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Quantum Bayesian inference for parameter estimation using quantum generative model
We present a quantum Bayesian inference method for model parameter estimation that uses a quantum generative model under the given training data. The...
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Evaluation of Hybrid Quantum Approximate Inference Methods on Bayesian Networks
Bayesian networks are a type of probabilistic graphical model widely used to characterize various real-world problem scenarios due to their ability...
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A quantum Bayes’ rule and related inference
A quantum analogue of Bayesian inference is considered here. Quantum state-update rule associated with instrument is elected as a quantum Bayes’...
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An Optimized Quantum Circuit Representation of Bayesian Networks
In recent years, there has been a significant upsurge in the interest surrounding Quantum machine learning, with researchers actively develo**...
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Quantum Gaussian process regression for Bayesian optimization
Gaussian process regression is a well-established Bayesian machine learning method. We propose a new approach to Gaussian process regression using...
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5 Unknown Quantum States and Operations,a Bayesian View
The classical de Finetti theorem provides an operational definition of the concept of an unknown probability in Bayesian probability theory, where...
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An efficient adaptive MCMC algorithm for Pseudo-Bayesian quantum tomography
We revisit the Pseudo-Bayesian approach to the problem of estimating density matrix in quantum state tomography in this paper. Pseudo-Bayesian...
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Bayesian inference for form-factor fits regulated by unitarity and analyticity
We propose a model-independent framework for fitting hadronic form-factor data, which is often only available at discrete kinematical points, using...
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A Bayesian-network-based quantum procedure for failure risk analysis
Studying the propagation of failure probabilities in interconnected systems such as electrical distribution networks is traditionally performed by...
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Quantum approximate optimization algorithm for Bayesian network structure learning
Bayesian network structure learning is an NP-hard problem that has been faced by a number of traditional approaches in recent decades. Currently,...
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Empirical optimization of molecular simulation force fields by Bayesian inference
AbstractThe demands on the accuracy of force fields for classical molecular dynamics simulations are steadily growing as larger and more complex...
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Information-Theoretic Interpretation of Quantum Formalism
We present an information-theoretic interpretation of quantum formalism based on a Bayesian framework and devoid of any extra axiom or principle....
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Quantum Bayesian Decision-Making
As a compact representation of joint probability distributions over a dependence graph of random variables, and a tool for modelling and reasoning in...
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Quantum approximate Bayesian computation for NMR model inference
Recent technological advances may lead to the development of small-scale quantum computers that are capable of solving problems that cannot be...
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Bayesian uncertainty quantification of perturbative QCD input to the neutron-star equation of state
The equation of state of neutron-star cores can be constrained by requiring a consistent connection to the perturbative Quantum Chromodynamics (QCD)...
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Introduction to Probability and Inference
Probability is a fundamental concept in physics because the outcome of experiments is determined by random processes. Different approaches to...
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Extending the Bayesian Framework from Information to Action
In this review, we examine an extended Bayesian inference method and its relation to biological information processing. We discuss the idea of...
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Applying Bayesian inference and deterministic anisotropy to retrieve the molecular structure ∣Ψ(R)∣2 distribution from gas-phase diffraction experiments
Currently, our general approach to retrieving molecular structures from ultrafast gas-phase diffraction heavily relies on complex ab initio...
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A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination
Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf...
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Learning quantum systems
The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in...
