Adapting Quantum Algorithms for Specialized Data

Interesting new study: "EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data." The authors introduce a novel framework to address the limitations of traditional amplitude embedding (AE) [GitHub repo included]. Traditional AE methods often involve deep, variable-length circuits, which can lead to high output error due to extensive gate usage and inconsistent error rates across different data samples. This variability in circuit depth and gate composition results in unequal noise exposure, obscuring the true performance of quantum algorithms. To overcome these challenges, the researchers developed EnQode, a fast AE technique based on symbolic representation. Instead of aiming for exact amplitude representation for each sample, EnQode employs a cluster-based approach to achieve approximate AE with high fidelity. Here are some of the key aspects of EnQode: * Clustering: EnQode begins by using the k-means clustering algorithm to group similar data samples. For each cluster, a mean state is calculated to represent the central characteristics of the data distribution within that cluster. * Hardware-optimized ansatz: For each cluster's mean state, a low-depth, machine-optimized ansatz is trained, tailored to the specific quantum hardware being used (e.g., IBM quantum devices). * Transfer Learning for fast embedding: Once the cluster models are trained offline, transfer learning is used for rapid amplitude embedding of new data samples. An incoming sample is assigned to the nearest cluster, and its embedding circuit is initialized with the optimized parameters of that cluster's mean state. These parameters can then be fine-tuned, significantly accelerating the embedding process without retraining from scratch. * Reduced circuit complexity: EnQode achieved an average reduction of over 28× in circuit depth, over 11× in single-qubit gate count, and over 12× in two-qubit gate count, with zero variability across samples due to its fixed ansatz design. * Higher state fidelity in noisy environments: In noisy IBM quantum hardware simulations, EnQode showed a state fidelity improvement of over 14× compared to the baseline, highlighting its robustness to hardware noise. While the baseline achieved 100% fidelity in ideal simulations (as it performs exact embedding), EnQode maintained an average of 89% fidelity when transpiled to real hardware in ideal simulations, which is considered a good approximation given the significant reduction in circuit complexity. Here the article: https://lnkd.in/dQMbNN7b And here the GitHub repo: https://lnkd.in/dbm7q3eJ #qml #datascience #machinelearning #quantum #nisq #quantumcomputing

#quantum + #SciML = #QuaSciML. 📄 Solving differential equations on quantum computers offers a great power, as we can represent solutions on an exponentially large and fine grid. At times it even 𝘵𝘰𝘰 powerful. Here’s a challenge: how do we even read out this vast amount of information?  In our recent work [https://lnkd.in/gW8ZSYqK], we explored this question and realized something simple yet exciting — 𝐐𝐃𝐢𝐟𝐟 𝐬𝐨𝐥𝐯𝐞𝐫𝐬, which generate solutions as quantum states, are a perfect source of 𝐪𝐮𝐚𝐧𝐭𝐮𝐦 𝐝𝐚𝐭𝐚. And to extract useful information from these states we can use specialized tools. This is where quantum scientific machine learning steps in, providing adaptive measurement operators to analyze solutions in the problem-specific way.  For example, in turbine modelling, you don’t need every tiny detail of the pressure curve. Instead, you care about actionable questions: Is the turbine faulty? What is the critical temperature for failure? For such tasks of classification and regression one can build decision boundaries by training readout operators based on few supplied labelled solutions. As a simple example, by examining computational fluid dynamics equations we classified shock waves and distinguished between turbulent and laminar flow regimes with high accuracy. Moreover, we introduced a dual quantum neural network (QNN) structure, demonstrating its efficiency in learning correlations between solutions and formulating hypotheses for these classifications.  Our findings show that analyzing quantum data from QDiff solvers could be a powerful mode of operation for QuaSciML, combining computational and learning advantages. Huge qudos to our collaboration Pasqal and Siemens Digital Industries Software, Chelsea Williams for doing hard work, and the team Daniel Berger Antonio Andrea Gentile Stefano Scali for the support. #quantumcomputing #machinelearning #quantumCFD

🚀 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