def optimize_circuit(params):
"""Minimize the variational circuit and return its minimum value.
The code you write for this challenge should be completely contained within this function
between the # QHACK # comment markers. You should create a device and convert the
variational_circuit function into an executable QNode. Next, you should minimize the variational
circuit using gradient-based optimization to update the input params. Return the optimized value
of the QNode as a single floating-point number.
Args:
params (np.ndarray): Input parameters to be optimized, of dimension 30
Returns:
float: the value of the optimized QNode
"""
optimal_value = 0.0
# QHACK #
# Initialize the device
# dev = ...
dev = qml.device('default.qubit', wires=2)
# Instantiate the QNode
# circuit = qml.QNode(variational_circuit, dev)
circuit = qml.QNode(variational_circuit, dev)
# Minimize the circuit
# Using default params
var = params
opt = AdamOptimizer(stepsize=0.01)
for it in range(250):
var, optimal_value = opt.step_and_cost(circuit, var)
# print("Iter: {:5d} | Cost: {:0.7f} ".format(it, optimal_value))
# print(circuit.draw())
# QHACK #
# Return the value of the minimized QNode
return optimal_value
#
# .. code-block:: none
#
# array([[ 0.08820262, 0.02000786, 0.0489369 , 0.11204466, 0.0933779 ],
# [-0.04886389, 0.04750442, -0.00756786, -0.00516094, 0.02052993],
# [ 0.00720218, 0.07271368, 0.03805189, 0.00608375, 0.02219316],
# [ 0.01668372, 0.07470395, -0.01025791, 0.01565339, -0.04270479]])
#
# Using the Adam optimizer, we update the weights for 500 steps (this
# takes some time). More steps will lead to a better fit.
opt = AdamOptimizer(0.01, beta1=0.9, beta2=0.999)
var = var_init
for it in range(500):
var, _cost = opt.step_and_cost(lambda v: cost(v, X, Y), var)
print("Iter: {:5d} | Cost: {:0.7f} ".format(it, _cost))
##############################################################################
# .. rst-class:: sphx-glr-script-out
#
# Out:
#
# .. code-block:: none
#
# Iter: 0 | Cost: 0.3006065
# Iter: 1 | Cost: 0.2689702
# Iter: 2 | Cost: 0.2472125
# Iter: 3 | Cost: 0.2300139
# Iter: 4 | Cost: 0.2157100
# Iter: 5 | Cost: 0.2035455