arXiv Machine Learning
Jul 31
Quantum Speedups for Stochastic Optimization with Heavy-Tailed Noise
arXiv:2607. 25492v2 Announce Type: replace Abstract: We study stochastic optimization with heavy-tailed gradient noise.
Provable Quantum--Classical Separation for Continuous Gibbs Sampling
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arXiv Machine Learning
Sep 10
Smoothed Picard Hamiltonian Monte Carlo
arXiv:2609.06906v1 Announce Type: cross Abstract: We develop a new low-accuracy sampler, called \emph{smoothed Picard Hamiltonian Monte Carlo}, which combines Gaussian smoothing, Picard iteration, an...
arXiv Machine Learning
Sep 10
Hierarchical Fourier Approximation for Variational Quantum Distribution Learning
arXiv:2609.06307v1 Announce Type: cross Abstract: We study variational quantum distribution learning through a hierarchy of Walsh--Fourier approximations on the Boolean cube. At each level, a selecte...
arXiv Machine Learning
Sep 10
Accelerated High-Accuracy Sampling from a Warm Start via the Proximal Bouncy Particle Sampler
arXiv:2609.06905v1 Announce Type: cross Abstract: We study the problem of sampling from $\mu(\mathrm{d}x)\propto e^{-V(x)}\,\mathrm{d}x$ on $\mathbb{R}^d$, where $V$ is $\alpha$-strongly convex and $...
arXiv Machine Learning
Sep 10
Tight Lower Bounds for State Tomography with Limited Entanglement
arXiv:2609.05718v1 Announce Type: cross Abstract: We study state tomography when each measurement acts on at most $k$ fresh copies and no quantum memory is retained between blocks. We prove a lower b...
arXiv Machine Learning
Jun 18
How fast can you find a good hypothesis?
arXiv:2509. 03734v3 Announce Type: replace-cross Abstract: In the hypothesis selection problem, we are given sample and query access to finite set of candidate distributions (hypotheses), $\mathcal{H} = \{H_1, \ldots, H_n\}$, and samples from an unknown distribution $P$, both over a domain $\mathcal{X}$.
arXiv Machine Learning
Sep 3
Quantum Speedups for Sampling and Non-convex Optimization with Stochastic Oracles
arXiv:2504. 03626v2 Announce Type: replace-cross Abstract: We present quantum speedups for sampling from distributions of the form $\pi\propto e^{-f}$ on $\mathbb{R}^d$.
arXiv Machine Learning
Jul 14
Depth-Efficient Quantum Topological Data Analysis for Regime-Specific Detection of Financial Stress
arXiv:2607. 09906v1 Announce Type: cross Abstract: We present, to our knowledge, the first adaptation of Pauli Correlation Encoding (PCE) to quantum topological data analysis, reformulating Betti number estimation as a depth-efficient variational optimization over a compressed qubit register.
arXiv Machine Learning
Jun 30
Learning the structure of open quantum systems
arXiv:2606. 30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degree of the interaction graph.
arXiv Machine Learning
Jul 31
Windowed thinning and query complexity for the bouncy particle and Zigzag samplers
arXiv:2607. 28413v1 Announce Type: cross Abstract: Let $\mu(d x)\propto e^{-U(x)} d x$ on $\R^d$, where $U$ is $m$-strongly convex and $L$-smooth, and denote by $\kappa=L/m$ the condition number.
arXiv AI
Aug 5
Separating quantum circuits from classical LLMs
arXiv:2608. 03962v1 Announce Type: cross Abstract: Modern large language models - transformers and diffusion language models - are built around two canonical algorithmic tasks: prediction and generation.
arXiv Machine Learning
3d ago
Learning CNF Formulas from Uniform Random Solutions: Near-Tight Sample Complexity for Valiant's Algorithm
arXiv:2609.15268v1 Announce Type: new Abstract: We revisit Valiant's algorithm (Commun. ACM'84) for learning $n$-variable CNF formulas with clause size $k$ and variable degree $d$ from i.i.d. uniform...