Quantum Machine Learning Strategies for Noisy 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

Excited to share another new #QuantumComputing result from Global Technology Applied Research at JPMorganChase.  We have justed posted a new arXiv preprint titled "On Speedups for Convex Optimization via Quantum Dynamics" (https://lnkd.in/e2sRz_my), which follows our recent work on “Fast Convex Optimization with Quantum Gradient Methods”(https://lnkd.in/eMtqXM-r). Convex optimization is a fundamental subroutine in #machinelearning, #engineering, and #datascience with many applications in #FinancialEngineering, and understanding the full potential for #quantum speedup is of great interest.   Complementing our previous research on quantum gradient methods, we now consider a natural optimization algorithm inspired by physics, namely, the simulation of a quantum particle subject to a potential defined by the objective function. Specifically, we study discrete simulations of the Quantum Hamiltonian Descent (QHD) framework (https://lnkd.in/e9xw_DDb) and establish the first rigorous query complexity bounds for this approach. Our findings reveal that, while the simulation of QHD probably does not improve upon classical algorithms for exact objective functions, it in fact offers a super-quadratic speedup over all known classical algorithms in the high-dimensional regime for noisy or stochastic convex optimization! These settings are common in machine learning, #reinforcementlearning, and #portfoliooptimization with empirically calibrated parameters. Our research highlights the potential for large quantum speedups on such problems.   Together with our previous work, this illustrates that gradient-based and dynamical methods for quantum convex optimization are complementary: with quantum gradient methods providing large speedups in the noiseless setting, and the dynamical approach providing large speedups in the noisy and stochastic setting.   Co-authors: Shouvanik Chakrabarti, Dylan Herman, Jacob Watkins, Enrico Fontana, Brandon Augustino, Junhyung Lyle Kim, and Marco Pistoia.

🚀 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

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