Hello, can CircuitQ handle mutual inductive coupling at the moment?

While drawing a flux tunable two island transmon I was not able to implement correctly the shunt capacitance to ground of left and right island.

graph = nx.MultiGraph()
graph.add_edge(1, 2, element='C')
graph.add_edge(1, 2, element='J')
graph.add_edge(1, 2, element='J')
# shunt capacitances to ground
graph.add_edge(0, 1, element='C')
graph.add_edge(2, 3, element='C')

circuit = cq.CircuitQ(graph, ground_nodes= [0,3])
circuit.h

where the error is
Exception: Specified ground node 0 is not an active node.

It seems this error is present in also more complex structures, whenever a ground node is not terminated at some qubit node. One could terminate all the ground nodes of a complex circuit to the same qubit ground node for example, although the graph get a bit confusing and makes the ground_nodes= parameter useless, and I am not sure if it is interpreted correctly since both C will be called C01

I expected Hamiltonians to be invariant under changing the order in which parallel edges are added, but this appears not to be the case.

Is this a change in coordinates somehow, or a bug?

graph.add_edge(0,1, element = 'L')
graph.add_edge(0,1, element = 'J')

circuit = cq.CircuitQ(graph, ground_nodes=[0, 1])
print(circuit.h)

yields

-E_{J010}*cos(\tilde{\Phi}_{010}/\Phi_{o})

While

graph.add_edge(0,1, element = 'J')
graph.add_edge(0,1, element = 'L')

circuit = cq.CircuitQ(graph, ground_nodes=[0, 1])
print(circuit.h)

yields what looks something like a small angle approximation (?) of the above Hamiltonian

-E_{J010} + \tilde{\Phi}_{010}**2/(2*L_{010})

When I calculate the T1 value of the qubit several times with the same hamiltonian, the values decrease in each time.

First, create the fluxonium qubit.

# this cell is extracted from the tutorial section of circuitQ documentation
import circuitq as cq
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
graph = nx.MultiGraph()
graph.add_edge(0,1, element = 'C')
graph.add_edge(0,1, element = 'J')
graph.add_edge(0,1, element = 'L')
circuit = cq.CircuitQ(graph)

# set the circuit parameters
# use the same parameters as the tutorial
EJ = circuit.c_v["E"]/3
phi_ext = np.pi*circuit.phi_0

There are three methods for T1 calculation, get_T1_quasiparticles(), get_T1_charge(), and get_T1_flux(). In my code, everytime I calcurate the T1 with the same hamiltonian, T1_charge and T1_flux decrease. ( I recreate the same hamiltonian in each loop.) I do not know why the values decrease even though I use the same circuit parameters.
The eigenvalues were not changing. Only T1_charge and T1_flux.

T1_qp = []
T1_c = []
T1_f = []
for i in range(15):
    circuit.get_numerical_hamiltonian(401, parameter_values=[False, EJ, False, phi_ext])
    circuit.get_eigensystem()
    
    # T1 caluculation
    T1_qp.append(circuit.get_T1_quasiparticles())
    T1_c.append(circuit.get_T1_charge())
    T1_f.append(circuit.get_T1_flux())

# plot each T1 value
plt.plot(np.arange(15), T1_qp/T1_qp[0],'ro', label = 'T1_qp')
plt.plot(np.arange(15), T1_c/T1_c[0],'go', label = 'T1_c')
plt.plot(np.arange(15), T1_f/T1_f[0],'bx', label = 'T1_f', markersize = 10)
plt.legend()
plt.xlabel('number of loop')
plt.ylabel('T1 proportion')
plt.show()

I also found that I could get the same T1_c and T1_f if we don't recreate the hamiltonian. The same value was calculated even when get_eigensystem() method was done in each loop. The get_numerical_hamiltonian() method, however, made the situation bad, I guess.

T1_qp = []
T1_c = []
T1_f = []
for i in range(15):
    circuit.get_eigensystem()
    # T1 caluculation
    T1_qp.append(circuit.get_T1_quasiparticles())
    T1_c.append(circuit.get_T1_charge())
    T1_f.append(circuit.get_T1_flux())

# plot each T1 value
plt.plot(np.arange(15), T1_qp/T1_qp[0],'ro', label = 'T1_qp')
plt.plot(np.arange(15), T1_c/T1_c[0],'go', label = 'T1_c')
plt.plot(np.arange(15), T1_f/T1_f[0],'bx', label = 'T1_f', markersize = 10)
plt.legend()
plt.xlabel('number of loop')
plt.ylabel('T1 proportion')
plt.show()

Hi,
I have issue with using the visualization function:

`import circuitq as cq
import networkx as nx
import matplotlib.pyplot as plt

Create the graph

graph = nx.MultiGraph()
graph.add_edge(0, 1, element='C')
graph.add_edge(0, 1, element='J')

Create a CircuitQ object

circuit = cq.CircuitQ(graph)

Visualize the circuit

circuitq.functions_file.visualize_circuit_general(circuit, "Macintosh HD/Users/fani/Desktop/circuit.png")`

here is the Error:

AttributeError Traceback (most recent call last)
Cell In[14], line 14
11 circuit = cq.CircuitQ(graph)
13 # Visualize the circuit
---> 14 circuitq.functions_file.visualize_circuit_general(circuit, "Macintosh HD/Users/fani/Desktop/circuit.png")

File /opt/anaconda3/envs/circuitQ1/lib/python3.9/site-packages/circuitq-1.2.1-py3.9.egg/circuitq/functions_file.py:17, in visualize_circuit_general(graph, save_as)
6 """
7 Visualises a circuit by creating a figure to an arbitrary path.
8
(...)
14 Arbitrary figure path
15 """
16 circuit_func = copy.deepcopy(graph)
---> 17 for e in circuit_func.edges.data():
18 e[2]['label'] = e[2]['element']
19 circuit_vis = nx.nx_agraph.to_agraph(circuit_func)

AttributeError: 'CircuitQ' object has no attribute 'edges'

Hi,

On line 637 of core.py, the following expression is used when creating the implemented Hamiltonian:

q_q = sp.symbols('q^{q}_{' + str(node_l) + '}')

I'm wondering if it should be the expression below instead?

q_q = sp.symbols('q^{2}_{' + str(node_l) + '}')

import scipy.sparse as spa
evals, estates = spa.linalg.eigsh(self.h_num, k=self.n_eig, which='SA' ,v0=v0) 

does not work in my installation with the latest scipy and python. The reason is this https://stackoverflow.com/questions/65909046/module-scipy-sparse-has-no-attribute-linalg-error-in-reticulate-virtual-envi

and can be easily fixed by doing

import scipy.sparse.linalg
 evals, estates = scipy.sparse.linalg.eigsh(self.h_num, k=self.n_eig, which='SA',v0=v0)

Hi Philipp!

I noticed CirquitQ breaks with the most recent numpy due to the np.complex attribute being deprecated. From what I understand np.complex128 is available to explicitly specify the datatype.

Relevant part of the traceback:

circuitq\core.py in transform_charge_to_flux(self)
   1207         reversed_subspace_pos = dict(map(reversed, self.subspace_pos.items()))
   1208         length = len(self.estates[:,0])
-> 1209         T = np.ones((length, length), dtype=np.complex)
   1210         for i in range(length):
   1211             for j in range(length):