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Eigenvalues and Eigenstates

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Eigenvalues and EigenvectorsQuantum Operators→→Observables and Hermitian OperatorsQuantum Mechanical Treatment of Hydrogen
linear-algebra quantum-mechanics eigenvalue-problem

Core Idea

For an operator Â, an eigenstate |φₙ⟩ satisfies Â|φₙ⟩ = λₙ|φₙ⟩ where λₙ is the eigenvalue. In quantum mechanics, eigenvalues of an observable operator are the only possible measurement outcomes, and eigenstates are states in which the observable has a definite value. The completeness of eigenstates ensures any quantum state can be expanded in eigenbasis.

Explainer

You already know from linear algebra that for a matrix M, an eigenvector v satisfies Mv = λv — the vector is unchanged in direction by the operation, only scaled by the eigenvalue λ. In quantum mechanics, this algebraic relationship becomes the central fact about measurement. An eigenstate |φₙ⟩ of operator  satisfies Â|φₙ⟩ = λₙ|φₙ⟩, and the eigenvalue λₙ is the only value you can ever obtain when measuring A in that state. This is the sharpest form of a quantum prediction: perfect certainty about a measurement outcome is equivalent to being in an eigenstate.

Why must observables have real eigenvalues? Because measured quantities must be real numbers. You know from the prerequisite on quantum operators that observables correspond to Hermitian operators ( = †). A fundamental theorem guarantees that Hermitian operators have real eigenvalues and that eigenstates belonging to distinct eigenvalues are orthogonal: ⟨φₘ|φₙ⟩ = δₘₙ. Orthogonality is physically essential — two distinct measurement outcomes must correspond to distinguishable states, and inner product zero means maximally distinguishable in quantum mechanics.

The real power of eigenstates comes from completeness: the eigenstates of any Hermitian operator span the Hilbert space. Any quantum state |ψ⟩ can be written as |ψ⟩ = Σₙ cₙ|φₙ⟩ where cₙ = ⟨φₙ|ψ⟩. This is just the projection decomposition you know from linear algebra, now applied to states. When you measure A in state |ψ⟩, the probability of obtaining λₙ is |cₙ|², and the state collapses to |φₙ⟩. The squared inner products give the Born rule; the eigenbasis provides the framework in which probabilities are computed.

A concrete example: the Hamiltonian Ĥ is the energy operator. Its eigenstates |Eₙ⟩ satisfy Ĥ|Eₙ⟩ = Eₙ|Eₙ⟩ — these are the stationary states, states of definite energy. For the hydrogen atom, the energy eigenvalues are Eₙ = −13.6/n² eV with n = 1, 2, 3, ... The discreteness of these eigenvalues is why atomic spectra consist of sharp lines: only these specific energy values are possible, so only photons with energies equal to differences between levels can be emitted or absorbed. Any general state of the hydrogen atom is a superposition of energy eigenstates, and an energy measurement collapses it to one of them with probability |cₙ|².

Practice Questions 5 questions

Prerequisite Chain

Understanding Zero → The Number Zero → Counting to Five → Counting to 10 → Counting to 20 → Counting a Set of Objects Up to 20 → Cardinality: The Last Number Counted → Matching Numerals to Quantities → Subitizing Small Quantities → Addition Within 10 → Number Bonds to 10 → Addition Within 20 → Doubles and Near Doubles → Doubles Facts Within 10 → Near Doubles Facts Within 20 → Mental Math Strategies for Addition → Mental Math: Adding and Subtracting Tens → Addition Within 100 → Repeated Addition as Multiplication → Multiplication as Equal Groups → Multiplication: Arrays → Basic Multiplication Facts (0s, 1s, 2s, 5s, 10s) → Multiplication Facts Within 100 → Division as Equal Sharing → Division as Grouping (Measurement Division) → Division: Grouping (Repeated Subtraction) Model → Division: Fair Sharing Model → Division as Equal Sharing → Division as Grouping → Basic Division Facts → Division Facts Within 100 → Multiplication and Division Fact Families → Relationship Between Multiplication and Division → Division Facts as Inverse of Multiplication → Remainders and Quotients in Division → Division Word Problems → Multi-Step Word Problems → Solving Multi-Step Word Problems → Multiplication Word Problems → Division Word Problems → Introduction to Long Division → Factors and Multiples → Prime and Composite Numbers → Equivalent Fractions → Relating Fractions and Decimals → Decimal Place Value → Integers and the Number Line → Comparing and Ordering Integers → Absolute Value → Adding Integers → Subtracting Integers → Multiplying Integers → Dividing Integers → Unit Rates → Proportions → Percent Concept → Converting Between Fractions, Decimals, and Percents → Operations with Rational Numbers → Two-Step Equations → Solving Multi-Step Equations → Equations with Variables on Both Sides → Angle Pairs: Complementary, Supplementary, and Vertical → Parallel Lines and Transversals → Corresponding Angles → Alternate Interior Angles → Triangle Angle Sum Theorem → Exterior Angle Theorem → Triangle Inequality Theorem → Similar Triangles: AA Similarity → Similar Triangles: SSS and SAS Similarity → Proportions in Similar Triangles → Right Triangle Trigonometry Introduction → Sine, Cosine, and Tangent Ratios → Trigonometric Ratios Review → Vectors in Two Dimensions → Vector Operations: Addition, Subtraction, and Scalar Multiplication → Dot Product (Inner Product in R^n) → Matrix Multiplication → Determinants of 2×2 and 3×3 Matrices → Invertible Matrices and Matrix Inverses → Systems of Linear Equations and Matrix Form → Gaussian Elimination and Row Reduction → Row Echelon Form and Back Substitution → The Standard Matrix of a Linear Transformation → Eigenvalues and Eigenvectors → Hilbert Spaces and Dirac Notation → Quantum Operators → Eigenvalues and Eigenstates

Longest path: 88 steps · 353 total prerequisite topics

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