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Algorithms · Variational quantum eigensolver

Classical-shadow VQE estimation

Randomized measurements are reused to estimate many observables from a shared data set.

Attested & literatureUpdated

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A small worked example, computed in your browser. It is not a result from this record's papers.

VQE on a 2-qubit transverse-field Ising Hamiltonian

2 qubits, H = Z⊗Z + 0.5*(X⊗I + I⊗X), a stated toy Hamiltonian (not a molecule), minimised with a hardware-efficient ansatz.

q0q1Step 1 of 5 · RY(π/2)RY(π/2)Step 2 of 5 · RY(5π/4)RY(5π/4)Step 3 of 5 · CXCXStep 4 of 5 · RY(0)RY(0)Step 5 of 5 · RY(0)RY(0)

Step 1 of 5 · RY(π/2)

⟨H⟩ after this step0.500

H = Z₀Z₁ + 0.5 X₁ + 0.5 X₀

  1. One of the four angles an optimiser would be free to turn. π/2 is not a textbook value: it came out of a deterministic coarse grid search over this file's own simulator (see the comment on VQE_ANGLES), which is how these four constants were fixed. A real VQE reaches angles like these by measuring the energy and adjusting; this example does not run that loop.

    Probability spreads from 1 outcome to 2. All 2 are equally likely.

  2. The same freedom on qubit 1, and a different answer: 5π/4 rather than π/2. The two qubits are not symmetric in this Hamiltonian, so the ansatz must be able to treat them differently.

    Probability spreads from 2 outcomes to 4.

  3. A CX entangles the two qubits, letting the ansatz reach states a product of single-qubit rotations alone cannot.

    Probability moves between the same 4 outcomes. Up: 01 7.3% → 42.7%. Down: 11 42.7% → 7.3%. This is the step that entangles the qubits: from here on no single qubit has a state of its own, only the register as a whole does.

  4. The second layer's rotation on qubit 0. The search settled on zero here, so it does nothing at all — the honest outcome, not a mistake: the layer has to be in the circuit for the search to have had the choice.

    Nothing about the state changes here — the same outcomes with the same probabilities and the same phases as the step before.

  5. And the last of the four, also zero. A two-layer ansatz turned out to need only its first layer for this Hamiltonian — something the search found, and not something you can see by inspecting the circuit.

    Nothing about the state changes here — the same outcomes with the same probabilities and the same phases as the step before.

Readout At these angles the ansatz's energy ⟨H⟩ reaches E0 = −√2, the Hamiltonian's exact ground energy; a real VQE run reaches these angles by repeating measure-the-energy then update-the-angles, not by a closed-form solution.

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  • Record typeLiterature method
  • ExecutionRequires a concrete ansatz and Hamiltonian
  • Papers1

Randomized measurements are reused to estimate many observables from a shared data set. In a complete experiment, the method must be paired with a defined qubit Hamiltonian, reference state, parameterized circuit, measurement grouping, classical optimizer, stopping rule, and error analysis. The catalog therefore treats it as a literature-backed algorithm record rather than pretending that one generic snippet is the paper's implementation. Use the cited source to recover assumptions and compare energy error, variance, circuit resources, measurement cost, optimizer evaluations, and robustness under the same instance and budget.

References

Attested & literature
  • Peer-reviewed paper
Method
Primary-paper or VQE-survey curation
Result
Pass · method purpose, optimization role, and non-circuit status are explicit.
Caveat
This is a method reference, not a fixed executable circuit; the ansatz, Hamiltonian, optimizer, and measurement plan must be supplied for a run.
Predicting Many Properties of a Quantum System from Very Few Measurements ↗
Kind
curated reference
Reviewed by
Leona Quantum curation pass
License
Citation metadata only; source publication terms apply
VQEvariational algorithmclassical-shadow vqe estimation

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