def test_trotterize_noiseless(self):
# Noiseless simulations
# Compare that the integrator adiabatic results match the trotterized results
# First, compare the non-IS subspace results
simulation = adiabatic_simulation(sample_graph(), IS_subspace=False)
res_trotterize = simulation.performance_vs_total_time(
np.arange(1, 5, 1),
metric='optimum_overlap',
initial_state=State(equal_superposition(6)),
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='trotterize')
res_RK45 = simulation.performance_vs_total_time(
np.arange(1, 5, 1),
metric='optimum_overlap',
initial_state=State(equal_superposition(6)),
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='RK45')
# Now test in the IS subspace
self.assertTrue(
np.allclose(res_trotterize[0]['trotterize']['optimum_overlap'],
res_RK45[0]['RK45']['optimum_overlap'],
atol=1e-2))
simulation = adiabatic_simulation(sample_graph(), IS_subspace=True)
res_trotterize = simulation.performance_vs_total_time(
np.arange(1, 4, 1),
metric='optimum_overlap',
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='trotterize')
res_RK45 = simulation.performance_vs_total_time(
np.arange(1, 4, 1),
metric='optimum_overlap',
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='RK45')
self.assertTrue(
np.allclose(res_trotterize[0]['trotterize']['optimum_overlap'],
res_RK45[0]['RK45']['optimum_overlap'],
atol=1e-2))
def test_hamiltonian_driver(self):
N = 6
hl_qubit = hamiltonian.HamiltonianDriver(graph=tools_test.sample_graph())
psi0 = State(np.zeros((2 ** N, 1)))
psi0[0, 0] = 1
psi1 = State(tools.outer_product(psi0, psi0))
# Evolve by e^{-i (\pi/2) \sum_i X_i}
psi0 = hl_qubit.evolve(psi0, np.pi / 2)
# Should get (-1j)^N |111111>
self.assertTrue(np.vdot(psi0, psi0) == 1)
self.assertTrue(psi0[-1, 0] == (-1j) ** N)
# Evolve by e^{-i (\pi/2) \sum_i X_i}
psi1 = hl_qubit.evolve(psi1, np.pi / 2)
# Should get (-1j)^N |111111>
self.assertTrue(tools.is_valid_state(psi1))
self.assertAlmostEqual(psi1[-1, -1], 1)
psi0 = State(np.zeros((2 ** N, 1)))
psi0[0, 0] = 1
psi0 = hl_qubit.left_multiply(psi0)
psi1 = np.zeros((2 ** N, 1), dtype=np.complex128)
for i in range(N):
psi1[2 ** i, 0] = 1
self.assertTrue(np.allclose(psi0, psi1))
psi2 = State(np.zeros((2 ** N, 1)))
psi2[0, 0] = 1
psi2 = hl_qubit.hamiltonian @ psi2
self.assertTrue(np.allclose(psi0, psi2))
N = 3
hl = hamiltonian.HamiltonianDriver(transition=(0, 1), code=rydberg)
psi0 = State(np.zeros((rydberg.d ** N, 1)), code=rydberg)
psi0[5, 0] = 1
psi1 = State(tools.outer_product(psi0, psi0), code=rydberg)
psi0 = hl.left_multiply(psi0)
self.assertTrue(psi0[2, 0] == 1)
self.assertTrue(psi0[14, 0] == 1)
psi1 = hl.left_multiply(psi1)
self.assertTrue(psi1[2, 5] == 1)
self.assertTrue(psi1[14, 5] == 1)
psi0 = State(np.zeros((rydberg.d ** N, 1)), code=rydberg)
psi0[5, 0] = 1
psi0 = hl.evolve(psi0, np.pi / 2)
self.assertTrue(np.isclose(psi0[11, 0], -1))
# IS subspace
hl = hamiltonian.HamiltonianDriver(transition=(0, 2), code=rydberg, IS_subspace=True, graph=line_graph(2))
psi0 = State(np.zeros((8, 1)), code=rydberg)
psi0[-1,0] = 1
psi0 = State(tools.outer_product(psi0, psi0), code=rydberg)
self.assertTrue(tools.is_hermitian(hl.evolve(psi0, 1)))
def test_IS_subspace(self):
g = tools_test.sample_graph()
self.assertTrue(g.num_independent_sets == 13)
self.assertTrue(g.mis_size == 2)
import numpy as np
import unittest
from qsim.graph_algorithms.graph import line_graph
from qsim import tools
from qsim.evolution import hamiltonian
from qsim.codes import rydberg
from qsim.test import tools_test
from qsim.codes.quantum_state import State
# Generate sample graph
g = tools_test.sample_graph()
class TestHamiltonian(unittest.TestCase):
def test_hamiltonian_C(self):
pass
# Then compute MaxCut energies
hc = hamiltonian.HamiltonianMaxCut(g)
self.assertTrue(hc.hamiltonian[0, 0] == 0)
self.assertTrue(hc.hamiltonian[-1, -1] == 0)
self.assertTrue(hc.hamiltonian[2, 2] == 3)
def test_rydberg_hamiltonian(self):
# Test normal MIS Hamiltonian
# Graph has six nodes and nine edges
hc = hamiltonian.HamiltonianMIS(g)
self.assertTrue(hc.hamiltonian[0, 0] == -3)
self.assertTrue(hc.hamiltonian[-1, -1] == 0)
self.assertTrue(hc.hamiltonian[-2, -2] == 1)
import numpy as np
import unittest
from qsim.test.tools_test import sample_graph
from qsim.tools.tools import equal_superposition, outer_product
from qsim.graph_algorithms.graph import Graph, ring_graph
from qsim.codes.quantum_state import State
import networkx as nx
from qsim.evolution import quantum_channels, hamiltonian
from qsim.graph_algorithms import qaoa
from qsim.codes import two_qubit_code, jordan_farhi_shor
# Generate sample graph
N = 6
g = sample_graph()
ring = ring_graph(N)
hc = hamiltonian.HamiltonianMaxCut(g)
hc_ring = hamiltonian.HamiltonianMaxCut(ring)
hb = hamiltonian.HamiltonianDriver()
hamiltonians = [hc, hb]
ring_hamiltonians = [hc_ring, hb]
sim = qaoa.SimulateQAOA(g, cost_hamiltonian=hc, hamiltonian=hamiltonians)
sim_ring = qaoa.SimulateQAOA(ring,
cost_hamiltonian=hc_ring,
hamiltonian=ring_hamiltonians)
sim_ket = qaoa.SimulateQAOA(g, cost_hamiltonian=hc, hamiltonian=hamiltonians)
sim_noisy = qaoa.SimulateQAOA(g,
cost_hamiltonian=hc,