Biology and medicine in the landscape of quantum advantages
Quantum computing holds substantial potential for applications in biology and medicine,
spanning from the simulation of biomolecules to machine learning methods for subty** …
spanning from the simulation of biomolecules to machine learning methods for subty** …
High-dimensional multi-fidelity Bayesian optimization for quantum control
We present the first multi-fidelity Bayesian optimization (BO) approach for solving inverse
problems in the quantum control of prototypical quantum systems. Our approach …
problems in the quantum control of prototypical quantum systems. Our approach …
Distributionally robust variational quantum algorithms with shifted noise
Given their potential to demonstrate near-term quantum advantage, variational quantum
algorithms (VQAs) have been extensively studied. Although numerous techniques have …
algorithms (VQAs) have been extensively studied. Although numerous techniques have …
Stochastic noise can be helpful for variational quantum algorithms
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In
notions of classical machine learning, they are avoided for example by means of stochastic …
notions of classical machine learning, they are avoided for example by means of stochastic …
Quantum conformal prediction for reliable uncertainty quantification in quantum machine learning
Quantum machine learning is a promising programming paradigm for the optimization of
quantum algorithms in the current era of noisy intermediate-scale quantum computers. A …
quantum algorithms in the current era of noisy intermediate-scale quantum computers. A …
Artificial Intelligence for Quantum Computing
Artificial intelligence (AI) advancements over the past few years have had an unprecedented
and revolutionary impact across everyday application areas. Its significance also extends to …
and revolutionary impact across everyday application areas. Its significance also extends to …
SantaQlaus: A resource-efficient method to leverage quantum shot-noise for optimization of variational quantum algorithms
We introduce SantaQlaus, a resource-efficient optimization algorithm tailored for variational
quantum algorithms (VQAs), including applications in the variational quantum eigensolver …
quantum algorithms (VQAs), including applications in the variational quantum eigensolver …
Deep Quantum Neural Networks are Gaussian Process
A Rad - arxiv preprint arxiv:2305.12664, 2023 - arxiv.org
The overparameterization of variational quantum circuits, as a model of Quantum Neural
Networks (QNN), not only improves their trainability but also serves as a method for …
Networks (QNN), not only improves their trainability but also serves as a method for …
Enhancing qubit readout with Bayesian learning
We introduce an efficient and accurate readout measurement scheme for single and
multiqubit states. Our method uses Bayesian inference to build an assignment probability …
multiqubit states. Our method uses Bayesian inference to build an assignment probability …
Generalized Bayesian likelihood-free inference
Generalized Bayesianlikelihood-free inference Page 1 Electronic Journal of Statistics Vol. 18
(2024) 3628–3686 ISSN: 1935-7524 https://doi.org/10.1214/24-EJS2283 Generalized …
(2024) 3628–3686 ISSN: 1935-7524 https://doi.org/10.1214/24-EJS2283 Generalized …