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Tag: quantum

  • What Makes Quantum Machine Learning “Quantum”?

    What Makes Quantum Machine Learning “Quantum”? And where is it today? The post What Makes Quantum Machine Learning “Quantum”? appeared first on Towards Data Science. Sara A. Metwalli Go to original source

  • Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise

    Robust Causal Directionality Inference in Quantum Inference under MNAR Observation and High-Dimensional Noise arXiv:2512.19746v1 Announce Type: new Abstract: In quantum mechanics, observation actively shapes the system, paralleling the statistical notion of Missing Not At Random (MNAR). This study introduces a unified framework for textbf{robust causal directionality inference} in quantum engineering, determining whether relations are system$to$observation,…

  • Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting

    Quantum Fourier Transform Based Kernel for Solar Irrandiance Forecasting arXiv:2511.17698v1 Announce Type: new Abstract: This study proposes a Quantum Fourier Transform (QFT)-enhanced quantum kernel for short-term time-series forecasting. Each signal is windowed, amplitude-encoded, transformed by a QFT, then passed through a protective rotation layer to avoid the QFT/QFT adjoint cancellation; the resulting kernel is used…

  • QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design

    QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design arXiv:2410.07961v2 Announce Type: cross Abstract: Quantum computing is an emerging field recognized for the significant speedup it offers over classical computing through quantum algorithms. However, designing and implementing quantum algorithms pose challenges due to the complex nature of quantum mechanics and the necessity for precise control…

  • Why Should We Bother with Quantum Computing in ML?

    Why Should We Bother with Quantum Computing in ML? Quantum Machine Learning principles The post Why Should We Bother with Quantum Computing in ML? appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications

    Instance-Optimal Matrix Multiplicative Weight Update and Its Quantum Applications arXiv:2509.08911v1 Announce Type: cross Abstract: The Matrix Multiplicative Weight Update (MMWU) is a seminal online learning algorithm with numerous applications. Applied to the matrix version of the Learning from Expert Advice (LEA) problem on the $d$-dimensional spectraplex, it is well known that MMWU achieves the minimax-optimal…

  • Quantum-inspired probability metrics define a complete, universal space for statistical learning

    Quantum-inspired probability metrics define a complete, universal space for statistical learning arXiv:2508.21086v1 Announce Type: new Abstract: Comparing probability distributions is a core challenge across the natural, social, and computational sciences. Existing methods, such as Maximum Mean Discrepancy (MMD), struggle in high-dimensional and non-compact domains. Here we introduce quantum probability metrics (QPMs), derived by embedding probability…

  • DO-EM: Density Operator Expectation Maximization

    DO-EM: Density Operator Expectation Maximization arXiv:2507.22786v1 Announce Type: cross Abstract: Density operators, quantum generalizations of probability distributions, are gaining prominence in machine learning due to their foundational role in quantum computing. Generative modeling based on density operator models (textbf{DOMs}) is an emerging field, but existing training algorithms — such as those for the Quantum Boltzmann…

  • Should Data Scientists Care About Quantum Computing?

    Should Data Scientists Care About Quantum Computing? I am sure the quantum hype has reached every person in tech (and outside it, most probably). With some over-the-top claims, like “some company has proved quantum supremacy,” “the quantum revolution is here,” or my favorite, “quantum computers are here, and it will make classical computers obsolete.” I…

  • Quantum Reservoir Computing and Risk Bounds

    Quantum Reservoir Computing and Risk Bounds arXiv:2501.08640v1 Announce Type: cross Abstract: We propose a way to bound the generalisation errors of several classes of quantum reservoirs using the Rademacher complexity. We give specific, parameter-dependent bounds for two particular quantum reservoir classes. We analyse how the generalisation bounds scale with growing numbers of qubits. Applying our…

  • The State of Quantum Computing: Where Are We Today?

    The State of Quantum Computing: Where Are We Today? And what we need to overcome Continue reading on Towards Data Science » Sara A. Metwalli Go to original source