How to Apply Quantum Computing to Data Analysis

This oddly worded Reddit question reminds me that it's not widely known that a Quantum Computer doesn't "crunch the numbers" in the way we've come to assume normal computers do. Nearly all descriptions of quantum computing fall into hand waving about cats and slits and spooky whatever, with the remainder leaping straight into equations with more Greek letters than a US tariff chart. AI models are no better. You can tell when journalists use ChatGPT or Claude as it churns out the not-quite-correct "1s and 0s at the same time" analogy which does exactly nothing to show what the workflow looks like. Here's the answer I posted to Reddit. "While you don't directly "feed" a classical text file into a quantum circuit like you would with a classical program, it is indeed theoretically and practically possible to process information derived from such a file using a quantum circuit. The key is that classical data must first be encoded into the quantum system. This encoding can take various forms, such as mapping bits to qubit states, encoding information in qubit amplitudes, or using classical values to parameterize the rotation angles of quantum gates within the circuit design. The design of the quantum circuit itself, including the sequence of gates and their parameters, becomes the "program" that operates on this encoded data. Many contemporary and near-term quantum algorithms operate as hybrid quantum-classical systems. In these approaches, classical data from a file can be used to initialize the quantum circuit, define its structure, or, crucially, parameterize the quantum gates. Classical optimization algorithms then often interact with the quantum circuit, adjusting these parameters based on measurement outcomes, effectively creating a feedback loop where the classical data indirectly guides the quantum computation. While the theoretical concept of Quantum Random Access Memory (QRAM) suggests future possibilities for more direct data loading, current methods rely on encoding the classical information into the initial state or the very fabric of the quantum circuit's operation." Side note, I'm watching companies like Haiqu to see how they are tackling this data encoding problem in the realm of #quantumcomputing. Lots happening on that front.

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

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.

⚛️ Parallel Data Processing in Quantum Machine Learning 🧾 We propose a Quantum Machine Learning (QML) framework that leverages quantum parallelism to process entire training datasets in a single quantum operation, addressing the computational bottleneck of sequential data processing in both classical and quantum settings. Building on the structural analogy between feature extraction in foundational quantum algorithms and parameter optimization in QML, we embed a standard parameterized quantum circuit into an integrated architecture that encodes all training samples into a quantum superposition and applies classification in parallel. This approach reduces the theoretical complexity of loss function evaluation from O(N^2) in conventional QML training to O(N), where N is the dataset size. Numerical simulations on multiple binary and multi-class classification datasets demonstrate that our method achieves classification accuracy comparable to conventional circuits while offering substantial training time savings. These results highlight the potential of quantum-parallel data processing as a scalable pathway to efficient QML implementations. ℹ️ Ramezani et al - 2025

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

🚀 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

Trying to enter QML in 2026? This is the path I’d take, step by step. A Quantum Machine Learning roadmap should build three pillars in parallel: 1)Mathematics & Classical ML foundations 2)Quantum Computing foundations 3)Hybrid Quantum-Classical ML implementation → Advance QML Models Think of QML as ML + Linear Algebra + Quantum Mechanics + Optimization Step 1: Mathematics, Python, ML Stack, & ML Basics Linear Algebra - vectors, matrices, eigenvalues, tensor products Probability & Statistics - distributions, expectation, variance Optimization - gradient descent, loss functions Python - NumPy, SciPy, Matplotlib PyTorch or TensorFlow, Scikit-learn Supervised, Unsupervised Learning Regression, Classification, Overfitting, Regularization Neural Networks, CNN basics Goal: You should be comfortable building classical ML pipelines Step 2: Quantum Computing Foundations Qubits, superposition, measurement, Bloch sphere Quantum gates, Entanglement and Bell states Quantum circuits, Interference Quantum Algorithms - Deutsch-Jozsa, Grover’s Algorithm, Quantum Fourier Transform, Variational Quantum Algorithms Qiskit, Cirq, Q#(1 of them) Goal: You must think in circuits before doing QML Step 3: Bridge to QML Parameterized Quantum Circuits Variational circuits Classical-quantum feedback loop Cost functions Barren plateaus Expressibility & trainability Difference between: Quantum data → quantum model Classical data → quantum embedding PennyLane, TensorFlow Quantum, Qiskit ML Goal: Understand QML is optimization on quantum parameters Step 4: Core QML Models Quantum Data Encoding Angle embedding Amplitude encoding Basis encoding Quantum Models Variational Quantum Classifier Quantum Neural Networks Quantum Kernel Methods Quantum Support Vector Machines Data re-uploading circuits Compare: Classical NN vs VQC Classical SVM vs Quantum Kernel Goal: Show measurable learning, not just circuit execution Step 5: Advanced QML Concepts Barren Plateaus Noise-aware training Hardware-efficient ansatz Quantum Convolutional Neural Networks Quantum Autoencoders QGANs QML for anomaly detection NISQ Constraints - Noise, Shot statistics, Error mitigation Goal: You understand real-world limitations and research gaps Step 6: Research Grade QML Read Papers Schuld & Killoran (Quantum ML theory) Havlíček et al. (Quantum kernel methods) McClean et al. (Barren plateaus) Cerezo et al. (Variational algorithms) Hybrid classical-quantum architectures Quantum kernels vs classical kernels Data-efficient QML Noise-resilient QML QML benchmarking 5–8 serious QML projects Implement: One paper reproduction One modification or improvement Happy Learning! Save this post for later. Repost ♻️ for Quantum & AI Learners! Check my profile for more resources on Quantum & AI Tech Follow Kiran Kaur Raina here: 📌LinkedIn: https://lnkd.in/gEpKMQ7z 📌YouTube: https://lnkd.in/gTTv2ewB 📌Topmate: https://lnkd.in/gDj-kmYW 📌Medium: https://lnkd.in/gWBppT7G 📌Instagram: https://lnkd.in/g8qZKHe7