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Quasiprobability distribution

Quasiprobability distribution is a mathematics 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 Quasiprobability distribution rather than just read about it. In short: A quasiprobability distribution is a mathematical object similar to a probability distribution but which relaxes some of Kolmogorov's axioms of probability theory. Quasiprobability distributions arise naturally in the study of quantum mechanics when treated in phase space formulation, commonly used in quantum optics, time-frequency analysis, and elsewhere.

Key takeaways

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

Reference excerpt

A quasiprobability distribution is a mathematical object similar to a probability distribution but which relaxes some of Kolmogorov's axioms of probability theory. Quasiprobability distributions arise naturally in the study of quantum mechanics when treated in phase space formulation, commonly used in quantum optics, time-frequency analysis, and elsewhere. Quasiprobabilities share several of general features with ordinary probabilities, such as, crucially, the ability to yield expectation values with respect to the weights of the distribution. However, they can violate the σ-additivity axiom: integrating over them does not necessarily yield probabilities of mutually exclusive states. Quasiprobability distributions also have regions of negative probability density, counterintuitively, contradicting the first axiom.

Introduction

In the most general form, the dynamics of a quantum-mechanical system are determined by a master equation in Hilbert space: an equation of motion for the density operator (usually written ρ ^ {\displaystyle {\widehat {\rho }}} ) of the system. The density operator is defined with respect to a complete orthonormal basis. Although it is possible to directly integrate this equation for very small systems (i.e., systems with few particles or degrees of freedom), this quickly becomes intractable for larger systems. However, it is possible to prove that the density operator can always be written in a diagonal form, provided that it is with respect to an overcomplete basis. When the density operator is represented in such an overcomplete basis, then it can be written in a manner more resembling of an ordinary function, at the expense that the function has the features of a quasiprobability distribution. The evolution of the system is then completely determined by the evolution of the quasiprobability distribution function. The coherent states, i.e. right eigenstates of the annihilation operator a ^ {\displaystyle {\widehat {a}}} serve as the overcomplete basis in the construction described above. By definition, the coherent states have the following property,

a ^ | α ⟩ = α | α ⟩ ⟨ α | a ^ † = ⟨ α | α ∗ . {\displaystyle {\begin{aligned}{\widehat {a}}|\alpha \rangle &=\alpha |\alpha \rangle \\[1ex]\langle \alpha |{\widehat {a}}^{\dagger }&=\langle \alpha |\alpha ^{*}.\end{aligned}}}

They also have some further interesting properties. For example, no two coherent states are orthogonal. In fact, if |α⟩ and |β⟩ are a pair of coherent states, then

⟨ β ∣ α ⟩ = e − 1 2 ( | β | 2 + | α | 2 − 2 β ∗ α ) ≠ δ ( α − β ) . {\displaystyle \langle \beta \mid \alpha \rangle =e^{-{\frac {1}{2}}(\left|\beta \right|^{2}+\left|\alpha \right|^{2}-2\beta ^{*}\alpha )}\neq \delta (\alpha -\beta ).}

Note that these states are, however, correctly normalized with ⟨α|α⟩ = 1. Owing to the completeness of the basis of Fock states, the choice of the basis of coherent states must be overcomplete. Click to show an informal proof.

In the coherent states basis, however, it is always possible to express the density operator in the diagonal form

ρ ^ = ∫ f ( α , α ∗ ) | α ⟩ ⟨ α | d 2 α {\displaystyle {\widehat {\rho }}=\int f(\alpha ,\alpha ^{*})|\alpha \rangle \langle \alpha |\,d^{2}\alpha }

where f is a representation of the phase space distribution. This function f is considered a quasiprobability density because it has the following properties:

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Quasiprobability distribution

Start with the simplest possible case. Write down what Quasiprobability distribution claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, 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 Quasiprobability distribution 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 Quasiprobability distribution 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 Quasiprobability distribution

In research
Quasiprobability distribution appears in mathematics 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 Quasiprobability distribution 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
Quasiprobability distribution is common in secondary-school and first-year university syllabi. It links to neighbouring topics Exotic probabilities, Particle distributions, Quantum optics, so understanding it makes those chapters shorter.
In everyday life
Look for Quasiprobability distribution 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 Quasiprobability distribution in 20 minutes

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

Frequently asked questions

What is Quasiprobability distribution in simple terms?

A quasiprobability distribution is a mathematical object similar to a probability distribution but which relaxes some of Kolmogorov's axioms of probability theory. Quasiprobability distributions arise naturally in the study of quantum mechanics when treated in phase space formulation, commonly used…

Why does Quasiprobability distribution matter?

Because it connects several mathematics 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 Quasiprobability distribution?

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 Quasiprobability distribution.

Tags

  • Exotic probabilities
  • Particle distributions
  • Quantum optics

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