def hea(params):
ansatz = Circuit(4)
for i in range(4):
ansatz.Ry(params[i], i)
for i in range(3):
ansatz.CX(i, i + 1)
for i in range(4):
ansatz.Ry(params[4 + i], i)
return ansatz
def test_implicit_perm() -> None:
c = Circuit(2)
c.CX(0, 1)
c.CX(1, 0)
c.Ry(0.1, 1)
c1 = c.copy()
CliffordSimp().apply(c1)
b = MyBackend()
b.compile_circuit(c)
b.compile_circuit(c1)
assert c.implicit_qubit_permutation() != c1.implicit_qubit_permutation()
for bo in [BasisOrder.ilo, BasisOrder.dlo]:
s = b.get_state(c, bo)
s1 = b.get_state(c1, bo)
assert np.allclose(s, s1)
# This notebook makes use of the Qiskit and ProjectQ backend modules `pytket_qiskit` and `pytket_projectq`, as well as the electronic structure module `openfermion`, all three of which should first be installed via `pip`.
#
# We will start by generating an ansatz and Hamiltonian for the chemical of interest. Here, we are just using a simple model of $\mathrm{H}_2$ with four qubits representing the occupation of four spin orbitals.
from pytket import Circuit, Qubit, Bit
from pytket.utils.operators import QubitPauliOperator
from sympy import symbols
from openfermion import QubitOperator
# Generate ansatz and Hamiltonian:
ansatz = Circuit()
qubits = ansatz.add_q_register("q", 4)
args = symbols("a0 a1 a2 a3 a4 a5 a6 a7")
for i in range(4):
ansatz.Ry(args[i], qubits[i])
for i in range(3):
ansatz.CX(qubits[i], qubits[i + 1])
for i in range(4):
ansatz.Ry(args[4 + i], qubits[i])
for command in ansatz:
print(command)
# In reality, you would use an expectation value calculation as the objective function for a classical optimisation routine to determine the parameter values for the ground state. For the purposes of this notebook, we will use some predetermined values for the ansatz, already optimised for $\mathrm{H}_2$.
arg_values = [
7.17996183e-02,
2.95442468e-08,
1.00000015e00,
1.00000086e00,
# # To run every option in this example, you will need `pytket`, `pytket-qiskit`, `pytket-pyquil`, `pytket-qsharp`, `pytket-qulacs`, and `pytket-projectq`. # # With the number of simulator `Backend`s available across the `pytket` extension modules, we are often asked why to use one over another. Surely, any two simulators are equivalent if they are able to sample the circuits in the same way, right? Not quite. In this notebook we go through each of the simulators in turn and describe what sets them apart from others and how to make use of any unique features. # # But first, to demonstrate the significant overlap in functionality, we'll just give some examples of common usage for different types of backends. # ## Sampling simulator usage from pytket import Circuit from pytket.extensions.qiskit import AerBackend # Define a circuit: c = Circuit(3, 3) c.Ry(0.7, 0) c.CX(0, 1) c.X(2) c.measure_all() # Run on the backend: backend = AerBackend() backend.compile_circuit(c) handle = backend.process_circuit(c, n_shots=2000) counts = backend.get_result(handle).get_counts() print(counts) # ## Statevector simulator usage from pytket import Circuit
