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Found 9 papers, 2 papers with code
Online Learning Quantum States with the Logarithmic Loss via VB-FTRL
Online learning quantum states with the logarithmic loss (LL-OLQS) is a quantum generalization of online portfolio selection, a classic open problem in the field of online learning for over three decades.
Fast Minimization of Expected Logarithmic Loss via Stochastic Dual Averaging
For the Poisson inverse problem, our algorithm attains an $\varepsilon$-optimal solution in $\smash{\tilde{O}}(d^2/\varepsilon^2)$ time, matching the state of the art, where $d$ denotes the dimension.
Faster Stochastic First-Order Method for Maximum-Likelihood Quantum State Tomography
In maximum-likelihood quantum state tomography, both the sample size and dimension grow exponentially with the number of qubits.
Online Self-Concordant and Relatively Smooth Minimization, With Applications to Online Portfolio Selection and Learning Quantum States
For online portfolio selection, the regret of online mirror descent with the logarithmic barrier is $\tilde{O}(\sqrt{T d})$.
Maximum-Likelihood Quantum State Tomography by Soft-Bayes
Quantum state tomography (QST), the task of estimating an unknown quantum state given measurement outcomes, is essential to building reliable quantum computing devices.
Learning-Based Compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns.
Learning Data Triage: Linear Decoding Works for Compressive MRI
The standard approach to compressive sampling considers recovering an unknown deterministic signal with certain known structure, and designing the sub-sampling pattern and recovery algorithm based on the known structure.
Learning-based Compressive Subsampling
In this paper, we instead take a principled learning-based approach in which a \emph{fixed} index set is chosen based on a set of training signals $\mathbf{x}_1,\dotsc,\mathbf{x}_m$.
Composite convex minimization involving self-concordant-like cost functions
The self-concordant-like property of a smooth convex function is a new analytical structure that generalizes the self-concordant notion.
