Understanding Quantum Machine Learning Applications

Quantum Machine Learning (QML) offers a new paradigm for addressing complex financial problems intractable for classical methods. This work specifically tackles the challenge of few-shot credit risk assessment, a critical issue in inclusive finance where data scarcity and imbalance limit the effectiveness of conventional models. To address this, the researchers design and implement a novel hybrid quantum-classical workflow. The methodology first employs an ensemble of classical machine learning models (Logistic Regression, Random Forest, XGBoost) for intelligent feature engineering and dimensionality reduction. Subsequently, a Quantum Neural Network (QNN), trained via the parameter-shift rule, serves as the core classifier. This framework was evaluated through numerical simulations and deployed on the Quafu Quantum Cloud Platform's ScQ-P21 superconducting processor. On a real-world credit dataset of 279 samples, the QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment. This performance surpasses a suite of classical benchmarks, with a particularly strong result on the recall metric. This study provides a pragmatic blueprint for applying quantum computing to data-constrained financial scenarios in the NISQ era and offers valuable empirical evidence supporting its potential in high-stakes applications like inclusive finance.

> Sharing Resource < Ok, that's huge: "Exponential quantum advantage in processing massive classical data" by Haimeng Zhao, Alexander Zlokapa, Hartmut Neven, Ryan Babbush, John Preskill, Jarrod R. McClean, Hsin-Yuan (Robert) Huang 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. Link: https://lnkd.in/gmA-ntVU #quantummachinelearning #quantumcomputing #research #paper #bigdata #logicalqubits

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

I’d like to draw your attention to a new paper on arXiv, “Shallow-circuit Supervised Learning on a Quantum Processor”, from IBM and Qognitive that develops a Hamiltonian-based framework for quantum machine learning. Instead of fixed amplitude or angle encodings used in many prior approaches, our method learns a local Hamiltonian embedding for classical data. https://lnkd.in/ejcxYstW We are very interested in new approaches to QML as we deal with recurring bottlenecks like expensive classical data loading and difficult training dynamics in parameterized circuit models. Here, both the feature operators and the label operator are learned during training, with predictions obtained from measurements on an approximate ground state. This aims to avoid those bottlenecks. A key enabler is Sample-based Krylov Quantum Diagonalization (SKQD), which approximates low-energy states by sampling from time-evolved Krylov states and then diagonalizing the Hamiltonian in the sampled subspace. SKQD was recently employed to estimate low-energy properties of impurity models (https://lnkd.in/epwCrG5R). In our setting, restricting to 2-local Hamiltonian embeddings keeps the required time-evolution circuits relatively shallow, which helps make the approach practical on current quantum processors. The team demonstrates end-to-end training on IBM Heron processor up to 50 qubits, with non-vanishing gradients and strong proof-of-concept performance on a binary classification task. There are many exciting next steps here, including testing on broader datasets, using more expressive operator ansatz, and performing systematic comparisons to strong classical baselines to pinpoint when Hamiltonian-based encodings offer the right inductive bias. I encourage the community to try out this approach and explore where it can be extended in meaningful ways.

🚀 New Paper on arXiv! I’m excited to share our latest work: “Learning to Program Quantum Measurements for Machine Learning” 📌 arXiv: https://lnkd.in/euRhBQJM 👥 With Huan-Hsin Tseng (Brookhaven National Lab), Hsin-Yi Lin (Seton Hall University), and Shinjae Yoo (BNL) In this paper, we challenge a long-standing limitation in quantum machine learning: static measurements. Most QML models rely on fixed observables (e.g., Pauli-Z), limiting the expressivity of the output space. We take this one step further--by making the quantum observable (Hermitian matrix) a learnable, input-conditioned component, programmed dynamically by a neural network. 🧠 Our approach integrates: 1. A Fast Weight Programmer (FWP) that generates both VQC rotation parameters and quantum observables 2. A differentiable, end-to-end architecture for measurement programming 3. A geometric formulation based on Hermitian fiber bundles to describe quantum measurements over data manifolds 🧪 Experiments on noisy datasets (make_moons, make_circles, and high-dimensional classification) show that our dual-generator model outperforms all traditional baselines—achieving faster convergence, higher accuracy, and stronger generalization even under severe noise. We believe this work opens the door to adaptive quantum measurements and paves the way toward more expressive and robust QML models. If you're working on QML, differentiable quantum programming, or quantum meta-learning, I’d love to connect! #QuantumMachineLearning #QuantumComputing #QML #FastWeightProgrammer #DifferentiableQuantumProgramming #arXiv #HybridAI #AI #Quantum

Any new approach to having a more efficient quantum encoding method in QML? Here's an interesting and novel perspective. A new study titled "A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning" introduces an interesting approach to address a significant barrier in Quantum Machine Learning (QML): efficiently embedding high-dimensional datasets onto noisy, low-qubit quantum systems. The research proposes Quantum Principal Geodesic Analysis (qPGA), a non-invertible method for dimensionality reduction and qubit-efficient encoding. Unlike existing quantum autoencoders, which can be constrained by current hardware and may be vulnerable to reconstruction attacks, qPGA offers a robust alternative. Key outcomes of this study include: * Qubit-efficient encoding: qPGA leverages Riemannian geometry to project data onto the unit Hilbert sphere (UHS), generating outputs inherently suitable for quantum amplitude encoding. This technique significantly reduces qubit requirements for amplitude encoding, allowing high-dimensional data to be mapped onto small-qubit systems. * Preservation of data structure: The method preserves the neighborhood structure of high-dimensional datasets within a compact latent space. Empirical results on MNIST, Fashion-MNIST, and CIFAR-10 datasets show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders. * Enhanced resistance to reconstruction attacks: Due to its non-invertible nature and lossy compression, qPGA enhances resistance to reconstruction attacks, offering better defense against data privacy leakage compared to quantum-dependent encoders like Quantum Autoencoders (QE) and Hybrid Quantum Autoencoders (HQE). * Noise-resilient and scalable: Initial tests on real hardware and noisy simulators confirm qPGA's potential for noise-resilient performance, offering a scalable solution for advancing QML applications. The study also provides theoretical bounds quantifying qubit requirements for effective encoding onto noisy systems. Here more details: https://lnkd.in/dSz_xM2q #qml #machinelearning #datascience #ml #quantum

Quantum-inspired machine learning will continue to play a significant role improving Artificial Intelligence performance. This is primarily due to myriad available applications utilizing efficient tensor network approximations that can be applied to complex or quantum systems. [01] Tensor networks, alongside active areas of dequantized algorithms and quantum variational algorithms represent effective 'QiML 2.0' software run on traditional computers. [02-04] In addition, quantum-inspired analogues will likely be further extended akin to Physics-Informed Machine Learning to 'assist machine learning tasks, representation of physical prior, and methods for incorporating physical prior.' [05] Recent ground breaking literature shown below has elevated tensor networks from efficient research tools to now increasing the pace of AI across disciplines. A) Tensor network/neural network hybrid performed better than standalone tensor networks or neural networks by NSF, MIT, and Harvard researchers. [06] B) More explainable and controllable compression of a Generative AI LLM to a fraction of its size by Multiverse Computing. [07] C) Researchers outperformed a leading quantum computer experiment in speed, precision, and accuracy - with scaling now corresponding to an infinite number of quantum bits on traditional hardware by Flatiron Institute, NYU. [08] Leading software platforms ITensor on C++ and Julia, and TeNPy on Python have been featured in a number of 2024 papers, and both maintain discussion forums to assist with tensor network developments. [09-12] In summary, High dimensional data in AI can now be distributed across tensor networks in more informed ways due to recent literature advancements and software library improvements. References [01] Tensor networks: https://lnkd.in/gipfeK_q [02] Ewin Tang: https://lnkd.in/gfgNSfKY [03] VQA: https://lnkd.in/gitb6TSq [04] QiML survey: https://lnkd.in/g97vr3_r [05] Physics-Informed ML: https://lnkd.in/gffmUFSx [06] NSF, MIT, Harvard: https://lnkd.in/gNkXEUtW [07] Multiverse: https://lnkd.in/gjNsqWJu [08] Flatiron, NYU: https://lnkd.in/gZmyJckE   [09] ITensor: https://lnkd.in/gXJWFNCU  [10] TeNPy: https://lnkd.in/g3Ciyyxs [11] ITensor: https://lnkd.in/gwhAp4BE [12] TeNPy: https://lnkd.in/gU5ceMFd

The Next Frontier—Why Quantum Machine Learning (QML) is a Strategic Imperative If you think your current Gen AI strategy is long-term, you're missing the horizon. The true strategic battle is already shifting to the convergence of Quantum Computing (QC) and Artificial Intelligence. 🚀 This isn't theory; this is the next high-stakes technological leap that will define market dominance for the next decade. As senior tech leaders, we must move past just utilizing existing LLMs and start funding the exploration of Quantum Machine Learning (QML). Here is why QML is a strategic bet, 1. Accelerated Training: Gaining Unfair Speed We are constrained by classical computing limits when processing massive, high-dimensional datasets for today's complex AI models. The Strategic Shift: Quantum algorithms—using superposition and entanglement—will process data exponentially faster than we can today. This isn't just a performance boost; it’s a competitive advantage. We will gain the ability to build and deploy the next generation of powerful LLMs and complex image recognition systems while our competition is still struggling to finish training their current models. Speed becomes an unfair strategic differentiator. 2. Solving Complex Optimization: The Pursuit of Perfect AI Many of our most challenging AI problems—like finding the optimal weights in deep learning models or perfecting complex neural network architectures—are optimization problems that classical computers can only approximate. The Strategic Win: Quantum algorithms like QAOA (Quantum Approximate Optimization Algorithm) are uniquely suited to tackling these challenges. This means we can move from "good enough" AI to perfectly tuned, highly robust AI. By perfectly tuning complex model parameters, QML will reduce the black box problem and deliver more accurate, reliable models in production, eliminating the costly instability that plagues today's deep learning systems. 3. Generating Unique Data: Manufacturing Disruption The greatest scarcity in advanced AI is not compute power; it is high-quality, unique data. Quantum computers excel at simulating quantum systems, like molecules. The Strategic Imperative: I see this as the key to market disruption. We can use QC's ability to simulate nature to generate synthetic, high-quality data that is impossible to collect in the real world (e.g., simulating a new protein fold for drug discovery). This unique, proprietary data can then be fed into our classical AI models, making them smarter, more predictive, and ultimately, leading to breakthroughs that dominate entire new markets—a phenomenon Google researchers are already highlighting with their "Quantum Echoes" work. We must treat QML not as a research curiosity, but as a strategic planning initiative today. The leaders who secure the necessary expertise and resources now are the ones who will define the technological and financial landscape of 2030. #QuantumComputing #QML #GenAI #TechStrategy #Leadership