User:Fawly/Machine learning in physics

The term quantum machine learning is also used for approaches that apply classical methods of machine learning to the study of quantum systems. A prime example is the use of classical learning techniques to process large amounts of experimental or calculated (for example by solving Schrodinger's equation data in order to characterize an unknown quantum system (for instance in the context of quantum information theory and for the development of quantum technologies or computational materials design), but there are also more exotic applications.

Noisy data

The ability to experimentally control and prepare increasingly complex quantum systems brings with it a growing need to turn large and noisy data sets into meaningful information. This is a problem that has already been studied extensively in the classical setting, and consequently, many existing machine learning techniques can be naturally adapted to more efficiently address experimentally relevant problems. For example, Bayesian methods and concepts of algorithmic learning can be fruitfully applied to tackle quantum state classification, Hamiltonian learning, and the characterization of an unknown unitary transformation. Other problems that have been addressed with this approach are given in the following list:

Identifying an accurate model for the dynamics of a quantum system, through the reconstruction of the Hamiltonian; Extracting information on unknown states; Learning unknown unitary transformations and measurements; Engineering of quantum gates from qubit networks with pairwise interactions, using time dependent or independent Hamiltonians. Calculated and noise-free data

Quantum machine learning can also be applied to dramatically accelerate the prediction of quantum properties of molecules and materials. This can be helpful for the computational design of new molecules or materials. Some examples include

Interpolating interatomic potentials; Inferring molecular atomization energies throughout chemical compound space; Accurate potential energy surfaces with restricted Boltzmann machines; Automatic generation of new quantum experiments; Solving the many-body, static and time-dependent Schrödinger equation; Identifying phase transitions from entanglement spectra; Generating adaptive feedback schemes for quantum metrology and quantum tomography. Variational Circuits

Variational circuits are a family of algorithms which utilize training based on circuit parameters and an objective function. Variational circuits are generally composed of a classical device communicating input parameters (random or pre-trained parameters) into a quantum device, along with a classical Mathematical optimization function. These circuits are very heavily dependent on the architecture of the proposed quantum device because parameter adjustments are adjusted based solely on the classical components within the device. Though the application is considerably infantile in the field of quantum machine learning, it has incredibly high promise for more efficiently generating efficient optimization functions.