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131 lines
3.9 KiB
Python
131 lines
3.9 KiB
Python
import math
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import numpy as np
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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.cpu import CpuTensorNet
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from qibotn.benchmark_cases import build_circuit as build_benchmark_circuit
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def build_circuit(nqubits=6):
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circuit = Circuit(nqubits)
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for qubit in range(nqubits):
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circuit.add(gates.RY(qubit, theta=0.1 * (qubit + 1)))
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circuit.add(gates.RZ(qubit, theta=-0.05 * (qubit + 1)))
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for qubit in range(nqubits - 1):
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circuit.add(gates.CNOT(qubit, qubit + 1))
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return circuit
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def build_observable(nqubits):
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form = 0
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for qubit in range(nqubits):
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form += 0.5 * X(qubit) * Z((qubit + 1) % nqubits)
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return hamiltonians.SymbolicHamiltonian(form=form)
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def test_cpu_generic_tn_expectation_matches_statevector():
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circuit = build_circuit()
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observable = build_observable(circuit.nqubits)
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exact = observable.expectation_from_state(circuit().state(numpy=True))
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backend = CpuTensorNet(
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{
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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": observable,
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}
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)
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value = backend.execute_circuit(circuit)[0]
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assert math.isclose(value, exact, abs_tol=1e-12)
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def test_cpu_mps_expectation_matches_statevector():
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circuit = build_circuit()
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observable = build_observable(circuit.nqubits)
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exact = observable.expectation_from_state(circuit().state(numpy=True))
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backend = CpuTensorNet(
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{
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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": observable,
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"max_bond_dimension": 64,
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"tensor_module": "torch",
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"torch_threads": 1,
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}
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)
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value = backend.execute_circuit(circuit)[0]
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assert math.isclose(value, exact, abs_tol=1e-12)
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def test_cpu_runcard_pauli_pattern_matches_statevector():
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circuit = build_circuit()
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observable = {"pauli_string_pattern": "IXZ"}
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exact_hamiltonian = hamiltonians.SymbolicHamiltonian(
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form=X(1) * Z(2) * X(4) * Z(5)
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)
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exact = exact_hamiltonian.expectation_from_state(circuit().state(numpy=True))
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for mps_enabled in (False, True):
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backend = CpuTensorNet(
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{
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"MPI_enabled": False,
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"MPS_enabled": mps_enabled,
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"NCCL_enabled": False,
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"expectation_enabled": observable,
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"max_bond_dimension": 64,
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"tensor_module": "torch",
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"torch_threads": 1,
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}
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)
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value = backend.execute_circuit(circuit)[0]
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assert math.isclose(value, exact, abs_tol=1e-12)
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def test_cpu_mps_sampling_uses_nshots():
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circuit = Circuit(4)
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circuit.add(gates.H(0))
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for qubit in range(3):
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circuit.add(gates.CNOT(qubit, qubit + 1))
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backend = CpuTensorNet(
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{
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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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)
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result = backend.execute_circuit(circuit, nshots=100)
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assert sum(result.frequencies().values()) == 100
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assert set(result.frequencies()) <= {"0000", "1111"}
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def test_cpu_generic_tn_long_pauli_string_matches_statevector():
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circuit = build_benchmark_circuit("rxx_rzz", 10, 2, 42)
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observable = {"pauli_string_pattern": "XZ"}
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exact_hamiltonian = hamiltonians.SymbolicHamiltonian(
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form=X(0) * Z(1) * X(2) * Z(3) * X(4) * Z(5) * X(6) * Z(7) * X(8) * Z(9)
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)
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exact = exact_hamiltonian.expectation_from_state(circuit().state(numpy=True))
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backend = CpuTensorNet(
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{
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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": observable,
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}
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)
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value = backend.execute_circuit(circuit)[0]
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assert math.isclose(value, exact, abs_tol=1e-12)
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