Quantum Joint, Conditional Entropy and Mutual Information
Shannon gave classical information theory three companion quantities — the joint entropy of two variables, the conditional entropy of one given the other, and the mutual information they share — glued together by chain rules and bounded above and below by inequalities everyone learns to trust. Quantum information theory keeps the algebra of those definitions but throws out the intuition. Replace probability distributions by a density operator on a tensor-product space , replace Shannon's by von Neumann's , and the same three quantities appear: the joint entropy , the conditional entropy , and the mutual information . What changes is everything about what they mean. The headline surprise is that conditional entropy can be negative. There is no conditional state from which is an average entropy, so the classical floor simply disappears: a maximally entangled pair has while , forcing . That negativity is not a bookkeeping artefact — it is a resource, equal to the coherent information that governs quantum channel capacity and to the entanglement a receiver can distil in state merging. Mutual information stays better behaved: it is a relative entropy, , and it climbs to , twice the classical ceiling. Underneath all of it sits one theorem — strong subadditivity — from which subadditivity, the triangle inequality, data processing, and the Holevo bound all descend.
- For a bipartite state the joint entropy is , the conditional entropy is , and the mutual information is — the algebra of Shannon's chain rules carried over verbatim.
- The defining quantum twist: can be negative. A maximally entangled pair on gives , whereas classical conditional entropy is always .
- Mutual information is a relative entropy, , zero iff , and it reaches — double the classical maximum of .
- Subadditivity and the Araki–Lieb triangle inequality bracket the joint entropy from both sides.
- Strong subadditivity — equivalently or — is the deepest fact, and is the data-processing inequality in disguise.
- Key idea: Conditional entropy can go negative — its negative is the coherent information , the basic quantum-channel resource — while mutual information measures all correlations and is governed by strong subadditivity.
Conditional entropy can be negative — its negative is the coherent information , the basic quantum-channel resource — while mutual information measures all correlations and is governed by strong subadditivity.
Where this lives today
These entropies are the standard measuring instruments of modern quantum information. In quantum channel coding, the Lloyd–Shor–Devetak theorem sets the quantum capacity equal to the regularized coherent information ; the search for its single-letter form drives the study of degradable and complementary channels, and superactivation — two zero-capacity channels with positive joint capacity — is a statement about the non-additivity of . In quantum key distribution, the duality is the security statement: if Bob's conditional entropy about the key is low, Eve's () is forced high, and the secret-key rate is written in these terms via the Devetak–Winter formula. In many-body physics and holography, across a boundary obeys area laws in gapped ground states; the Ryu–Takayanagi formula computes entanglement entropy geometrically, and strong subadditivity becomes a constraint on spacetime. Negative conditional entropy has been measured — photonic and NMR realizations of state merging demonstrate entanglement gain when . The open frontier is the capacity problem itself: no computable formula is known for the quantum capacity of a general channel, because no one has tamed the non-additivity of coherent information.
The physical picture
Start with the single fact that reorganizes everything: a pure entangled state is less uncertain as a whole than in either of its parts. If is pure, , yet tracing out leaves a mixed with . Classically, learning nothing about the whole while being uncertain about a part is impossible; for entanglement it is generic. The quantity that records it is the conditional entropy , driven below zero exactly when is entangled with . The first figure puts real numbers on the smallest case — a Bell pair — showing the conditional-entropy bar dipping under the axis while the mutual-information bar overshoots the classical ceiling.
The Bell pair is one point; the whole story is a curve. Take the one-parameter family of pure states and slide from a product state ( or ) to maximal entanglement (). Each marginal has spectrum , so , the binary entropy, while throughout. Mutual information and conditional entropy are perfect mirror images across the axis: everything the joint state hides below zero in reappears, doubled, as shared correlation in . The magnitude is the coherent information — the ebits a receiver holding could distil. The second figure traces both.
One theme underlies every proof below: purification simplifies. Any mixed is the marginal of a pure , and for a pure global state complementary parts share entropy, and — which is what turns the Araki–Lieb inequality and the duality into one-line consequences.
The core equations
The starting point is a definition: apply von Neumann's entropy to the whole and to the parts. The joint entropy is the entropy of the global density operator, computed from its eigenvalues as for any single system.
where: — density operator on ; — base-2 logarithm, so entropy is in bits (natural log gives nats); — trace over the whole space. Reading: is the eigenvalue entropy of the joint state, blind to the tensor split. The conditional entropy is defined by the chain rule — there is no averaged-state definition to fall back on — using the marginal . Keeping only this identity is what allows the quantity to go negative.
where: — partial trace over , producing the reduced state ; . Reading: is "the entropy adds once you already hold ", and for an entangled pair that increment is negative. Mutual information is the symmetric combination that survives from Shannon theory, equal both to the inclusion–exclusion form and to the marginal-minus-conditional form.
where: — total (classical plus quantum) correlation; with . Reading: it counts how much less uncertain the pair is than the sum of its parts. The single most useful re-expression identifies mutual information as a quantum relative entropy against the product reference; non-negativity and monotonicity then come for free.
where: is the quantum relative entropy. Reading: as a "distance" from to the product state, , zero exactly when . Non-negativity of is subadditivity; the matching lower bound on the joint entropy is the Araki–Lieb triangle inequality. Together they pin between two marginal-dependent walls.
where: the right inequality is subadditivity, the left is Araki–Lieb (proved via purification). Reading: equivalently — the conditional entropy is squeezed symmetrically about zero, not floored at it. The deepest relation is strong subadditivity, the Lieb–Ruskai theorem on a tripartite state ; everything structural in quantum Shannon theory is a corollary.
where: — entropy of the tripartite state; — entropy of the central marginal. Reading: equivalently and — discarding a subsystem never increases correlations, and conditioning on more never increases conditional entropy. The negative of conditional entropy earns its own name and its own operational life as the central rate in quantum channel coding.
where: — coherent information; the regularized maximum of over channel inputs is the quantum capacity (the LSD theorem). Reading: wherever there is positive coherent information, and quantum information can be sent. Finally, purification gives a duality with no classical analogue: the sign of conditional entropy is exclusive between the two complementary partners of a pure global state.
where: — pure state with . Reading: can have positive conditional entropy with at most one of — a monogamy-flavoured constraint that underlies the security of key distribution.
The goal is to prove, from one soft assumption — that quantum relative entropy is non-negative — that mutual information is non-negative and hence the joint entropy subadditive, . The engine is Klein's inequality, with equality iff , which follows from operator concavity of the logarithm. We compare the true state against the product of its own marginals, expand the relative entropy, and watch the marginal terms collapse into exactly , turning non-negativity of a distance into non-negativity of a correlation.
Step 1. Take the reference state , which has the same marginals as , and write out . Why: choosing the product reference isolates correlations — the two states differ only in how and are tied together, not in their local statistics.
Step 2. Split the tensor logarithm, , valid because and commute. Why: the logarithm of a product of commuting operators is the sum of logarithms, which is what lets the joint trace break into marginal pieces.
Step 3. Evaluate the cross terms by partial trace: , and likewise the term gives ; the first term is . Assemble: . Why: each cross term sees only the corresponding marginal, so the tripartite-looking expression reduces to the three ordinary entropies.
Step 4. Apply Klein: , i.e. , with equality iff . Why: a distance that vanishes only at coincidence turns "zero correlation" into "exact product state" — there is no residual hidden correlation, classical or quantum.
Go deeper · Derivation B · 1 minThe Araki–Lieb triangle inequality from purification
The aim is the lower wall on the joint entropy, , which has no classical counterpart (classically , a statement quantum entropy violates). The trick is to buy the lower bound from the upper one: purify to a pure state on a larger space, exploit that complementary parts of a pure state share entropy, and apply the subadditivity just proved to a chosen pair — the purifying system converts a triangle inequality into an instance of subadditivity.
Step 1. Introduce a reference and a purification with . For the global pure state the complementary-subsystem entropies match: and . Why: a pure bipartition has equal Schmidt spectra on both sides, so any region and its complement carry identical entropy — the equalities are free.
Step 2. Apply subadditivity to the pair : . Why: subadditivity was already established for any two systems, and are just such a pair inside the enlarged pure state.
Step 3. Substitute the pure-state equalities and to get , i.e. . Why: the substitution trades the auxiliary -entropies back for the physical joint and marginal entropies, leaving a bound purely in quantities.
Step 4. Repeat with to get , and combine: . Why: symmetry supplies the mirror bound, and the two together give the absolute value. *Equality holds, for example, for a pure state, where .*
Go deeper · Derivation C · 1 minStrong subadditivity is monotonicity of mutual information
The purpose is to show that the Lieb–Ruskai inequality , the conditional form , and "throwing away cannot increase " are one theorem written three ways — and that this common statement is exactly the data-processing inequality. We do not prove strong subadditivity from scratch (that needs Lieb's concavity theorem); we prove the equivalences that make it usable, expanding the two mutual informations on a tripartite state until the algebra reveals their difference is the Lieb–Ruskai defect.
Step 1. Expand and from the definitions on and its marginals. Why: both are ordinary mutual informations, one treating as a single system, so the same inclusion–exclusion formula applies.
Step 2. Subtract: , the terms cancelling. Why: the local entropy of appears in both and drops out, leaving a difference built only from entropies of regions containing .
Step 3. Recognize the right-hand side as the Lieb–Ruskai defect: is identical to . Why: the two inequalities have the same content term for term, so one holds iff the other does — they are logically the same statement.
Step 4. Rewrite through conditional entropy: subtracting differently gives , "conditioning on more never increases conditional entropy". Letting be the purifying environment of a channel acting on yields — the data-processing inequality. Why: identifying with a channel's environment turns "discard a subsystem" into "apply a quantum operation", so monotonicity under discarding becomes monotonicity under any processing.
Limiting cases
Evaluating the three quantities on canonical states fixes their range and exposes exactly where the classical intuition survives and where it breaks.
| Case | Result | Meaning |
|---|---|---|
| Product state | No correlation; conditional entropy equals the marginal, just as classically. The unique zero of mutual information. | |
| Maximally entangled on | Maximal negativity of and maximal — double the classical ceiling . | |
| Classically correlated | Separable "diagonal" states reproduce Shannon bounds: conditional entropy stays non-negative, mutual information capped at . | |
| General bipartite bounds | Araki–Lieb floor and subadditivity ceiling bracket the joint entropy; equivalently . |
Worked examples
1 · Conditional entropy and mutual information of an entangled pair (fully worked)
Take the pure state . Compute , , , , and the coherent information , first for the Bell case and then for .
Step 1. The global state is pure, so for every . This already guarantees and .
Step 2. Trace out : , spectrum , so .
Step 3. Bell case : bit, so . Then , , and . This saturates every quantum ceiling for two qubits: maximal and maximal negativity .
Step 4. Non-maximal case : . Hence , bit, , and .
Step 5. Operationally: in state merging a receiver holding absorbs using — sending no qubits and distilling ebits per copy ( ebit for the Bell pair, ebit at ), while is the total correlation to be paid for in bringing the shares together. Bell: , , ; at : , , (bits).
2 · The Holevo quantity of a two-state ensemble (fully worked)
Alice encodes a classical bit by sending either or with equal probability. Form the classical–quantum state and compute its mutual information — the Holevo quantity that bounds how much classical information a measurement of can recover.
Step 1. For a classical–quantum state the mutual information equals the Holevo , with average state . Why: the classical register has no coherences, so , , and the definition collapses to the Holevo form.
Step 2. Each signal state is pure, so and the subtracted term vanishes: .
Step 3. Build . Its eigenvalues solve , giving .
Step 4. Evaluate the entropy: .
Step 5. Interpret: even though Alice used two perfectly distinguishable labels, the non-orthogonal signals and (overlap ) let Bob extract at most bit of the bit sent — the Holevo bound in action, and a direct corollary of strong subadditivity. bit bit.
3 · A classically correlated state hits the classical ceiling
Compare the Bell pair with the separable state .
Step 1. The eigenvalues of are , so bit, and both marginals are maximally mixed, bit. Hence and — conditional entropy on the classical floor, mutual information on the classical ceiling.
Step 2. The Bell state has the same marginals but , driving to and to . Same local descriptions; entanglement alone doubles the correlation and pushes conditional entropy from to bit.
Physics of the portrait
The idea that "the entropy of a part can exceed the entropy of the whole" took decades to be seen as a resource rather than a paradox. It runs from von Neumann's 1932 definition of quantum entropy, through the hard analysis of Lieb and Ruskai, to a clean operational meaning only in this century.
Origins · 1 minquantum conditional entropy and how this was discovered
Quantum conditional entropy is a quantity whose very sign rewrote how physicists think about information. When John von Neumann introduced in 1932, he could not have anticipated that the naive chain-rule combination would ever be negative — classically it is an average that cannot be. The analytic backbone came in 1973, when Elliott Lieb and Mary Beth Ruskai proved strong subadditivity, supplying the inequality from which subadditivity, the Araki–Lieb bound, and monotonicity of relative entropy all follow; Lieb's concavity theorem for the trace function was the decisive lemma. Through the 1990s the operational meaning crystallized: Schumacher and Nielsen (1996) identified the coherent information as the quantity controlling quantum channel capacity, and in 1997 Cerf and Adami argued that negative conditional entropy is the signature of entanglement. The story closed in 2005 when Horodecki, Oppenheim and Winter proved the state-merging theorem: the cost of moving Alice's share to Bob is exactly qubits, and when it is negative the parties instead harvest that many ebits. A quantity that looked like a paradox became a currency.
Going further
Texts. Nielsen & Chuang, Quantum Computation and Quantum Information, ch. 11 (entropy and information) · Wilde, Quantum Information Theory, 2nd ed., ch. 11–13 (relative entropy, strong subadditivity, coherent information) · Preskill, Lecture Notes on Quantum Computation, ch. 10 · Watrous, The Theory of Quantum Information, ch. 5 · Kitaev, Shen & Vyalyi, Classical and Quantum Computation.
Primary sources. Lieb & Ruskai, J. Math. Phys. 14, 1938 (1973) — strong subadditivity · Cerf & Adami, Phys. Rev. Lett. 79, 5194 (1997) — negative conditional entropy · Horodecki, Oppenheim & Winter, Nature 436, 673 (2005) — quantum state merging · Devetak, IEEE Trans. Inf. Theory 51, 44 (2005) — coherent information as quantum capacity. The maximally entangled values , , , the pure-family curves and , and the classically correlated are standard results. The Holevo example bit follows directly from the eigenvalues of .
Prerequisites. Von Neumann Entropy · Shannon Entropy and Classical Information Measures · Quantum Relative Entropy. Leads to. Strong Subadditivity of Quantum Entropy · Data-Processing Inequality · Holevo Bound · Complementary Channels and Degradability.
Ask the tutor: Ask me why a maximally entangled state pushes all the way to , or to trace how strong subadditivity becomes the data-processing inequality.
Why is there no quantum conditional density operator?
Classically is an average of the entropies of well-defined conditional distributions , so it cannot be negative. Quantum-mechanically there is no state playing the role of : measuring to fix an outcome disturbs the joint state, and the spectrum of does not factor through any classical index on . So the averaged-entropy definition has nothing to average. All that survives is the chain-rule identity, which we promote to the definition . Because it is now a bare difference of entropies rather than an average, it is free to go negative, and does so precisely for entangled states.
What does a negative S(A|B) actually mean operationally?
Its cleanest meaning is the state-merging cost. Alice holds , Bob holds , they share many copies of plus free classical communication. The optimal rate to transfer Alice's share to Bob is qubits per copy. When she must send that many qubits; when she sends none, and the protocol leaves them with ebits of fresh entanglement — the prior correlation is a resource they can redeploy for teleportation. So negative conditional entropy is "entanglement in the bank", and its magnitude counts the ebits.
How can mutual information reach 2 log d when the classical maximum is log d?
Write . Classically , so . Quantum-mechanically can dip to for a maximally entangled pair, so reaches . The extra factor of two is not double-counting: one is classical correlation readable by local measurements, the other is the quantum correlation stored in entanglement. It is the same factor that lets superdense coding send two classical bits down one qubit.
Is mutual information the same as entanglement?
No — it is broader. measures total correlation, and a classical mixture such as has bit with zero entanglement. Entanglement is only the part of the correlation that cannot be made by local operations and classical communication; separable states can carry mutual information but no entanglement. For pure states the two coincide, with the entanglement entropy. But in general large means the parties are correlated, not entangled — for that you need an entanglement measure, or the negativity of , which does certify entanglement.
Why is strong subadditivity considered so deep?
Because almost every structural theorem in quantum information is a corollary of it, yet it is hard to prove. Monotonicity of relative entropy, the data-processing inequality, the Holevo bound, the converse parts of capacity theorems, and the impossibility results behind quantum key distribution all reduce to strong subadditivity. Unlike ordinary subadditivity (a one-line consequence of Klein's inequality), its proof needs Lieb's concavity theorem — a genuinely non-trivial fact about trace functions of operators — which is why it resisted proof until 1973. In holography it reappears as a constraint on the geometry of spacetime, underscoring how much physics one inequality can carry.
The duality S(A|B) = −S(A|C) sounds too clean. When does it hold?
It holds for any pure tripartite state , which is no restriction at all: every mixed is the marginal of some purification, so you can always append a that makes it exact. The proof is two lines using pure-state marginal equalities: and give . The consequence is a monogamy-like exclusivity — can have non-negative conditional entropy with at most one of — and it is the mathematical heart of why an eavesdropper's information and a legitimate receiver's information trade off so sharply in security proofs.