How to get the unitary matrix of a given kernel

[ACE07-Sev]

Greetings there,

Hope all are well. I would like to know how I can get the unitary matrix of a circuit in cudaq. I've seen .sample() and .get_state() for counts and statevector definitions, but haven't been able to find the unitary matrix definition. Thanks in advance!

[bettinaheim]

Tentatively moving this to 0.9 for tracking, but this is not yet confirmed as planned.

[zohimchandani]

+1 for this since we need it for a collaboration, thanks.

[ACE07-Sev]

I would make a few suggestions for this in the interest of performance. To get the unitary we have to do tensor contraction, and it gets expensive real fast as we increase the entanglement. With that being said, I would suggest using sth like cotengra or contengrust to find optimal order of contraction. Frankly, anything by Dr. Gray is usually very good for this type of operation.

Dr. Nguyen mentioned these have to be accessible in C++, so I'll have a look for any alternatives for this in C++.

Ohh, and one thing. It would be great if you don't do this as we add gates. Some packages (I believe qiskit) do this which adds alot of overhead for deep circuits. I imagine it would be better to contract it at the end if the user wants a unitary matrix of the overall circuit. This also makes it easier to use JIT, or GPU-acceleration for performing this.

[ACE07-Sev]

Greetings there,

Hope all are well. May I ask if there's an expected timeline for when this feature will be done?

[zohimchandani]

You can use this hack in the meantime if it helps:

import cudaq 
import numpy as np
from typing import List


num_qubits = 2
input_state = [0.0] * (2**num_qubits)  #just input a zero state 
N = 2**num_qubits
U = np.zeros((N, N), dtype=np.complex128)
params = [1 ,2]

@cudaq.kernel
def kernel(params: List[float], input_state: List[complex]):

    qubits = cudaq.qvector(input_state)
    
    rx(params[0], qubits[0])
    ry(params[1], qubits[1])
    
    y.ctrl(qubits[0], qubits[1])
    

for j in range(N): 
    state_j    = np.zeros((N), dtype=np.complex128) 
    state_j[j] = 1.0
    
    U[:, j] = np.array(cudaq.get_state(kernel, params, state_j), copy=False)
    
print(U)

[ACE07-Sev]

@zohimchandani Thank you!

[ACE07-Sev]

A bit of an unrelated question, sorry (too small to open a ticket for). How can I pass a list of qubits here? It doesn't let me slice nor pass in a list:

# Create a `Kernel` that accepts a qubit as an argument.
# Apply an X-gate on that qubit.
target_kernel, qubit = cudaq.make_kernel(cudaq.qubit)
target_kernel.tdg(qubit)

# Create another `Kernel` that will apply `target_kernel`
# as a controlled operation.
kernel = cudaq.make_kernel()
qubits = kernel.qalloc(3)

# In this case, `control` performs the equivalent of a
# controlled-X gate between `control_qubit` and `target_qubit`.
kernel.control(target_kernel, qubits[:2], qubits[2])

print(cudaq.draw(kernel))

I want to add a controlled Tdg gate, with qubits 0 and 1 being the control indices, and qubit 2 being the target.

[zohimchandani]

See this for an example and let us know if that helps.

It is recommended that you switch to the new way of creating kernels with the @cudaq.kernel decorator as shown in the example.

[ACE07-Sev]

Ohh I have read that. I am wrapping cuda-quantum in my package and I use a UI similar to qiskit, hence why I need the addition of the gates to be in form of methods as opposed to all in one with the decorator. Is there a way around it?