physics

Machine learning in physics

Machine learning in physics is a physics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Machine learning in physics rather than just read about it. In short: Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement.

Key takeaways

Machine learning in physics belongs to physics; place it in that map before memorising details.
Learn the definition first, then one example that makes the definition concrete.
Connect Machine learning in physics to a quantity you can measure, compute or draw — that is where exam questions come from.
Reproduce the core statement of Machine learning in physics from memory before moving on to harder problems.

Reference excerpt

Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. Other examples include learning Hamiltonians,, detecting phase transition in spin-systems even when not trained on physical configurations near criticality, learning quantum phase transitions, and automatically generating new quantum experiments. ML is effective at processing large amounts of experimental or calculated data in order to characterize an unknown quantum system, making its application useful in contexts including quantum information theory, quantum technology development, and computational materials design. In this context, for example, it can be used as a tool to interpolate pre-calculated interatomic potentials, or directly solving the Schrödinger equation with a variational method.

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. Improving the extraction accuracy of physical observables from absorption images of ultracold atoms (degenerate Fermi gas), by the generation of an ideal reference frame.

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.

Sign problem Machine learning techniques can be used to find a better manifold of integration for path integrals in order to avoid the sign problem.

Fluid dynamics

Physics discovery and prediction

A deep learning system was reported to learn intuitive physics from visual data (of virtual 3D environments) based on an unpublished approach inspired by studies of visual cognition in infants. Other researchers have developed a machine learning algorithm that could discover sets of basic variables of various physical systems and predict the systems' future dynamics from video recordings of their behavior. In the future, it may be possible that such can be used to automate the discovery of physical laws of complex systems. Beyond discovery and prediction, "blank slate"-type of learning of fundamental aspects of the physical world may have further applications such as improving adaptive and broad artificial general intelligence. In specific, prior machine learning models were "highly specialised and lack a general understanding of the world".

Physics-informed neural networks

See also Quantum computing Quantum machine learning Quantum annealing Quantum neural network HHL Algorithm Comparison of machine learning software

References

Illustrations

Worked examples

Example 1 — a first encounter with Machine learning in physics

Start with the simplest possible case. Write down what Machine learning in physics claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In physics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Machine learning in physics before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Machine learning in physics ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Machine learning in physics

In research: Machine learning in physics appears in physics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry: Engineering practice reuses Machine learning in physics in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom: Machine learning in physics is common in secondary-school and first-year university syllabi. It links to neighbouring topics Machine learning, Quantum information science, Quantum programming, so understanding it makes those chapters shorter.
In everyday life: Look for Machine learning in physics outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.

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How to study Machine learning in physics in 20 minutes

Read the reference excerpt below once, without taking notes.
Close the page and write down what Machine learning in physics means in your own words.
Compare your version with the excerpt and mark what you missed.
Work through the three examples above with pen and paper.
Explain Machine learning in physics out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Machine learning in physics in simple terms?

Why does Machine learning in physics matter?

Because it connects several physics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Machine learning in physics?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Machine learning in physics.