co-packaging, wafer-level fabrication, and system integration—an achievement its creators call a world first. • Already deployed in aerospace, biomedicine, and finance, it delivers processing speeds beyond the limits of classical silicon. • Photonic computing reduces power consumption, increases bandwidth, and accelerates AI model training and cloud-scale computation. • The architecture is scalable toward future quantum systems, with a design pathway that could support up to 1 million qubits. Industrialization and Global Competition • CHIPX has built a full closed-loop pilot production line for thin-film lithium niobate photonic wafers, capable of producing 12,000 wafers annually. • Each wafer yields roughly 350 chips—bringing industrial-grade optical quantum computing into real-world deployment for the first time. • Rapid prototyping has improved tenfold, cutting development cycles from six months to two weeks. • China’s progress signals a strategic push into a field historically led by Europe and the U.S., where companies such as SMART Photonics and PsiQuantum are expanding their own photonic manufacturing lines. Implications for AI, Quantum, and National Power • Photonic chips deliver the speed, efficiency, and low latency needed for next-generation AI training, 5G and 6G networks, and secure quantum communication. • Their scalability enables hybrid quantum-classical systems capable of tackling problems in chemistry, finance, and national defense simulation. • With quantum threats rising globally, photonic architectures offer a pathway to resilient, high-throughput compute infrastructure that traditional chips cannot match. Conclusion China’s new photonic quantum chip marks a decisive step toward industrial-scale quantum acceleration. By pairing optical physics with mature semiconductor manufacturing, China has positioned itself to compete aggressively in the race for AI dominance, quantum-secure communication, and next-generation supercomputing infrastructure. I share daily insights with 33,000+ followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Microsoft ’s Majorana 2 program provides one of the strongest current examples of “Quantum–AI Convergence”. Through its Discovery Agentic AI platform, Microsoft used AI not simply as an application layer, but as an #engineering co-designer to accelerate materials discovery, optimize quantum device #architectures, improve fabrication processes, and enhance validation workflows. However, this is only one example of the broader Quantum-Convergence paradigm, where #quantum computing increasingly intersects with #AI, #HPC, advanced #semiconductors, #photonics, #robotics. The convergence is transforming AI from a user of computational infrastructure into an active participant in designing, calibrating, orchestrating, and securing next-generation quantum systems. NVIDIA – Building the orchestration layer for hybrid quantum-classical computing through accelerated computing, quantum simulation, quantum error-correction support, and AI-driven workload management. IBM – Integrating quantum hardware, AI, cloud computing, and enterprise software into unified computational ecosystems with a strong focus on fault-tolerant quantum computing. Google – Applying machine learning to quantum hardware calibration, error mitigation, quantum algorithm development, and advanced scientific computing. Quantinuum – Combining trapped-ion quantum computing, AI-enhanced control systems, cybersecurity, chemistry, and optimization applications. SandboxAQ – Developing Large Quantitative Models (LQMs) that integrate AI with quantum science, chemistry, materials discovery, and post-quantum cybersecurity. Xanadu – Advancing photonic quantum computing, quantum machine learning, differentiable quantum circuits, and AI-enabled photonic optimization. IonQ – Strong focus on AI optimization, quantum networking, distributed quantum computing, and hybrid quantum-classical architectures designed for future scalable quantum ecosystems. D-Wave Quantum – focused on quantum optimization systems, frequently integrated with AI workflows for logistics, manufacturing, scheduling, and industrial optimization. Rigetti Computing – Developing superconducting quantum processors tightly coupled with classical computing resources to support AI, optimization, and scientific simulation workloads. PsiQuantum – Pursuing large-scale fault-tolerant quantum computing through photonic architectures, leveraging silicon-photonics manufacturing techniques that may enable industrial-scale quantum systems. Collectively, these organizations are advancing the foundations for a novel computational ecosystem.

I get at least two inbound pings every month from life-sciences companies asking some version of the same question: “So what can #quantumcomputing actually do for drug discovery and pharmaceuticals?” These used to be cocktail-napkin conversations. Now they sound more like budget discussions. What’s changed isn’t the curiosity—it’s the intent. The tone has shifted from fiction to spreadsheet. These are no longer blue-sky debates about some distant quantum future. The focus is on near-term, hybrid quantum-classical use cases that can deliver measurable advantage well before fault-tolerant quantum machines arrive. Over lunch during JPM with our resident quantum whisperer and Global Quantum leader for healthcare and life sciences at IBM Research , Gopal Karemore, PhD, I got the clearest framing yet: quantum computing today is roughly where AI was a decade ago—promising, awkward, and not quite ready to replace classical HPC. Pharma leaders don’t see it as a substitute for today’s supercomputers; they see it as a future-defining capability for molecular science. Right now, the real value is in learning, experimentation, and building hybrid workflows that blend quantum with classical simulation—especially for molecular problems where classical methods rely on approximations like DFT and force fields that eventually hit physical limits. Gopal and team came back from JPM with a consistent message from pharma and biotech: #quantum is viewed as a long-term R&D accelerator, not a near-term cost-cutting tool. The smartest organizations are running targeted pilots, building internal quantum literacy, and partnering with technology providers to become “quantum-ready.” The goal isn’t speed for its own sake—it’s better science: improved decision-making, reduced uncertainty, and new chemical and biological insight when quantum complements AI and classical simulation. So far, the use cases cluster into three main buckets: -->Molecular and electronic structure simulation – improving the accuracy of binding energies and reaction mechanisms. -->Drug discovery optimization – particularly hit-to-lead and lead optimization. -->Protein structure and dynamics – longer term, to better understand folding and functional conformations. The metrics are refreshingly practical: prediction accuracy, fewer experimental cycles, and faster (and cheaper) advancement of candidates. The hoped-for outcome is equally old-fashioned: higher-quality leads, lower attrition, and better R&D productivity. In other words, #quantum isn’t here to fire your chemists. It’s here to give them fewer wrong answers—eventually. Thank you Gopal and was lovely catching up with you and Shervin Ayati. #quantumcomputing #quantum #drugdiscovery

Six months ago, the IDC Worldwide Quantum Computing Forecast made a specific bet: the next phase of quantum computing wouldn't be driven by better hardware alone. It would come from combining increasingly capable quantum systems with AI, HPC, and domain expertise to solve problems beyond the practical reach of classical computing. Last week offered a compelling example of exactly that. IBM, Oak Ridge National Laboratory, and Cleveland Clinic used a hybrid quantum-classical workflow to model the chemistry of molten FLiBe salt, a leading candidate material for future fusion reactors. Rather than replacing classical computing, IBM's increasingly capable quantum hardware was applied to the portion of the problem where it provides the greatest computational advantage, while classical systems handled the remaining calculations. That's exactly the heterogeneous computing model we expect to define enterprise quantum adoption. What's equally important is where this work happened. The research is part of the U.S. Department of Energy's Genesis Mission, bringing together quantum computing, HPC, AI, and domain expertise across the national laboratory ecosystem. Read alongside recent initiatives like QuantumEAGLe, it reinforces a broader trend: government investment is evolving beyond advancing quantum hardware. It's increasingly focused on building the collaborative ecosystem needed to translate scientific breakthroughs into real-world applications. This is also why simulation continues to stand out in our enterprise research. Alongside optimization and quantum AI, simulation remains one of the leading quantum use cases organizations are exploring. Fusion materials research represents one of the most demanding examples imaginable, but the underlying challenge extends well beyond energy. Industries including pharmaceuticals, chemicals, advanced manufacturing, and materials science all face computational problems where heterogeneous computing could eventually deliver meaningful advantages. The remaining challenge isn't demonstrating that quantum can contribute to scientific discovery. It's making these capabilities accessible outside national laboratories. Today's breakthrough required quantum scientists, computational chemists, HPC researchers, and highly specialized workflows. The next phase of the market will depend on advances in both quantum hardware and the surrounding software ecosystem, development platforms, and workflow orchestration that allow domain experts to leverage quantum computing without becoming quantum specialists. I explore what this means for enterprise quantum adoption, heterogeneous computing, and the evolution of the quantum software ecosystem in our latest IDC Link: https://lnkd.in/gFQV95FF Ashish Nadkarni Jeff Janukowicz Jerry M. Chow Jay Gambetta Mike Houston Steven Malkiewicz #Quantum #QuantumComputing #HPC #AI #FusionEnergy #DOE #IDC

🚨 Quantum Computing Breakthrough in Finance 🚨 HSBC just announced a world-first. By using IBM’s Heron quantum processor, the bank achieved a 34% improvement in predicting bond trading probabilities. This marks the first time a bank has applied quantum computing to real financial trading data at scale, moving beyond theory and into production-level application. Some are calling this a “Sputnik moment” for quantum. That is not a perfect analogy, given the geopolitical nature of Sputnik and the corporate implications of HSBC's use of quantum computing. But I am not surprised to see a big leap forward for quantum in the world of finance. In fact, when I wrote Quantum: Computing Nouveau back in 2018, I predicted this exact trajectory: that quantum would move from academic labs to financial markets and other industries where optimization, forecasting, and massive data challenges are prevalent. In my 2018 book, I outlined - Why finance would be among the earliest adopters of quantum, thanks to its reliance on complex risk management, forecasting, and trading models. - How quantum computing could deliver step-change improvements in processing power, solving problems classical computing struggles and corporate NP problems. In computer science, NP (nondeterministic polynomial-time) problems are problems where it’s easy to verify a solution once you have it, but extremely hard to calculate the solution in the first place. - The looming arms race for quantum advantage, not only among tech companies, but also in financial services, energy, logistics, and governments. HSBC’s milestone confirms that we’re crossing the threshold from theory to practice. Quantum computing isn’t just “new math”—it’s new computing, with profound implications for markets, cybersecurity, and global competition. 🔮 Back in 2018, I wrote that quantum computing is not just optional. It is a conditio sine qua non for the future of finance and data-driven industries. Today, we’re watching that future unfold. #Quantum #QuantumComputing #Future #Finance https://lnkd.in/gMNc2M9b

Some of the industries investing the most in quantum computing will wait the longest to see returns. I spent months mapping every major fault-tolerant algorithm resource estimate to the real-world problem it solves, and kept updating through today's Q-CTRL practical quantum advantage announcement. The research upends lots of the assumptions I see in most quantum strategy decks. Pharma, chemicals, batteries, and advanced materials have the strongest near-term case. Battery degradation simulation needs fewer than 500 logical qubits. Photosensitizer drug design needs 180–350. Hardware for these is almost here. This week alone, Cleveland Clinic, RIKEN, and IBM simulated a 12,635-atom protein using quantum-centric supercomputing, and Q-CTRL demonstrated a 3,000x wall-clock speedup over classical methods on a condensed-matter simulation. Pre-fault-tolerant hardware is already producing results in these domains. Finance? The first useful derivative pricing requires ~4,700 logical qubits, a 45 MHz logical clock rate, and hardware roughly 1,000x faster than anything projected before 2035. Quadratic speedups struggle against classical alternatives that improve every year. Logistics and ML face similar structural barriers. That doesn't mean these sectors should disengage. Algorithmic breakthroughs are unpredictable, quantum talent takes years to develop, and being caught flat-footed is worse than investing early. But invest with clear eyes about where the evidence actually stands. And for CISOs: there's a common assumption in cybersecurity that quantum computers will visibly transform other industries before they threaten encryption, giving security teams a natural early warning. The research shows the opposite. The cryptographic threshold sits below most grand-challenge applications on the capability ladder, not above. Breaking ECC-256 needs ~1,200 logical qubits. Breaking RSA-2048 needs ~1,400. Drug metabolism simulation needs ~4,900. By the time quantum delivers its full industrial potential, it will already have been capable of breaking your encryption for years. If you've been waiting for visible scientific milestones as your signal to start PQC migration, reconsider. I know this won't make me popular with everyone in the quantum space. But I've watched overhype trigger funding winters before, and that hurts the applications where quantum genuinely works. Honesty serves the field better than hype. Seven-article Deep Dive series, updated through May 6, 2026: The Quantum Utility Map. https://lnkd.in/dxd_FRcS #Quantum #QuantumComputing #QuantumAdvantage

IQM Quantum Computers and Deutsche Bahn demonstrated a hybrid quantum-classical approach that used real railway scheduling data to solve a large-scale optimization problem on current quantum hardware, providing a framework that could be applied across industries. The project used a real Deutsche Bahn dataset covering 190 trips across five German cities and about 98,500 possible scheduling cycles, with QAOA solving smaller optimization subproblems within a larger classical workflow. The researchers found the approach produced feasible solutions on today’s hardware, improved as larger quantum subproblems could be processed, and completed the full optimization pipeline on an IQM quantum computer. https://lnkd.in/esiDtTX6