mailitics

Category: quant-ph

  • 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…

  • 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…

  • The Observational Partial Order of Causal Structures with Latent Variables

    The Observational Partial Order of Causal Structures with Latent Variables arXiv:2502.07891v1 Announce Type: new Abstract: For two causal structures with the same set of visible variables, one is said to observationally dominate the other if the set of distributions over the visible variables realizable by the first contains the set of distributions over the visible…

  • Ensuring superior learning outcomes and data security for authorized learner

    Ensuring superior learning outcomes and data security for authorized learner arXiv:2501.00754v1 Announce Type: new Abstract: The learner’s ability to generate a hypothesis that closely approximates the target function is crucial in machine learning. Achieving this requires sufficient data; however, unauthorized access by an eavesdropping learner can lead to security risks. Thus, it is important to…

  • Using matrix-product states for time-series machine learning

    Using matrix-product states for time-series machine learning arXiv:2412.15826v1 Announce Type: new Abstract: Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has…