Advancing Data Analytics with Quantum Computing

Exponential quantum advantage in processing massive classical data by John Preskill https://lnkd.in/eUTvGHaX Abstract Broadly applicable quantum advantage, particularly in classical data processing and machine learning, has been a fundamental open problem. In this work, we prove that a small quantum computer of polylogarithmic size can perform large-scale classification and dimension reduction on massive classical data by processing samples on the fly, whereas any classical machine achieving the same prediction performance requires exponentially larger size. Furthermore, classical machines that are exponentially larger yet below the required size need superpolynomially more samples and time. We validate these quantum advantages in real-world applications, including single-cell RNA sequencing and movie review sentiment analysis, demonstrating four to six orders of magnitude reduction in size with fewer than 60 logical qubits. These quantum advantages are enabled by quantum oracle sketching, an algorithm for accessing the classical world in quantum superposition using only random classical data samples. Combined with classical shadows, our algorithm circumvents the data loading and readout bottleneck to construct succinct classical models from massive classical data, a task provably impossible for any classical machine that is not exponentially larger than the quantum machine. These quantum advantages persist even when classical machines are granted unlimited time or if BPP = BQP, and rely only on the correctness of quantum mechanics. Together, our results establish machine learning on classical data as a broad and natural domain of quantum advantage and a fundamental test of quantum mechanics at the complexity frontier.

Exciting work from Caltech, Google Quantum AI, MIT, and Oratomic on quantum advantage for classical machine learning. The long standing question: can quantum computers offer a rigorous advantage in large scale classical data processing, not just specialized problems like cryptography or quantum simulation? This paper gives rigorous results for formalized machine learning tasks. In the benchmarks they report, a quantum computer with fewer than 60 logical qubits performs classification and dimension reduction on massive datasets using 4 to 6 orders of magnitude less memory than the classical and QRAM based baselines in the paper. The key idea is quantum oracle sketching. Instead of loading an entire dataset into quantum memory, it streams classical samples one at a time, applies small quantum rotations, and discards each sample immediately. These operations coherently build an approximate quantum oracle that can then be used in downstream quantum algorithms. The authors present numerical experiments on IMDb sentiment analysis and single cell RNA sequencing that are consistent with the theory. What makes this notable: - A provable quantum memory advantage for classification and dimension reduction - The advantage is framed as a theorem under the paper's learning model, not just a conjecture or empirical trend - The approach is designed to work with streaming, noisy, and time varying classical data Read the paper here: https://lnkd.in/g77PuZzQ

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.

🚀Faster Algorithms for Big-Data Clustering🚀   k-means #clustering is among the most widely used methods in the field of #DataScience and unsupervised #MachineLearning. The recent work by the #QuantumComputing team in the Global Technology Applied Research group at JPMorganChase, titled “Provably Faster Randomized and Quantum Algorithms for k-means Clustering via Uniform Sampling” (https://lnkd.in/euKTX5Fe) has made a significant advancement to develop faster clustering algorithms for big-data problems.   The most popular algorithm for k-means clustering is Lloyd’s algorithm, which is readily available in the Scikit-learn k-means package (https://lnkd.in/eD48CHjV). A key bottleneck of Lloyd’s algorithm is that each iteration requires time linear in the number of data points, which can be expensive in big-data applications.    Our first major contribution is a description of a simple randomized mini-batch clustering algorithm via uniform sampling to probably speedup the per-iteration clustering step. Additionally, our worse-case sample complexity bounds significantly improve upon the bounds of previously proposed quantum-inspired algorithm (https://lnkd.in/eNX3ZZNF).   Our second major contribution is the construction of a new quantum algorithm based on the classical mini-batch algorithm. The quantum algorithm provides a quadratic improvement in sample complexity bounds compared to our classical algorithm, and also improves upon the two quantum algorithms previously proposed (https://lnkd.in/eTR3Zhpy, https://lnkd.in/eNX3ZZNF).   This improvement is due to a more fine-grained analysis of our algorithms and the key observation that previous #quantum and #QuantumInspired algorithms do not preserve the k-means problem symmetry corresponding to rigid-body transforms of the data, and can therefore fail to produce good centers even on intuitively easy-to-cluster datasets. The algorithms we study, whose core is uniform sampling, are invariant to such transforms, leading to the improved bounds.   Authors: Tyler Chen, Archan Ray, Akshay Seshadri, Dylan Herman, Bao Bach, Pranav Deshpande, Abhishek Som, Niraj Kumar, and Marco Pistoia (all authors from JPMorganChase).

“The convergence of #artificialintelligence (#AI) and #quantum computing (QC) holds transformational potential across the economy. .. Though independent technologies, QC and AI can complement each other in many significant and multidirectional ways. For example, AI could assist QC by accelerating the development of circuit design, applications, and error correction and generating test data for algorithm development. QC can solve certain types of problems more efficiently, such as optimization and probabilistic tasks, potentially enhancing the ability of AI models to analyze complex patterns or perform computations that are infeasible for classical systems. A hybrid approach integrating the strengths of classical AI methods with the potential of QC algorithms leverages the two technologies to substantially reduce algorithmic complexity, improving the efficiency of computational processes and resource allocation.” The study identifies promising application areas where QC + AI could deliver early impact. In financial services these include, not exhaustively: ➡️ Portfolio optimization: QC + AI could solve complex portfolio rebalancing and risk-return optimization problems faster and at larger scale than classical methods. ➡️ Risk modeling and stress testing: hybrid QC + AI systems could simulate extreme market scenarios and systemic contagion effects with greater accuracy. ➡️ Fraud detection and anti-money laundering (AML): AI models enhanced by QC’s pattern recognition potential could identify anomalies in massive transaction datasets more efficiently. ➡️ Option pricing and derivatives valuation: quantum algorithms could improve accuracy in pricing complex, path-dependent financial instruments where classical Monte Carlo simulations are costly. ➡️ Credit risk assessment: combining QC-enhanced optimization with AI could improve scoring models for borrowers and counterparties by analyzing large, multidimensional datasets. ➡️ Algorithmic trading: QC-assisted optimization could improve trade execution strategies under multiple constraints, balancing latency, liquidity, and cost. ➡️ Supply chain and trade finance: QC + AI could optimize logistics and cash-flow forecasts across global trade networks, in complex logistical chains, reducing financing risks. ➡️ Climate and ESG risk analytics – QC-enhanced simulations could model environmental and economic interdependencies, supporting sustainable finance decisions. ➡️ Cybersecurity and quantum-safe finance – AI applied to quantum sensing and post-quantum cryptography could strengthen detection and defense mechanisms for financial networks.   Looking ahead, the report highlights lessons from AI, such as the importance of benchmarks, responsible use frameworks, and managing hype cycles, that QC can adopt early to avoid pitfalls. https://lnkd.in/dYr74YyK #digital #genAI #banking #creditrisk #fraud #Cybersecurity Nafis Alam Dr. Martha Boeckenfeld Prasanna Lohar Dr. Debashis Dutta

Quantum Computing and AI Join Forces for Powerful Data Analysis Breakthrough in Quantum Machine Learning • Researchers have combined quantum computing and machine learning to analyze massive datasets quickly and accurately. • The technique has been tested on groundwater monitoring and is now being explored for traffic management and medical imaging. Why This Matters • Quantum computing harnesses properties like superposition to process information exponentially faster than classical computers. • Machine learning helps extract patterns from vast datasets, making the combination a game-changer for scientific and industrial applications. Key Insights from CSIRO Scientist Dr. Muhammad Usman • Quantum technology is evolving rapidly, and though practical quantum computers are still years away, simulations on classical machines have already demonstrated impressive results. • Groundwater monitoring was chosen as a test case due to its complexity and high cost, proving the potential of quantum-enhanced AI in solving real-world problems. Potential Applications • Environmental Science – Predicting water quality and resource sustainability. • Traffic Management – Optimizing urban transportation systems in real-time. • Medical Imaging – Enhancing diagnostics through faster, AI-driven analysis. As quantum computing continues to advance, integrating it with AI could revolutionize data science, engineering, and decision-making across multiple industries.

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.

🚀 Quantum Machine Learning (QML) vs. Machine Learning (ML) The convergence of Quantum Computing and AI is often described as a “match made in heaven.” But beyond the hype, it represents something more important: 👉 A potential shift in how we approach computational limits in AI. 🧠 What is Quantum Machine Learning (QML)? QML sits at the intersection of quantum physics and machine learning—exploring how quantum systems can process and learn from data in fundamentally different ways. The goal? 👉 To tackle problems that are computationally infeasible for classical systems. 🛑 Where Classical ML Faces Limits Despite massive advances in CPUs and GPUs, classical ML still encounters real constraints: ⏳ Compute & time costs scale rapidly with data and model complexity ⛓️ Sequential processing limits restrict exploration of solution spaces 📊 Data and model constraints can limit performance in highly complex systems ⚡ What Quantum Brings to the Table QML introduces new computational primitives grounded in quantum mechanics: 🌌 Superposition — Qubits can represent multiple states simultaneously 🔗 Entanglement — Enables complex correlations beyond classical systems 📉 Potential speedups — Certain classes of problems may see exponential gains 💡 Early research suggests promise in areas like optimization, simulation, and complex pattern recognition—though results remain highly context-dependent. 🛠 The Reality: Still Early Days QML is not yet production-ready at scale. Key challenges remain: 1️⃣ Quantum data encoding — Efficiently mapping classical data into quantum states 2️⃣ Hardware limitations — Noise, instability, and limited qubit counts 3️⃣ Hybrid approaches — Combining classical and quantum systems effectively 🎯 The Bottom Line Classical ML is mature, scalable, and here to stay. Quantum ML is emerging, experimental, and promising—particularly for high-complexity problems where classical approaches hit fundamental limits. 👉 The future is unlikely to be “QML vs ML” 👉 It will be QML + ML, working together in hybrid systems 💭 The real opportunity isn’t just faster models— it’s rethinking what problems we can solve at all. Follow and Connect: Woongsik Dr. Su, MBA #QuantumComputing #MachineLearning #AI #QuantumAI #EmergingTech #DeepTech #Innovation

🚀 𝐀𝐖𝐄𝐒𝐎𝐌𝐄: 𝐓𝐇𝐄 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐏𝐀𝐑𝐀𝐃𝐈𝐆𝐌 — 𝐔𝐍𝐃𝐄𝐑𝐒𝐓𝐀𝐍𝐃𝐈𝐍𝐆 𝐐𝐍𝐍𝐬 Quantum Neural Networks (QNNs) represent the ultimate convergence of AI, Physics, and Mathematics. We are moving beyond the era of "simulating" intelligence on silicon and entering an era where we leverage the fundamental laws of nature to process information. This isn't just a faster version of what we have; it is a 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐥𝐲 𝐧𝐞𝐰 𝐜𝐨𝐦𝐩𝐮𝐭𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐠𝐫𝐚𝐦𝐦𝐚𝐫. 𝟏. 𝐓𝐇𝐄 𝐐𝐔𝐀𝐍𝐓𝐔𝐌 𝐀𝐃𝐕𝐀𝐍𝐓𝐀𝐆𝐄 𝐈𝐍 𝐀𝐈 Classical Neural Networks rely on bits and linear algebra. QNNs leverage quantum phenomena to explore a state space that is exponentially larger. 𝐒𝐮𝐩𝐞𝐫𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧: Allows the network to process a massive "blend" of input states simultaneously, rather than checking them sequentially. 𝐄𝐧𝐭𝐚𝐧𝐠𝐥𝐞𝐦𝐞𝐧𝐭: Captures hidden, non-local correlations in data that classical models—even the most complex Transformers—simply cannot see. 𝐇𝐢𝐥𝐛𝐞𝐫𝐭 𝐒𝐩𝐚𝐜𝐞: While a classical model operates in a flat vector space, a QNN operates in a high-dimensional Hilbert space, where complex patterns are easier to separate and classify. 𝟐. 𝐓𝐇𝐄 𝐇𝐘𝐁𝐑𝐈𝐃 𝐖𝐎𝐑𝐊𝐅𝐋𝐎𝐖: 𝐇𝐎𝐖 𝐈𝐓 𝐅𝐔𝐍𝐂𝐓𝐈𝐎𝐍𝐒 We aren't replacing classical computers yet; we are using them as "controllers" for quantum processors. This is the 𝐕𝐚𝐫𝐢𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 approach: 𝐐𝐮𝐚𝐧𝐭𝐮𝐦 𝐄𝐧𝐜𝐨𝐝𝐢𝐧𝐠: Classical data (like an image or a chemical string) is mapped into a quantum state $|\psi\rangle$. 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫𝐢𝐳𝐞𝐝 𝐂𝐢𝐫𝐜𝐮𝐢𝐭𝐬: Instead of "weights" in a neuron, we have "rotation angles" in quantum gates. 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐦𝐞𝐧𝐭: The quantum state collapses into a classical output. 𝐂𝐥𝐚𝐬𝐬𝐢𝐜𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫: A standard computer uses Gradient Descent to adjust the quantum gate angles for the next pass. 𝟑. 𝐑𝐄𝐀𝐋-𝐖𝐎𝐑𝐋𝐃 𝐒𝐓𝐑𝐀𝐓𝐄𝐆𝐈𝐂 𝐈𝐌𝐏𝐀𝐂𝐓 QNNs shine where classical models hit a "wall" of complexity: 𝐃𝐫𝐮𝐠 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐲: Simulating molecules is a quantum problem. Using a quantum computer to design drugs is like using a 3D printer to build a 3D object, rather than drawing it on paper. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Solving the "Traveling Salesperson Problem" for global logistics or finding the lowest risk in a multi-billion dollar portfolio. 𝐀𝐈 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Training LLMs currently costs hundreds of millions in electricity. QNNs could theoretically find the optimal "weights" using a fraction of the energy. 𝟒. 𝐓𝐇𝐄 𝐁𝐈𝐆 𝐈𝐃𝐄𝐀: 𝐏𝐇𝐘𝐒𝐈𝐂𝐒-𝐈𝐍𝐅𝐎𝐑𝐌𝐄𝐃 𝐀𝐈 We are shifting from "𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐦𝐨𝐝𝐞𝐥𝐬 𝐨𝐧 𝐝𝐚𝐭𝐚" to "𝐝𝐞𝐬𝐢𝐠𝐧𝐢𝐧𝐠 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐜𝐨𝐦𝐩𝐮𝐭𝐞 𝐮𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐥𝐚𝐰𝐬 𝐨𝐟 𝐧𝐚𝐭𝐮𝐫𝐞." By aligning our algorithms with the way the universe actually works at a subatomic level, we unlock a level of efficiency and intelligence that was previously considered science fiction.

Lets Learn #Quantum – Post 14: Quantum Machine Learning Beyond the Limits of Classical AI: The QML Frontier Imagine training AI systems to analyze massive financial data, cybersecurity threats, or global logistics networks. Now imagine the data becomes so large and interconnected that classical AI struggles to process patterns efficiently. This is where Quantum Machine Learning (QML) enters the picture. Modern AI increasingly faces challenges with high-dimensional data, long training times, and complex optimization limitations. Hybrid systems bridge this gap by combining classical neural networks with quantum circuits. The Quantum AI Breakthrough Let’s simplify this with a real-world scenario: Think of a logistics network optimizing routes for 50 trucks across 1,000 cities. A classical AI must calculate millions of route combinations step-by-step, quickly hitting a computational wall. A QML system evaluates the entire web of cities and traffic variables simultaneously, pinpointing the optimal paths in seconds. Traditional AI systems process large datasets sequentially, requiring enormous computing power and consuming significant energy. Quantum-enhanced AI explores a different approach, representing and processing complex data patterns in fundamentally new ways. Researchers are actively exploring key QML advantages:  * High-dimensional data representation  * Faster quantum kernel methods  * Improved optimization landscapes  * More efficient pattern discovery How Does QML Work? Quantum Machine Learning combines AI with core quantum principles:  1. **Superposition** – Evaluating thousands of data states and relationships at the same time, rather than step-by-step.  2. **Entanglement** – Instantly capturing how a change in one variable (like a sudden traffic delay) impacts the entire network.  3. **Quantum Interference** – Canceling out inefficient solutions while amplifying the most optimal, high-value patterns. The Strategic Reality AI is becoming central to nearly every industry—from fraud detection in banking to drug discovery in healthcare. Globally, governments, hyperscalers, and technology leaders are investing heavily because the future of AI requires entirely new computational approaches. Organizations that understand QML early stand to gain faster analytical capabilities, sharper optimization, and a distinct competitive advantage in decision intelligence. Classical AI transformed how machines learn from data. Quantum Machine Learning may transform how machines understand complexity itself. This is not simply “AI running faster”—it is a completely new frontier in intelligent computing.   #QuantumComputing #QuantumAI #QuantumTechnology #FutureTech #Innovation #DeepTech #AI #DataScience #DigitalTransformation #soyoucan Coauthored with Atul Tripathi Sundar Ram, Sachin Arora, Himanshu Ghawri, Dr. Raghav Manohar Narsalay, Navnit Nakra, Vinish Bawa, Kavan Mukhtyar, Azizur Rahman, Praveen Sasidharan, Sundareshwar K (Sundar).