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Found 5 papers, 2 papers with code
An invitation to the sample complexity of quantum hypothesis testing
By making use of the wealth of knowledge that already exists in the literature on QHT, we characterize the sample complexity of binary QHT in the symmetric and asymmetric settings, and we provide bounds on the sample complexity of multiple QHT.
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 $\tilde{O}(\sqrt{T d})$.
The Learnability of Unknown Quantum Measurements
Quantum machine learning has received significant attention in recent years, and promising progress has been made in the development of quantum algorithms to speed up traditional machine learning tasks.
