Uses of Quantum Learning Algorithms in Industry

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.

Is Quantum Machine Learning (QML) Closer Than We Think? Select areas within quantum computing are beginning to shift from long-term aspiration to practical impact. One of the most promising developments is Quantum Machine Learning, where early pilots are uncovering advantages that classical systems are unable to match. 🔷 The Quantum Advantage: Quantum computers operate on qubits, which can represent multiple states simultaneously. This enables them to process complex, interdependent variables at a scale and speed that classical machines cannot. While current hardware still faces limitations, consistent progress in simulation and optimization is confirming the technology’s potential. 🔷 Why QML Matters: QML combines quantum circuits with classical models to unlock performance improvements in targeted, data-intensive domains. Early-stage experimentation is already showing promise: • Accelerated training for complex models • More effective handling of high-dimensional and sparse datasets • Greater accuracy with smaller sample sizes 🔷 The Timeline Is Shortening: Quantum systems are inherently probabilistic, aligning well with generative AI and modeling under uncertainty. Just as classical computing advanced despite hardware imperfections, current-generation quantum systems are producing measurable results in narrow but high-value use cases. As these outcomes become more consistent, enterprise adoption will follow. 🔷 What Enterprises Can Do Today: Quantum hardware does not need to be perfect for companies to begin exploring value. Practical entry points include: • Simulating rare or complex risk scenarios in finance and operations • Using quantum inspired sampling for better forecasting and sensitivity analysis • Generating synthetic datasets in regulated or data scarce environments • Targeting challenges where classical AI struggles, such as subtle anomalies or low signal environments • Exploring use cases in fraud detection, claims forecasting, patient risk stratification, drug efficacy modeling, and portfolio optimization 🔷 Final Thought: Quantum Machine Learning is no longer confined to research. It is becoming a tool with real strategic potential. Organizations that begin investing in awareness, experimentation, and talent today will be better positioned to lead as the ecosystem matures. #QuantumMachineLearning #QuantumComputing #AI

Interesting approach alert! QUBO-based SVM tested on QPU (Neutral Atoms). A recent study, "QUBO-based SVM for credit card fraud detection on a real QPU," explores the application of a novel quantum approach to a critical cybersecurity challenge: credit card fraud detection. Here are some of the key findings: * QUBO-based SVM model: The study successfully implemented a Support Vector Machine (SVM) model whose training is reformulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem. This approach could leverage the capabilities of quantum processors. * Performance: The results demonstrate that a version of the QUBO SVM model, particularly when used in a stacked ensemble configuration, achieves high performance with low error rates. The stacked configuration uses the QUBO SVM as a meta-model, trained on the outputs of other models. * Noise robustness: Surprisingly, the study observed that a certain amount of noise can lead to enhanced results. This is a new phenomenon in quantum machine learning, but it has been seen in other contexts. The models were robust to noise both in simulations and on the real QPU. * Scalability: Experiments were extended up to 24 atoms on the real QPU, and the study showed that performance increases as the size of the training set increases. This suggests that even better results are possible with larger QPUs. Practical implications: This research highlights the potential of quantum machine learning for real-world applications, using a hybrid approach where the training is performed on a QPU and the testing on classical hardware. This approach makes the model applicable on current NISQ devices. The model is also advantageous because it uses the QPU only for training, reducing costs and allowing the trained model to be reused. * Ideal for cybersecurity and regulatory issues: The study also observed that the model preserves data privacy because only the atomic coordinates and laser parameters reach the QPU, and the model test is done locally. Here the article: https://lnkd.in/d5Vfhq2G #quantumcomputing #machinelearning #cybersecurity #frauddetection #neutralatoms #QPU #NISQ #quantumml #fintech #datascience

🌟 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 𝘄𝗶𝘁𝗵 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 🌟 Excited to share a groundbreaking study that explores the potential of quantum computing in transforming the pharmaceutical industry! 🚀💊 🧪 𝗙𝗼𝗰𝘂𝘀: Precise determination of Gibbs free energy profiles for prodrug activation. Accurate simulation of covalent bond interactions. This pioneering work goes beyond conventional proof-of-concept studies by addressing real-world drug design challenges. By constructing a versatile quantum computing pipeline, the researchers have taken significant steps towards integrating quantum computation into practical drug discovery workflows. 🧬🔗 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: 💥 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗧𝗵𝗲𝗼𝗿𝗲𝘁𝗶𝗰𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝗧𝗮𝗻𝗴𝗶𝗯𝗹𝗲 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Unlike previous studies that were primarily theoretical, this research implements a hybrid quantum computing pipeline to solve practical problems in drug design. This marks a significant shift towards real-world applicability of quantum computing in pharmaceuticals, making it a valuable tool for researchers and industry professionals. 💥 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 𝗤𝘂𝗮𝗻𝘁𝘂𝗺 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗔𝗴𝗮𝗶𝗻𝘀𝘁 𝗩𝗲𝗿𝗶𝘁𝗮𝗯𝗹𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗶𝗻 𝗗𝗿𝘂𝗴 𝗗𝗲𝘀𝗶𝗴𝗻: The study sets a new benchmark by applying quantum computing to actual drug design scenarios. This involves precise calculations and simulations that are critical in the drug discovery process, showcasing the capability of quantum computing to handle complex biochemical problems that traditional methods struggle with. 💥 𝗘𝗺𝗽𝗵𝗮𝘀𝗶𝘇𝗶𝗻𝗴 𝗖𝗼𝘃𝗮𝗹𝗲𝗻𝘁 𝗕𝗼𝗻𝗱𝗶𝗻𝗴 𝗜𝘀𝘀𝘂𝗲𝘀 𝗶𝗻 𝗖𝗮𝘀𝗲 𝗦𝘁𝘂𝗱𝗶𝗲𝘀: The research specifically targets covalent bond interactions, a crucial aspect in drug development. By focusing on the precise determination of Gibbs free energy profiles for prodrug activation and accurate simulation of covalent bond interactions, the study addresses critical tasks that are central to designing effective drugs. This focus on covalent bonding issues underscores the practical significance of the study. The results demonstrate the immense potential of quantum computing in creating scalable solutions for the pharmaceutical industry. This is a remarkable step forward in the quest to revolutionize drug discovery and design! 🌐💡 Citation: Li, W., Yin, Z., Li, X. et al. A hybrid quantum computing pipeline for real world drug discovery. Sci Rep 14, 16942 (2024). https://lnkd.in/d3mkrAPs #QuantumComputing #DrugDiscovery #Pharmaceuticals #Innovation #Technology #Science #Research

Quantum computing for financial mathematics A key paper published in 2023 by Jack Jacquier, Oleksiy Kondratyev, Gordon Lee, and Mugad Oumgari reviews the state of quantum computing in financial mathematics and leaves a clear message: the value is not in waiting for the perfect machine, but in how we manage the transition with what we already have. Three application lines highlighted by the authors - Portfolio optimization with variational algorithms (QAOA, VQE), where hybrid approaches already help explore scenarios that scale poorly in the classical world. - Quantum Machine Learning, with generative and discriminative models (QGANs, QNNs, Quantum Circuit Born Machines) applied to market data generation, credit scoring, and detection of distribution shifts. - Quantum Monte Carlo, with algorithms achieving a quadratic speedup in expectation estimation, useful for high-dimensional derivative pricing. Other areas mentioned The paper also points to the potential of Quantum Semidefinite Programming (QSDP) for robust risk management and portfolio optimization under uncertainty. The key takeaway The authors emphasize: it’s not just about speed, it’s about thinking differently. - Use quantum algorithms to accelerate critical steps of classical pipelines. - Develop hybrid and quantum-inspired schemes. - Prepare data structures and methodologies that can scale once hardware matures. Ultimately: the real race lies in turning current limitations into opportunities for integration and new value models, while technological acceleration follows its own path. Link https://lnkd.in/d-CPDkN9 Imperial College London Abu Dhabi Investment Authority (ADIA) Lloyds Banking Group

D-Wave’s Quantum Leap: Solving Ford’s Real-World Optimization Problem Quantum Annealing Meets Industry as D-Wave Tackles Automotive Challenges In a significant milestone for applied quantum computing, Palo Alto-based D-Wave Quantum Inc. has demonstrated how its hybrid quantum-classical platform can solve real-world industrial problems—most recently for global automobile giant Ford Motor Company. The breakthrough signals a shift from theoretical promise to practical implementation, as quantum computing begins to deliver measurable benefits in the manufacturing and logistics sectors. Quantum Computing’s Practical Edge • What Makes Quantum Different • Unlike classical computers that operate using bits (0s and 1s), quantum computers leverage quantum states, enabling them to process vast combinations of variables simultaneously. • This capability is particularly powerful for problems involving optimization, pattern recognition, and combinatorial complexity—areas where traditional supercomputers often hit limits. • D-Wave’s Unique Approach: Quantum Annealing • D-Wave uses a quantum annealing architecture, ideal for finding optimal solutions by simulating the way natural systems seek their lowest energy state. • Its hybrid system blends quantum processors with classical algorithms, making the platform ready for real-world use today, unlike more fragile gate-based quantum systems still in development. Ford’s Optimization Problem and D-Wave’s Solution • Industrial Workflow Optimization • Ford sought to improve operational efficiency in its manufacturing and logistics systems—complex processes involving thousands of interdependent variables. • Using D-Wave’s quantum annealing platform, the problem was modeled as an energy landscape, and the machine rapidly identified the lowest-energy (most efficient) configuration. • Real-World Impact • This approach led to more streamlined scheduling, reduced production delays, and optimized inventory management, demonstrating tangible ROI. • Ford’s case illustrates how quantum computing can already be integrated into existing enterprise workflows, offering a glimpse of how industry can benefit before universal quantum computers are available. Why It Matters for the Quantum Ecosystem • Bridging Theory and Application • D-Wave’s success highlights a commercially viable path for quantum technology through targeted problem-solving, particularly in logistics, finance, automotive, and pharmaceuticals. • The company’s hybrid architecture bypasses the need for error correction or extremely low error rates, giving it a first-mover advantage in real-world deployments. • Growing Momentum Across Sectors • This milestone reinforces the belief that quantum value creation doesn’t have to wait for fault-tolerant, general-purpose machines. • It also raises the bar for startups and tech giants competing in the quantum space, accelerating the push toward broader industrial adoption.

Our R&D team at Stellium Inc. has recently been diving deep into concepts like quantum machine learning and quantum PCA, with the goal of identifying the best levers out there to address supply chain challenges with emerging tech. After our most recent midmonth Innov8 workshop, I’m no longer surprised by the fact that the market size for quantum computing is projected to grow at a CAGR of 18+% during the forecast period 2025-2032. The modern supply chain, as we all know, forms a sophisticated network of interconnected elements, where decision-making amid complexity often involves significant uncertainty. Effective management hinges on processing vast streams of real-time data to minimize costs and fulfill customer demands. As these global systems expand, classical computing approaches are reaching their limits in processing speed and handling intricate modeling. Enter Quantum Computing: 🎱 Quantum solutions are exceptionally positioned to tackle the most demanding challenges in logistics, including route optimization, operational efficiency, and emissions reduction. This capability stems from foundational quantum mechanics principles such as Superposition, Interference and Entanglement, that are redefining computational processes. For supply chain executives, this really boils down to resolving complex problems more rapidly than classical algorithms, including those on supercomputers. The aim is to develop responsive analytics through dramatically reduced computation times. Large scale supply chain optimization problems are no longer going to need hrs or days but rather seconds. Industry researchers and a few enterprises are already applying techniques such as the Quantum Approximate Optimization Algorithm (QAOA) and Quantum Annealing. These methods reformulate combinatorial challenges, like the traveling salesman problem in transportation logistics into quantum frameworks, identifying optimal solutions by reaching the ‘minimum energy state’. We are now seeing progress beyond conceptual stages to practical Proofs of Concept (PoCs): • BMW Group applied recursive QAOA to address partitioning issues in supply chain resource allocation. • Volkswagen demonstrated real-time optimal routing through urban traffic variations. • Coca-Cola Bottlers Japan Inc. utilized quantum computing to refine their logistics for a network exceeding 700,000 vending machines. Quantum-powered logistics and supply chain innovations are poised for substantial growth in the years ahead. Forward-thinking organizations recognize the impending transformation and are proactively preparing to become quantum-ready. At Stellium Inc., we are in our early R&D stage when it comes to exploring quantum use cases and strategic partnerships. I am bullish about the impact it’s going to have on supply chain and recognize the need to invest in it right now. DM if you’re interested to discuss more over coffee at Dubai this coming week or at SAP Connect early October in Vegas.