Glossary¶
- Bayes factor¶
Statistical measure of performance between two models at explaining the same dataset
- BF¶
See also
- EDH¶
- ES¶
See also
- ET¶
See also
- Experiment Design Heuristic¶
Mechanism to design informative experiments to perform upon the system from which the model training can learn. Defined in Experiment design heuristic.
- Exploration Strategy¶
The mechanism by which a tree grows, specifying new models to consider, when to stop considering new models, how to remove models, etc.
See also
Defined in Exploration Strategy.
- Exploration Tree¶
Unique tree associated with an individual Exploration Strategy.
See also
Defined in Structure.
- global champion¶
Single model favoured by Quantum Model Learning Agent as the strongest candidate to represent the system.
- Instance¶
A single implementation of QMLA.
See also
Defined in Structure.
- Probe¶
Input state evolved by both the candidate model and system to draw comparisons between them. Defined in Probes.
- QHL¶
See also
- QLE¶
See also
- QMLA¶
See also
- Quantum Hamiltonian Learning¶
Algorithm for learning the parameters of a given model.
- Quantum Likelihood Estimation¶
Algorithm used to perform Bayesian inference during QHL
- Quantum Model Learning Agent¶
Algorithm/framework for finding model of quantum system.
- Run¶
A collection of instance s. Note that for a run, all instances must target the same system .
See also
Defined in Structure.
- Run Results Directory¶
Directory to which results are stored for an individual run. Consists of results files for all the instance s in the run, as well as analyses on the model, instance and run levels.
- System¶
The target system, i.e. underlying model. In simulation, this is used to generate the expectation values against which likelihood estimation occurs. In experiments, the form of the system is unknown, but data obtained from experiments are used in the likelihood estimation instead.
- True Model¶
See also