190 lines
6.3 KiB
Python
190 lines
6.3 KiB
Python
import math
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import cupy as cp
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import pytest
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import qibo
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from qibo import construct_backend, hamiltonians
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from qibo.models import QFT
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from qibo.symbols import X, Z
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def qibo_qft(nqubits, swaps):
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circ_qibo = QFT(nqubits, swaps)
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state_vec = circ_qibo().state(numpy=True)
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return circ_qibo, state_vec
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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_observable_dict(nqubits):
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"""Construct a target observable as a dictionary representation.
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Returns a dictionary suitable for `create_hamiltonian_from_dict`.
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"""
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terms = []
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for i in range(nqubits):
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term = {
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"coefficient": 0.5,
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"operators": [("X", i % nqubits), ("Z", (i + 1) % nqubits)],
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}
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terms.append(term)
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return {"terms": terms}
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@pytest.mark.gpu
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@pytest.mark.parametrize("nqubits", [1, 2, 5, 10])
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def test_eval(nqubits: int, dtype="complex128"):
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"""
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Args:
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nqubits (int): Total number of qubits in the system.
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dtype (str): The data type for precision, 'complex64' for single,
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'complex128' for double.
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"""
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# Test qibo
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qibo.set_backend(backend="numpy")
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qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
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result_sv_cp = cp.asarray(result_sv)
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# Test cutensornet
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backend = construct_backend(backend="qibotn", platform="cutensornet")
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# Test 1: no computation settings specified. Use default.
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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print(
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f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
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)
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assert cp.allclose(
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result_sv_cp, result_tn.statevector.flatten()
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), "Resulting dense vectors do not match"
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# Test 2: Explicit computation settings specified (same as default).
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computation_settings = {
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"MPI_enabled": False,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": False,
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}
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backend.configure_tn_simulation(computation_settings)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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print(
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f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
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)
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assert cp.allclose(
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result_sv_cp, result_tn.statevector.flatten()
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), "Resulting dense vectors do not match"
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@pytest.mark.gpu
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@pytest.mark.parametrize("nqubits", [2, 5, 10])
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def test_mps(nqubits: int, dtype="complex128"):
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"""Evaluate MPS with cuQuantum.
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Args:
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nqubits (int): Total number of qubits in the system.
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dtype (str): The data type for precision, 'complex64' for single,
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'complex128' for double.
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"""
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# Test qibo
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qibo.set_backend(backend="numpy")
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qibo_circ, result_sv = qibo_qft(nqubits, swaps=True)
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result_sv_cp = cp.asarray(result_sv)
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# Test cutensornet
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backend = construct_backend(backend="qibotn", platform="cutensornet")
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# Test 1: No MPS computation settings specified. Use default.
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computation_settings_1 = {
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"MPI_enabled": False,
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"MPS_enabled": True,
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"NCCL_enabled": False,
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"expectation_enabled": False,
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}
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backend.configure_tn_simulation(computation_settings_1)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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print(
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f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
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)
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assert cp.allclose(
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result_tn.statevector.flatten(), result_sv_cp
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), "Resulting dense vectors do not match"
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# Test 2: Explicit MPS computation settings specified (same as default).
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computation_settings_2 = {
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"MPI_enabled": False,
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"MPS_enabled": {
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"qr_method": False,
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"svd_method": {
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"partition": "UV",
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"abs_cutoff": 1e-12,
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},
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},
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"NCCL_enabled": False,
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"expectation_enabled": False,
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}
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backend.configure_tn_simulation(computation_settings_2)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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print(
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f"State vector difference: {abs(result_tn.statevector.flatten() - result_sv_cp).max():0.3e}"
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)
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assert cp.allclose(
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result_tn.statevector.flatten(), result_sv_cp
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), "Resulting dense vectors do not match"
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@pytest.mark.parametrize("nqubits", [2, 5, 10])
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def test_expectation(nqubits: int, dtype="complex128"):
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# Test qibo
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qibo_circ, state_vec_qibo = qibo_qft(nqubits, swaps=True)
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ham, ham_form = build_observable(nqubits)
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numpy_backend = construct_backend("numpy")
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exact_expval = numpy_backend.calculate_expectation_state(
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hamiltonian=ham,
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state=state_vec_qibo,
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normalize=False,
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)
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# Test cutensornet
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backend = construct_backend(backend="qibotn", platform="cutensornet")
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# Test 1: No Hamilitonian computation settings specified. Use default.
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computation_settings_1 = {
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"MPI_enabled": False,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": True,
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}
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backend.configure_tn_simulation(computation_settings_1)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)
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# Test 2: hamiltonians.SymbolicHamiltonian object in computation settings specified.
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computation_settings_2 = {
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"MPI_enabled": False,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": ham,
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}
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backend.configure_tn_simulation(computation_settings_2)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)
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# Test 3: Dictionary object form of hamiltonian in computation settings specified.
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ham_dict = build_observable_dict(nqubits)
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computation_settings_3 = {
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"MPI_enabled": False,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": ham_dict,
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}
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backend.configure_tn_simulation(computation_settings_3)
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result_tn = backend.execute_circuit(circuit=qibo_circ)
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assert math.isclose(exact_expval.item(), result_tn.real.get().item(), abs_tol=1e-7)
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