Multi-qubit entangled states generalize entanglement to arbitrary numbers of qubits ($n \geq 3$). This page covers general $n$-qubit entanglement structures; for three-qubit specific patterns, see three-qubit entangled states.

• Dicke state: symmetric superposition of all states with Hamming weight $k$, generalizes W states for any $n$
• Graph state: arbitrary graph topology enables measurement-based computing on any structure
• Cluster state: lattice-structured graph state (1D chain, 2D square, etc.) optimized for universal computation
• Maximally entangled state (Φ_d): uniform superposition across all bases, extends Bell state structure
• Choi state: represents quantum channels as entangled bipartite states for process tomography

For specific three-qubit cases (GHZ, W), see three-qubit entangled states.

Generalization of cluster states to arbitrary graph structures:

• Graph topology: each vertex is a qubit, edges define CZ interactions
• Measurement-based computation: implement gates through adaptive single-qubit measurements
• Flexibility: arbitrary graphs enable tailored entanglement patterns
• Applications: universal quantum computation on 2D lattices, topological error correction
• Efficiency: stabilizer formalism enables classical simulation of Clifford measurements

Lattice-structured graph states (1D chains, 2D square grids, higher-dimensional lattices):

• Regular topology: vertices arranged in periodic or open boundary conditions
• Two-qubit gates: CZ interactions between neighboring pairs only
• Measurement-based: universal quantum computation via adaptive single-qubit measurements
• Topological protection: 2D clusters admit surface codes and topological error correction
• Scalability: extends to arbitrary lattice size with uniform local structure

Extends Bell state structure to $n$ qubits and $d$ dimensions:

• Uniform superposition: all basis states appear with equal amplitude
• Maximum entanglement entropy: reduced density matrices are maximally mixed
• Channel applications: Choi state represents arbitrary quantum channels as entangled states
• Resource consumption: quantified by entanglement entropy; bounds quantum advantage in protocols

Canonical representation of quantum channels via Choi-Jamiolkowski correspondence:

• Channel-to-state mapping: applies channel to half of maximally entangled bipartite state
• Process tomography: measure Choi state in Bell basis to characterize entire channel
• Theoretical importance: connects channel properties (completely positive, trace-preserving) to state properties (positive semidefinite, normalized)

Graph states (including cluster states) are stabilizer states:

• Defined by commuting Pauli stabilizer generators
• Classical simulation: Clifford circuits + measurements are efficiently simulatable via Gaussian elimination
• Non-Clifford extension: adding T gates makes simulation hard (exponential)
• Error correction: stabilizer formalism enables detection of errors without measuring individual qubits

General $n$-qubit entanglement requires:

• Initial state preparation: typically $|0\rangle^{\otimes n}$ or $|+\rangle^{\otimes n}$
• Entangling two-qubit gates: CZ gates for graph/cluster states
• Gate sequence complexity: $O(n)$ for cluster states on regular lattices
• Decoherence management: total preparation time must be less than coherence time

• Coherence time: maintaining $n$-qubit superposition and entanglement
• Gate fidelity: error accumulation in $O(n)$ or $O(n^2)$ gate sequences
• Two-qubit connectivity: limited by processor architecture (not all pairs may be directly interacting)
• Classical simulation: graph states with Clifford measurements simulatable; non-Clifford measurements are hard
• Resource cost: arbitrary $n$-qubit states require exponential classical memory to represent
• Measurement resolution: discriminating measurement outcomes with sufficient signal-to-noise for all $2^n$ possibilities

• Small systems (3–4 qubits): three-qubit entanglement tests, quantum error correction primitives
• Medium systems (10–20 qubits): graph states for measurement-based computing, quantum simulation
• Large systems (100+ qubits): cluster states for topological codes, distributed quantum sensing