uniqc.algorithms.core.circuits.vqd module¶
Variational Quantum Deflation (VQD) circuit components.
- uniqc.algorithms.core.circuits.vqd.vqd_ansatz(n_qubits, ansatz_params, prev_states, qubits=None, penalty=10.0, n_layers=2)[source]¶
Build a VQD ansatz circuit fragment (variational style).
Returns a fresh
Circuit.prev_statesis accepted to keep the VQD signature, but is only used by the classical optimiser.
- uniqc.algorithms.core.circuits.vqd.vqd_circuit(*args, ansatz_params=None, prev_states=None, qubits=None, penalty=10.0, n_layers=2)[source]¶
Build (or apply) a VQD ansatz.
Two calling conventions:
# Variational fragment style (recommended; see also vqd_ansatz): c = vqd_circuit(2, ansatz_params=[0.1]*4, prev_states=[gs], n_layers=2) # Legacy in-place (deprecated): c = Circuit(2) vqd_circuit(c, [0.1]*4, prev_states=[gs], n_layers=2)
- uniqc.algorithms.core.circuits.vqd.vqd_example()[source]¶
Return a small VQD ansatz fragment for tests/docs.
- Return type:
- uniqc.algorithms.core.circuits.vqd.vqd_overlap_circuit(prev_state, ansatz_params, n_layers=2, qubits=None)[source]¶
Build a circuit to compute \(|\langle\psi(\boldsymbol{\theta})|\phi\rangle|^2\).
Uses the swap test: an ancilla qubit controls SWAPs between the ansatz register and a register prepared in prev_state. Measuring the ancilla in the computational basis gives an estimate of the overlap.
Circuit layout (2 data qubits):
ancilla: ──H──●──────●──●──────●── Measure | | | | data_A: ──[ansatz]──SWAP──[ansatz]──SWAP── | | | | data_B: ──[prev]──SWAP──[prev]──SWAP──- Parameters:
prev_state (ndarray) – State vector \(|\phi\rangle\) of dimension \(2^n\).
ansatz_params (list[float]) – Parameters for the HEA ansatz.
n_layers (int) – Number of HEA layers.
qubits (list[int] | None) – Data qubit indices for the ansatz register.
Nonemeans[0, 1, …, n-1]where n is inferred fromprev_state.
- Returns:
A new
Circuitcontaining the swap-test circuit with the ancilla measured.- Raises:
ValueError – prev_state is not a power-of-2 length.
- Return type:
Example
>>> import numpy as np >>> gs = np.array([1, 0, 0, 0], dtype=complex) >>> circ = vqd_overlap_circuit(gs, [0.1]*4, n_layers=2)