#!/usr/bin/env python
# coding: utf-8

# # Cirq Integration

# This notebook shows a simple example of how to use pyGSTi with Cirq. It has three sections:
# 
# 1. Sets up pyGSTi.
# 2. Shows how pyGSTi circuits can be converted to Cirq circuits.
# 3. Shows how the Cirq circuits can be run and the results loaded back into pyGSTi for analysis.

# In[1]:


import cirq
import pygsti
from pygsti.modelpacks import smq1Q_XYI
import numpy as np
import tqdm


# ## 1. Generate the GST circuits

# ### Make target gate set $\{\sqrt{X},\sqrt{Y},I\}$

# In[2]:


target_model = smq1Q_XYI.target_model()


# ### Preparation and measurement fiducials, germs

# In[3]:


preps = smq1Q_XYI.prep_fiducials()
effects = smq1Q_XYI.meas_fiducials()
germs = smq1Q_XYI.germs()


# ### Construct pyGSTi circuits

# In[4]:


max_lengths = list(np.logspace(0, 10, 11, base=2, dtype=int))


# In[5]:


print(max_lengths)


# In[6]:


pygsti_circuits = pygsti.construction.create_lsgst_circuits(target_model, preps, effects, germs, max_lengths)


# In[7]:


len(pygsti_circuits)


# ## 2. Convert to runable `cirq.Circuit`'s

# ### Setup

# Now, we need to map the qubit names from pyGSTi (`0`, `1`, etc.) into cirq qubits. There's nothing special about `cirq.GridQubit(8, 3)`; it's just an example.

# In[8]:


q0 = cirq.GridQubit(8, 3)
qubit_label_dict = {0: q0}


# ### Testing examples

# Do an example conversion.

# In[9]:


pygsti_circuit = pygsti_circuits[111]
print('pyGSTi:')
print(pygsti_circuit)
print('Cirq:')
print(pygsti_circuit.convert_to_cirq(qubit_label_dict))


# Do another example conversion.

# In[10]:


pygsti_circuit = pygsti_circuits[90]
print('pyGSTi:')
print(pygsti_circuit)
print('Cirq:')
print(pygsti_circuit.convert_to_cirq(qubit_label_dict))


# Now, lets try the same thing but specifing a wait duration for the idle operation.

# In[11]:


wait_duration = cirq.Duration(nanos=100)


# In[12]:


pygsti_circuit = pygsti_circuits[111]
print('pyGSTi:')
print(pygsti_circuit)
print('Cirq:')
print(pygsti_circuit.convert_to_cirq(qubit_label_dict, wait_duration))


# In[13]:


pygsti_circuit = pygsti_circuits[90]
print('pyGSTi:')
print(pygsti_circuit)
print('Cirq:')
print(pygsti_circuit.convert_to_cirq(qubit_label_dict, wait_duration))


# ### The real thing

# Now, convert all the circuits.

# In[14]:


cirq_circuits = [c.convert_to_cirq(qubit_label_dict, wait_duration) for c in tqdm.tqdm(pygsti_circuits)]


# Note that we're missing the measurments, the idle operations don't have a time associated with them, and the first circuit is empty (it's should just be an idle). Otherwise, the results look good, and those things should be easy to fix.

# ## 3. Run the circuits

# Add measurements to the circuits.

# In[15]:


for circuit in cirq_circuits:
    circuit.append(cirq.measure(q0, key='result'))


# Simulate the circuits (or run them on a real quantum computer!)

# In[16]:


simulator = cirq.Simulator()
results = [simulator.run(circuit, repetitions=1000) for circuit in tqdm.tqdm(cirq_circuits)]


# Load everything the results into a pyGSTi dataset.

# In[17]:


dataset = pygsti.objects.dataset.DataSet()
for pygsti_circuit, trial_result in zip(pygsti_circuits, results):
    dataset.add_cirq_trial_result(pygsti_circuit, trial_result, key='result')


# Perform GST.

# In[18]:


gst_results = pygsti.run_stdpractice_gst(dataset, target_model, preps, effects, germs, max_lengths, modes="TP,Target", verbosity=1)


# See what if finds.

# In[19]:


mdl_estimate = gst_results.estimates['TP'].models['stdgaugeopt']
print("2DeltaLogL(estimate, data): ", pygsti.tools.two_delta_logl(mdl_estimate, dataset))
print("2DeltaLogL(ideal, data): ", pygsti.tools.two_delta_logl(target_model, dataset))


# In[ ]: