JaunatisBlue
c4a82614b3
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92 lines
2.7 KiB
Python
92 lines
2.7 KiB
Python
import math
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import pytest
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from qibo import Circuit, gates, hamiltonians
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from qibo.symbols import X, Z
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from qibotn.backends.qmatchatea import QMatchaTeaBackend
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def build_observable(nqubits):
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"""Helper function to construct a target observable."""
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hamiltonian_form = 0
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for i in range(nqubits):
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hamiltonian_form += 0.5 * X(i % nqubits) * Z((i + 1) % nqubits)
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hamiltonian = hamiltonians.SymbolicHamiltonian(form=hamiltonian_form)
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return hamiltonian, hamiltonian_form
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def build_GHZ(nqubits):
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"""Helper function to construct a layered quantum circuit."""
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circ = Circuit(nqubits)
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circ.add(gates.H(0))
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[circ.add(gates.CNOT(q, q + 1)) for q in range(nqubits - 1)]
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return circ
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def construct_targets(nqubits):
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"""Construct strings of 1s and 0s of size `nqubits`."""
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ones = "1" * nqubits
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zeros = "0" * nqubits
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return ones, zeros
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@pytest.mark.parametrize("nqubits", [2, 10, 40])
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def test_probabilities(backend, nqubits):
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circ = build_GHZ(nqubits=nqubits)
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if isinstance(backend, QMatchaTeaBackend):
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# unbiased prob
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out_u = backend.execute_circuit(
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circuit=circ,
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prob_type="U",
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num_samples=1000,
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).probabilities()
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math.isclose(out_u[0], 0.5, abs_tol=1e-7)
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math.isclose(out_u[1], 0.5, abs_tol=1e-7)
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out_g = backend.execute_circuit(
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circuit=circ,
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prob_type="G",
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prob_threshold=1.0,
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).probabilities()
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math.isclose(out_g[0], 0.5, abs_tol=1e-7)
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math.isclose(out_g[1], 0.5, abs_tol=1e-7)
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out_e = backend.execute_circuit(
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circuit=circ,
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prob_type="E",
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prob_threshold=0.2,
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).probabilities()
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math.isclose(out_e[0], 0.5, abs_tol=1e-7)
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math.isclose(out_e[1], 0.5, abs_tol=1e-7)
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@pytest.mark.parametrize("nqubits", [2, 10, 40])
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@pytest.mark.parametrize("nshots", [100, 1000])
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def test_shots(backend, nqubits, nshots):
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circ = build_GHZ(nqubits=nqubits)
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ones, zeros = construct_targets(nqubits)
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# For p = 0.5, sigma = sqrt(nshots * 0.5 * 0.5) = sqrt(nshots)/2.
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sigma_threshold = 3 * (math.sqrt(nshots) / 2)
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outcome = backend.execute_circuit(circ, nshots=nshots)
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frequencies = outcome.frequencies()
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shots_ones = frequencies.get(ones, 0)
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shots_zeros = frequencies.get(zeros, 0)
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# Check that the counts for both outcomes are within the 3-sigma threshold of nshots/2.
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assert (
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abs(shots_ones - (nshots / 2)) < sigma_threshold
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), f"Count for {ones} deviates too much: {shots_ones} vs expected {nshots/2}"
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assert (
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abs(shots_zeros - (nshots / 2)) < sigma_threshold
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), f"Count for {zeros} deviates too much: {shots_zeros} vs expected {nshots/2}"
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