Quantum Computing Achievements and Applications

Thought you knew which #quantumcomputers were best for #quantum optimization? The latest results from Q-CTRL have reset expectations for what is possible on today's gate-model machines. Q-CTRL today announced newly published results that demonstrate a boost of more than 4X in the size of an optimization problem that can be accurately solved, and show for the first time that a utility-scale IBM quantum computer can outperform competitive annealer and trapped ion technologies. Full, correct solutions at 120+ qubit scale for classically nontrivial optimizations! Quantum optimization is one of the most promising quantum computing applications with the potential to deliver major enhancements to critical problems in transport, logistics, machine learning, and financial fraud detection. McKinsey suggests that quantum applications in logistics alone are worth over $200-500B/y by 2035 – if the quantum sector can successfully solve them. Previous third-party benchmark quantum optimization experiments have indicated that, despite their promise, gate-based quantum computers have struggled to live up to their potential because of hardware errors. In previous tests of optimization algorithms, the outputs of the gate-based quantum computers were little different than random outputs or provided modest benefits under limited circumstances. As a result, an alternative architecture known as a quantum annealer was believed – and shown in experiments – to be the preferred choice for exploring industrially relevant optimization problems. Today’s quantum computers were thought to be far away from being able to solve quantum optimization problems that matter to industry. Q-CTRL’s recent results upend this broadly accepted industry narrative by addressing the error challenge. Our methods combine innovations in the problem’s hardware execution with the company’s performance-management infrastructure software run on IBM’s utility-scale quantum computers. This combination delivered improved performance previously limited by errors with no changes to the hardware. Direct tests showed that using Q-CTRL’s novel technology, a quantum optimization problem run on a 127-qubit IBM quantum computer was up to 1,500 times more likely than an annealer to return the correct result, and over 9 times more likely to achieve the correct result than previously published work using trapped ions These results enable quantum optimization algorithms to more consistently find the correct solution to a range of challenging optimization problems at larger scales than ever before. Check out the technical manuscript! https://lnkd.in/gRYAFsRt

🚨 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

Many of you will have seen the news about HSBC’s world-first application of quantum computing in algorithmic bond trading. Today, I’d like to highlight the technical paper that explains the research behind this milestone. In collaboration with IBM, our teams investigated how quantum feature maps can enhance statistical learning methods for predicting the likelihood that a trade is filled at a quoted price in the European corporate bond market. Using production-scale, real trading data, we ran quantum circuits on IBM quantum computers to generate transformed data representations. These were then used as inputs to established models including logistic regression, gradient boosting, random forest, and neural networks. The results: • Up to 34% improvement in predictive performance over classical baselines. • Demonstrated on real, production-scale trading data, not synthetic datasets. • Evidence that quantum-enhanced feature representations can capture complex market patterns beyond those typically learned by classical-only methods. This marks the first known application of quantum-enhanced statistical learning in algorithmic trading. For full technical details please see our published paper: 📄 Technical paper: https://lnkd.in/eKBqs3Y7 📰 Press release: https://lnkd.in/euMRbbJG Congratulations to Philip Intallura Ph.D , Joshua Freeland Freeland and all HSBC colleagues involved — and huge thanks to IBM for their partnership.

Oxford Scientists Achieve Quantum Teleportation, Advancing Scalable Quantum Computing Researchers at Oxford University Physics have achieved a breakthrough in quantum computing, successfully demonstrating the first-ever teleportation of logical quantum gates between two separate quantum computers. This marks a significant step toward scalable quantum supercomputers, capable of solving complex problems far beyond the reach of today’s classical machines. Key Achievement: Teleportation of Logical Quantum Gates • The Oxford team connected two separate quantum processors over a photonic network, forming a fully integrated quantum system. • This technique enables distributed quantum computing, where separate quantum systems can function as a single, larger computer. • Quantum teleportation was used to transfer quantum operations, a critical milestone in making scalable and modular quantum computing possible. Why Scalability is a Major Hurdle • Quantum computers rely on qubits that leverage superposition to perform computations exponentially faster than classical computers. • However, qubits are highly fragile and must be maintained at extremely low temperatures, making large-scale quantum computers difficult to build. • The teleportation breakthrough offers a way to scale quantum computing without needing massive single-chip processors, instead using networked quantum systems. Implications for the Future • Scalable Quantum Supercomputers: This method allows smaller quantum processors to be linked, potentially overcoming hardware limitations. • Solving Global Challenges: Quantum computing could revolutionize medical research, climate modeling, cryptography, and complex optimization problems. • Toward a Quantum Internet: Teleportation-based computing brings us closer to secure quantum communication networks, which could reshape cybersecurity and global data exchange. Oxford’s success in quantum gate teleportation is a landmark achievement, demonstrating that modular, scalable quantum computing is within reach. This brings the world one step closer to practical quantum supercomputers, unlocking new possibilities for scientific and technological breakthroughs.

I’m excited to share this new work from our IBM Quantum team in collaboration with Oak Ridge National Laboratory. This is a major demonstration of what we mean by realizing useful Quantum-centric supercomputing. Building on the chemistry work developed with RIKEN (https://lnkd.in/eK8jW-Wp) last year, and the previous Krylov demonstration with University of Tokyo (https://lnkd.in/eae_8zGc), the IBM Quantum and ORNL teams developed a quantum algorithm for ground states with convergence guarantees similar to phase estimation, while retaining the error mitigation aspect of sample-based methods. Putting together sample-based approaches and Krylov methods, we call this sample-based Krylov quantum diagonalization (SKQD). The algorithm can be used to compute ground state energies of quantum systems for many lattice models relevant in materials science and high-energy physics. SKQD is demonstrated experimentally on 85 qubits and 6,000 two-qubit gates on IBM quantum processors, against the ground state of the Anderson impurity model, obtaining high accuracies for problem sizes beyond the reach of exact diagonalization. This marks one of the largest implementations of quantum diagonalization to date, and points at how quantum computing, combined with classical computation in quantum-centric supercomputing environments, will enable us to push beyond classical methods for interesting applications. These new results also show again how algorithmic discovery is essential, especially for quantum-centric supercomputing architectures. Classical algorithms for materials science have made an impressive progress in the last decades. However, by thinking of quantum-classical workflows where quantum can deliver a value that cannot be matched by classical, we will move closer to demonstrating quantum advantage. Congratulations again to the team on this achievement. Check out the paper here: https://lnkd.in/epwCrG5R.

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.

Quantum Computing (QC) and Its Impact on Systems and Society: This information pertains to our work areas; numerous other applications exist beyond our current focus. All data is sourced from public, non-classified information, acknowledging that more advanced developments may be occurring behind the scenes: > China’s Zuchongzhi-3 (Fall 2024): A 105-qubit superconducting quantum processor developed by the University of Science and Technology of China (USTC). It achieved a significant quantum computational advantage, performing complex computations that challenged classical supercomputers. > Google’s Willow (Fall 2024): Google’s 105-qubit superconducting processor, Willow, demonstrated exponential error reduction with increased qubits, achieving below-threshold quantum error correction. It completed a benchmark computation in under five minutes—a task that would take classical supercomputers an unfathomable amount of time. > Microsoft’s Majorana 1 (February 2025): Utilizing topological qubits, Microsoft’s Majorana 1 operates with eight qubits, aiming for greater stability and scalability to millions of qubits with fewer errors. This approach addresses challenges related to qubit coherence and error rates. > QC Impact Areas for our team include: 1. EMP and Infrastructure Security: QC can model electromagnetic pulse impacts on power grids and critical infrastructure, aiding in designing systems that maintain stability under such conditions and enabling faster, more accurate recovery simulations. 2. Complex Dynamical Systems Stability: QC may predict failures in complex systems — such as large power grids, defense networks, and flight systems — and develop strategies to prevent them, enhancing overall system reliability. 3. Critical Systems Risk Assessment: By processing large, uncertain datasets, QC can assess risks in infrastructure, aerospace, defense, and energy systems, providing clearer insights into potential failures. 4. Secure Networks Optimization: QC could optimize the design and defense of secure networks, ensuring safer and more efficient data transmission across critical systems. 5. Material Science and Design: QC has the potential to accelerate the design of stronger, lighter, and more resilient materials by simulating atomic interactions and predicting material behavior under stress. 6. Scenario Planning: QC can rapidly model numerous global risk scenarios, enhancing decision-making in complex and uncertain conditions. Timeline: Some targeted applications of QC may emerge within the next 5 to 10 years, with broader integration anticipated over the next 10 to 20 years. Current efforts are focused on preparing systems to incorporate QC capabilities as the technology becomes more viable. Related post: AI and Quantum Leap (https://lnkd.in/gpsxhthd). #computing #infrastructure #materials #networks #quantum #resilience #risk #security #stability #technology

  Even AI-driven approaches are limited by the performance of classical computers. Molecular simulations pose a challenge for classical computers, as the interactions between particles scale exponentially. Quantum computing could be a turning point here, especially in materials science. By modeling the behavior of electrons and atoms with extreme precision, quantum mechanical systems could help researchers design new materials with specific properties. Quantum technologies could, for example, provide deeper insights into the unique genetic makeup of a patient and show how they might respond to certain treatments, helping us understand how drug compounds interact with biological systems. This highlights the remarkable potential in drug discovery, potentially leading to the development of more personalized medications and the discovery of treatments that are currently beyond classical computational methods. We at Merck Group also aim to harness the tremendous opportunities that this technological advancement theoretically offers in practice. Therefore, we are conducting #research in this field alongside startups and institutions to innovate and enable quantum chemical applications for faster and more cost-effective drug discovery. Although #quantumcomputing has the potential to transform the pharmaceutical industry, many #innovations and applications are still in development. To fully realize the potential of quantum, advancements in error correction as well as in quantum software and hardware are necessary. via Forbes https://lnkd.in/dRWqkbWr

⚛️ Google’s quantum leap tl;dr: Google's quantum computer, Sycamore, can now outperform classical supercomputers in certain tasks, even with noise. This is a major milestone in the race for quantum advantage, and we might (finally) be getting closer to practical applications of quantum computing. * * * 🧮 The Big Picture: For decades, scientists have envisioned quantum computers as the future of computation, capable of solving problems far beyond the reach of even the most powerful classical computers. We're talking simulating complex molecules for drug discovery, optimizing financial models, or breaking today's encryption standards. But proving "quantum supremacy" - the point where a quantum computer demonstrably outperforms a classical one - has been a challenge. Google researchers found a specific threshold of noise (errors in quantum calculations) where their Sycamore processor, running a task called random circuit sampling, becomes unbeatable by classical means. To give an order of scale: a classical supercomputer would need ten trillion years to match what Sycamore does in seconds. 👾 What it means for You: While it won’t change our lives in the immediate future, this milestone is HUGE. It proves quantum computers aren't just theoretical curiosities, they're on a trajectory to tackle real-world problems with unprecedented speed and efficiency. This impacts fields from medicine to materials science, finance, and beyond. 🚀 My take on it: This "noise threshold" discovery is key. It shows that even with the inherent instability of quantum systems, we can achieve computational power that makes today's supercomputers look like abacuses. The real excitement lies not in replacing classical computers, but in unlocking the potential of quantum computing for problems we haven't even dreamt of solving yet. And if applicable to AI, quantum computers may well be the path towards Artificial General Intelligence. #QuantumComputing #Google #QuamtumAI #FutureTech Image credit: Google