def test_cost_graph_error(self):
"""Tests that the cost Hamiltonians throw the correct error"""
graph = [(0, 1), (1, 2)]
with pytest.raises(ValueError, match=r"Input graph must be a nx\.Graph"):
qaoa.maxcut(graph)
with pytest.raises(ValueError, match=r"Input graph must be a nx\.Graph"):
qaoa.max_independent_set(graph)
with pytest.raises(ValueError, match=r"Input graph must be a nx\.Graph"):
qaoa.min_vertex_cover(graph)
with pytest.raises(ValueError, match=r"Input graph must be a nx\.Graph"):
qaoa.max_clique(graph)
def test_max_clique_output(self, graph, constrained, cost_hamiltonian, mixer_hamiltonian):
"""Tests that the output of the Maximum Clique method is correct"""
cost_h, mixer_h = qaoa.max_clique(graph, constrained=constrained)
assert decompose_hamiltonian(cost_hamiltonian) == decompose_hamiltonian(cost_h)
assert decompose_hamiltonian(mixer_hamiltonian) == decompose_hamiltonian(mixer_h)
def max_clique_falqon(graph, n, beta_1, delta_t, dev):
comm_h = build_hamiltonian(graph) # Builds the commutator
cost_h, driver_h = qaoa.max_clique(graph, constrained=False) # Builds H_c and H_d
ansatz = build_maxclique_ansatz(cost_h, driver_h, delta_t) # Builds the FALQON ansatz
beta = [beta_1] # Records each value of beta_k
energies = [] # Records the value of the cost function at each step
for i in range(n):
# Creates a function that can evaluate the expectation value of the commutator
cost_fn = qml.ExpvalCost(ansatz, comm_h, dev)
# Creates a function that returns the expectation value of the cost Hamiltonian
cost_fn_energy = qml.ExpvalCost(ansatz, cost_h, dev)
# Adds a value of beta to the list and evaluates the cost function
beta.append(-1 * cost_fn(beta))
energy = cost_fn_energy(beta)
energies.append(energy)
return beta, energies
# where each qubit is a node in the graph, and the states :math:`|0\rangle` and :math:`|1\rangle`
# represent whether the vertex has been marked as part of the clique, as is the case for `most standard QAOA encoding
# schemes <https://arxiv.org/abs/1709.03489>`__.
# Note that :math:`\bar{G}` is the complement of :math:`G`: the graph formed by connecting all nodes that **do not** share
# an edge in :math:`G`.
#
# In addition to defining :math:`H_c`, we also require a driver Hamiltonian :math:`H_d` which does not commute
# with :math:`H_c`. The driver Hamiltonian's role is similar to that of the mixer Hamiltonian in QAOA.
# To keep things simple, we choose a sum over Pauli :math:`X` operations on each qubit:
#
# .. math:: H_d = \displaystyle\sum_{i \in V(G)} X_i.
#
# These Hamiltonians come nicely bundled together in the PennyLane QAOA module:
#
cost_h, driver_h = qaoa.max_clique(graph, constrained=False)
print("Cost Hamiltonian")
print(cost_h)
print("Driver Hamiltonian")
print(driver_h)
######################################################################
# One of the main ingredients in the FALQON algorithm is the operator :math:`i [H_d, H_c]`. In
# the case of MaxClique, we can write down the commutator :math:`[H_d, H_c]` explicitly:
#
# .. math:: [H_d, H_c] = 3 \displaystyle\sum_{k \in V(G)} \displaystyle\sum_{(i, j) \in E(\bar{G})} \big( [X_k, Z_i Z_j] - [X_k, Z_i]
# - [X_k, Z_j] \big) + 3 \displaystyle\sum_{i \in V(G)} \displaystyle\sum_{j \in V(G)} [X_i, Z_j].
#
# There are two distinct commutators that we must calculate, :math:`[X_k, Z_j]` and :math:`[X_k, Z_i Z_j]`.
# This is straightforward as we know exactly what the
