Sign in
← Atlas
Exact & formalOperatorsSpin Hamiltonians

Transverse-field Ising model operator

The transverse-field Ising model (TFIM): the standard minimal Hamiltonian exhibiting a quantum (zero-temperature) phase transition driven by competing Z-Z order and X-field disorder.

ising modeltransverse fieldquantum phase transitionjordan-wigner

Atlas stars stay in the public catalog. Saving this entry to your workspace starts an unstarred private copy.

Adding a transverse field to the classical Ising chain turns a purely statistical-mechanics model into the paradigmatic example of a quantum phase transition — one driven by the Heisenberg uncertainty between competing non-commuting terms rather than by thermal fluctuations.

Circuit & simulation
h < J: ferromagnetic order50%
h > J: paramagnetic50%
What this takes and returns
TakesNothingWhat joins here

No input port, deliberately. You measure a state with this entry; you do not apply it and pass a register on.

Nothing in the Atlas meets this end.

ReturnsNothingWhat joins here

No output port, deliberately. An observable is measured with, never applied, so there is no register to hand on.

Nothing in the Atlas meets this end.

Not a stage. You measure a state with this; you do not apply it and pass a register on. It has a width and deliberately no ports. See all 60 →

Where the map uses this

This record is an instance of an object the map names, so these are the processes that consume or produce one. None of them is about this record in particular.

Hamiltonian you can query 4 of 33 processes

How it works

The transverse-field Ising model (TFIM) on a chain of nn sites is

H=Ji=1n1ZiZi+1hi=1nXi.H = -J\sum_{i=1}^{n-1} Z_iZ_{i+1} - h\sum_{i=1}^{n} X_i.

Genuinely quantum. Unlike the classical Ising operator, [H,Zi]0[H, Z_i] \neq 0 because XiX_i and ZiZ_i anticommute: {X,Z}=0XZ=ZX\{X,Z\}=0 \Rightarrow XZ=-ZX. The transverse field therefore drives real transitions between computational-basis states, and HH cannot be diagonalized simply by inspection.

Exact solvability. Via a Jordan–Wigner transformation, the 1D TFIM maps exactly onto free fermions, giving the closed-form dispersion relation (periodic chain, NN\to\infty)

ϵ(k)=2J1+(h/J)22(h/J)cosk,\epsilon(k) = 2J\sqrt{1 + (h/J)^2 - 2(h/J)\cos k},

so ground-state energies and gaps are known in closed form at every system size — the reason this model is the standard exact benchmark for VQE, Trotterized time evolution, and quantum annealing schedules.

Quantum phase transition. At zero temperature, tuning h/Jh/J drives a transition at the self-dual point h=Jh=J between a ferromagnetically ordered phase (h<Jh<J, spontaneous Z2\mathbb{Z}_2 symmetry breaking of the global spin-flip iXi\prod_i X_i) and a paramagnetic phase (h>Jh>J, field-aligned along X). The transition is continuous, with the gap closing as ΔhJzν\Delta \sim |h-J|^{z\nu} — the paradigmatic example distinguishing a quantum phase transition (driven by h/Jh/J at T=0T=0) from the thermal transition of the classical 2D Ising model, to which the 1D TFIM's partition function is related by a quantum-classical mapping.

Use in the catalog. This operator sits directly above the classical Ising Hamiltonian and the XXZ chain in the family of exactly-characterizable spin models: setting h=0h=0 recovers the classical diagonal operator, and the model's non-commuting structure is exactly what a Trotter-based Hamiltonian simulation or a VQE ansatz must reproduce.

Implementation
Native
transverse_field_ising_operator.py
import numpy as np
from qiskit.quantum_info import SparsePauliOp

def tfim_hamiltonian(n: int, J: float, h: float) -> SparsePauliOp:
    terms, coeffs = [], []
    for i in range(n - 1):
        s = ["I"] * n
        s[i], s[i + 1] = "Z", "Z"
        terms.append("".join(reversed(s)))
        coeffs.append(-J)
    for i in range(n):
        s = ["I"] * n
        s[i] = "X"
        terms.append("".join(reversed(s)))
        coeffs.append(-h)
    return SparsePauliOp(terms, coeffs)

n = 4
H_crit = tfim_hamiltonian(n, J=1.0, h=1.0)  # self-dual critical point
Z0 = SparsePauliOp("IIIZ")
commutator = H_crit.to_matrix() @ Z0.to_matrix() - Z0.to_matrix() @ H_crit.to_matrix()
print("max |[H, Z0]| =", np.abs(commutator).max())  # nonzero: genuine quantum dynamics

eigvals = np.linalg.eigvalsh(H_crit.to_matrix())
print("Ground energy:", eigvals[0])

RESULT = {"ground_energy": float(eigvals[0]), "max_commutator_norm": float(np.abs(commutator).max())}
Quantum vs classical

Classical baseline

Use a classical state-vector or matrix simulation at the same width, precision, and measurement objective.

Quantum claim

The quantum record demonstrates a state or operator behavior; it does not make classical simulation or communication costs disappear.

How to compare

Compare fidelity, samples, gate depth, noise, memory, and the cost of preparing and reading the state.

Declared gaps

Nobody has reviewed this record for gaps yet.

Literature & references
The one-dimensional Ising model with a transverse field1970 · P. Pfeuty

Original exact solution of the TFIM via Jordan-Wigner fermionization, giving the dispersion relation used in this entry.

doi.org/10.1016/0003-4916(70)90270-8
Quantum Phase Transitions2011 · S. Sachdev

Standard textbook treatment of the TFIM as the canonical example of a quantum (zero-temperature) phase transition.

doi.org/10.1017/cbo9780511973765