Tootfinder

A Note on Bayesian Networks with Latent Root Variables
Marco Zaffalon, Alessandro Antonucci
https://arxiv.org/abs/2402.17087 https://…

A Note on Bayesian Networks with Latent Root Variables
We characterise the likelihood function computed from a Bayesian network with latent variables as root nodes. We show that the marginal distribution over the remaining, manifest, variables also factorises as a Bayesian network, which we call empirical. A dataset of observations of the manifest variables allows us to quantify the parameters of the empirical Bayesian net. We prove that (i) the likelihood of such a dataset from the original Bayesian network is dominated by the global maximum of th…

Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Sean Chen, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh
https://arxiv.org/abs/2402.16465

Training Classical Neural Networks by Quantum Machine Learning
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, …

This https://arxiv.org/abs/2402.11101 has been replaced.
initial toot: https://mastoxiv.page/@a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Jonathan Lindbloom, Jan Glaubitz, Anne Gelb
https://arxiv.org/abs/2402.16623

Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown which is augmented by a generalized gamma hyper-prior model for variance hyper-parameters. This investigation generalizes these models and their efficient maximum a posterior (MAP) estimation using the iterative alternating sequential (IAS) algorithm in two ways: (1) General sparsifying transf…

This https://arxiv.org/abs/2301.04863 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Choosing observation operators to mitigate model error in Bayesian inverse problems
In statistical inference, a discrepancy between the parameter-to-observable map that generates the data and the parameter-to-observable map that is used for inference can lead to misspecified likelihoods and thus to incorrect estimates. In many inverse problems, the parameter-to-observable map is the composition of a linear state-to-observable map called an `observation operator' and a possibly nonlinear parameter-to-state map called the `model'. We consider such Bayesian inverse problems where…

Training Classical Neural Networks by Quantum Machine Learning
Chen-Yu Liu, En-Jui Kuo, Chu-Hsuan Abraham Lin, Sean Chen, Jason Gemsun Young, Yeong-Jar Chang, Min-Hsiu Hsieh
https://arxiv.org/abs/2402.16465

Training Classical Neural Networks by Quantum Machine Learning
In recent years, advanced deep neural networks have required a large number of parameters for training. Therefore, finding a method to reduce the number of parameters has become crucial for achieving efficient training. This work proposes a training scheme for classical neural networks (NNs) that utilizes the exponentially large Hilbert space of a quantum system. By mapping a classical NN with $M$ parameters to a quantum neural network (QNN) with $O(\text{polylog} (M))$ rotational gate angles, …

This https://arxiv.org/abs/2402.11101 has been replaced.
initial toot: https://mastoxiv.page/@a…

Physics-based material parameters extraction from perovskite experiments via Bayesian optimization
The ability to extract material parameters from quantitative experimental analysis is essential for rational design and theory advancement. However, the difficulty of this analysis increases significantly with the complexity of the theoretical model and the number of material parameters. Here we use Bayesian optimization to develop an analysis platform that can extract up to 8 fundamental material parameters of an organometallic perovskite semiconductor from a transient photoluminescence experi…

Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Jonathan Lindbloom, Jan Glaubitz, Anne Gelb
https://arxiv.org/abs/2402.16623

Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
Bayesian hierarchical models can provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models typically comprise a conditionally Gaussian prior model for the unknown which is augmented by a generalized gamma hyper-prior model for variance hyper-parameters. This investigation generalizes these models and their efficient maximum a posterior (MAP) estimation using the iterative alternating sequential (IAS) algorithm in two ways: (1) General sparsifying transf…

First-principles construction of symmetry-informed quantum metrologies
Jes\'us Rubio
https://arxiv.org/abs/2402.16410 https://arx…

First-principles construction of symmetry-informed quantum metrologies
Combining quantum and Bayesian principles leads to optimality in metrology, but exact solutions can be hard to find. This work mitigates this problem with a novel class of exactly solvable optimisation equations. For any quantity isomorphic to a location parameter, rules to devise optimal measurements are given in closed form. These are valid for any parameter range, prior information, or state, and the associated estimators apply to finite samples. This framework unifies the metrology of locat…

First-principles construction of symmetry-informed quantum metrologies
Jes\'us Rubio
https://arxiv.org/abs/2402.16410 https://arx…

First-principles construction of symmetry-informed quantum metrologies
Combining quantum and Bayesian principles leads to optimality in metrology, but exact solutions can be hard to find. This work mitigates this problem with a novel class of exactly solvable optimisation equations. For any quantity isomorphic to a location parameter, rules to devise optimal measurements are given in closed form. These are valid for any parameter range, prior information, or state, and the associated estimators apply to finite samples. This framework unifies the metrology of locat…