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Joint quantum entropy

Joint quantum entropy 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 Joint quantum entropy rather than just read about it. In short: The joint quantum entropy generalizes the classical joint entropy to the context of quantum information theory. Intuitively, given two quantum states ρ {\displaystyle \rho } and σ {\displaystyle \sigma } , represented as density operators that are subparts of a quantum system, the joint quantum entropy is a measure of the total uncertainty or entropy of the joint system.

Key takeaways

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

Reference excerpt

The joint quantum entropy generalizes the classical joint entropy to the context of quantum information theory. Intuitively, given two quantum states ρ {\displaystyle \rho } and σ {\displaystyle \sigma } , represented as density operators that are subparts of a quantum system, the joint quantum entropy is a measure of the total uncertainty or entropy of the joint system. It is written S ( ρ , σ ) {\displaystyle S(\rho ,\sigma )} or H ( ρ , σ ) {\displaystyle H(\rho ,\sigma )} , depending on the notation being used for the von Neumann entropy. Like other entropies, the joint quantum entropy is measured in bits, i.e. the logarithm is taken in base 2. In this article, we will use S ( ρ , σ ) {\displaystyle S(\rho ,\sigma )} for the joint quantum entropy.

Background In information theory, for any classical random variable X {\displaystyle X} , the classical Shannon entropy H ( X ) {\displaystyle H(X)} is a measure of how uncertain we are about the outcome of X {\displaystyle X} . For example, if X {\displaystyle X} is a probability distribution concentrated at one point, the outcome of X {\displaystyle X} is certain and therefore its entropy H ( X ) = 0 {\displaystyle H(X)=0} . At the other extreme, if X {\displaystyle X} is the uniform probability distribution with n {\displaystyle n} possible values, intuitively one would expect X {\displaystyle X} is associated with the most uncertainty. Indeed, such uniform probability distributions have maximum possible entropy H ( X ) = log 2 ⁡ ( n ) {\displaystyle H(X)=\log _{2}(n)} . In quantum information theory, the notion of entropy is extended from probability distributions to quantum states, or density matrices. For a state ρ {\displaystyle \rho } , the von Neumann entropy is defined by

− Tr ⁡ ρ log ⁡ ρ . {\displaystyle -\operatorname {Tr} \rho \log \rho .}

Applying the spectral theorem, or Borel functional calculus for infinite dimensional systems, we see that it generalizes the classical entropy. The physical meaning remains the same. A maximally mixed state, the quantum analog of the uniform probability distribution, has maximum von Neumann entropy. On the other hand, a pure state, or a rank one projection, will have zero von Neumann entropy. We write the von Neumann entropy S ( ρ ) {\displaystyle S(\rho )} (or sometimes H ( ρ ) {\displaystyle H(\rho )} .

Definition Given a quantum system with two subsystems A and B, the term joint quantum entropy simply refers to the von Neumann entropy of the combined system. This is to distinguish from the entropy of the subsystems. In symbols, if the combined system is in state ρ A B {\displaystyle \rho ^{AB}} , the joint quantum entropy is then

S ( ρ A , ρ B ) = S ( ρ A B ) = − Tr ⁡ ( ρ A B log ⁡ ( ρ A B ) ) . {\displaystyle S(\rho ^{A},\rho ^{B})=S(\rho ^{AB})=-\operatorname {Tr} (\rho ^{AB}\log(\rho ^{AB})).}

Each subsystem has its own entropy. The state of the subsystems are given by the partial trace operation.

Properties The classical joint entropy is always at least equal to the entropy of each individual system. This is not the case for the joint quantum entropy. If the quantum state ρ A B {\displaystyle \rho ^{AB}} exhibits quantum entanglement, then the entropy of each subsystem may be larger than the joint entropy. This is equivalent to the fact that the conditional quantum entropy may be negative, while the classical conditional entropy may never be. Consider a maximally entangled state such as a Bell state. If ρ A B {\displaystyle \rho ^{AB}} is a Bell state, say,

| Ψ ⟩ = 1 2 ( | 00 ⟩ + | 11 ⟩ ) , {\displaystyle \left|\Psi \right\rangle ={\frac {1}{\sqrt {2}}}\left(|00\rangle +|11\rangle \right),}

… excerpt ends here. Continue reading the full article.

Worked examples

Example 1 — a first encounter with Joint quantum entropy

Start with the simplest possible case. Write down what Joint quantum entropy 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 Joint quantum entropy 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 Joint quantum entropy 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 Joint quantum entropy

In research
Joint quantum entropy 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 Joint quantum entropy 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
Joint quantum entropy is common in secondary-school and first-year university syllabi. It links to neighbouring topics Quantum information theory, Quantum mechanical entropy, so understanding it makes those chapters shorter.
In everyday life
Look for Joint quantum entropy 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 Joint quantum entropy in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Joint quantum entropy 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 Joint quantum entropy out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Joint quantum entropy in simple terms?

The joint quantum entropy generalizes the classical joint entropy to the context of quantum information theory. Intuitively, given two quantum states ρ {\displaystyle \rho } and σ {\displaystyle \sigma } , represented as density operators that are subparts of a quantum system, the joint quantum ent…

Why does Joint quantum entropy 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 Joint quantum entropy?

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 Joint quantum entropy.

Tags

  • Quantum information theory
  • Quantum mechanical entropy

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