← Atlas

Algorithms · Variational quantum algorithm

Variational quantum eigensolver for ground-state energy

A hybrid chemistry workflow that compares a quantum expectation loop with classical eigensolvers.

Strong empiricalUpdated

Open on the mapView frozen graph

ansatz statePauli measurementsclassical optimizerStep 1 of 3 · U(θ) · on ansatz stateU(θ)Step 2 of 3 · ⟨H⟩ · on ansatz state, Pauli measurements⟨H⟩Step 3 of 3 · θ ← opt · on Pauli measurementsθ ← opt

3 steps across 3 wires. Hover a step, or press play to go through them in order.

Expected outcomes

  • Energy92%
  • Steps3
  • Wires3
  • QubitsProblem mapped
  • LoopQuantum + classical
  • OutputEnergy estimate
  • Papers2

VQE estimates a ground-state energy using the variational principle: for any normalized trial state ∣ψ(θ)⟩=U(θ)∣0⟩|\psi(\theta)\rangle=U(\theta)|0\rangle and Hamiltonian HH, the Rayleigh quotient obeys ⟨ψ(θ)∣H∣ψ(θ)⟩≥E0\langle\psi(\theta)|H|\psi(\theta)\rangle \ge E_0, the true ground energy, with equality only when ∣ψ(θ)⟩|\psi(\theta)\rangle is an exact ground eigenstate. The circuit prepares ∣ψ(θ)⟩|\psi(\theta)\rangle with a parameterized ansatz U(θ)U(\theta), measures each Pauli term of H=∑kckPkH=\sum_k c_k P_k to build ⟨H⟩(θ)\langle H\rangle(\theta), and hands that scalar to a classical optimizer that updates θ\theta to push the energy down — a hybrid quantum/classical loop repeated to convergence. This record checks that expectation-value loop for a small Pauli Hamiltonian against exact diagonalization, matching its own caveat: the variational bound is guaranteed by the math above, but reaching a chemically useful energy still depends on ansatz expressivity, optimizer convergence, and shot noise this toy fixture does not certify.

References

Strong empirical
  • Small-instance agreement
  • Invariant & structural checks
  • Peer-reviewed paper
Method
A small Pauli Hamiltonian expectation is compared with exact diagonalization while the classical optimizer is visible.
Result
Pass for the toy Hamiltonian · molecular benchmark remains a separate dataset and basis-set decision.
Caveat
Noise, ansatz expressivity, optimizer convergence, measurement grouping, and mapping choices dominate practical outcomes.
A variational eigenvalue solver on a quantum processor ↗
Kind
curated reference
Reviewed by
LeonaQ literature curation pass
License
Public bibliographic link; code is an original reference implementation
chemistryvqeground statehybrid
  • Molecular ground states and reaction energies
  • Materials and catalyst screening
  • Hybrid quantum chemistry prototyping

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