Search
Search Results
-
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...
-
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’...
-
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...
-
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...
-
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...
-
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,...
-
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...
-
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....
-
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)...
-
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...
-
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...
-
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...
-
Learning quantum systems
The future development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in...
-
Interplay Between Classical and Quantum Probability
This chapter presents the motivation for employing quantum probability (QP), instead of classical probability (CP), the mathematical modeling in...
-
A machine learning approach to Bayesian parameter estimation
Bayesian estimation is a powerful theoretical paradigm for the operation of the approach to parameter estimation. However, the Bayesian method for...
-
Quantum approximate optimization via learning-based adaptive optimization
Combinatorial optimization problems are ubiquitous and computationally hard to solve in general. Quantum approximate optimization algorithm (QAOA),...
-
Bayesian Estimation of Laser Linewidth From Delayed Self-Heterodyne Measurements
We present a statistical inference approach to estimate the frequency noise characteristics of ultra-narrow linewidth lasers from delayed...
-
Valid and efficient entanglement verification with finite copies of a quantum state
Detecting entanglement in multipartite quantum states is an inherently probabilistic process, typically with a few measured samples. The level of...
-
Classical Versus Quantum Rationality
The Savage Sure thing principle is the starting point of the discussion on CP versus QP-based notions of rationality. The problem of rationality is...
-
Interpretable deep learning models for the inference and classification of LHC data
The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative...