Probability() usage

#1

According to the documentation, there is a class probability() which will return probabilities of each computational basis state.
Can anybody enligthen me with an example? I cant get it working after some attemps

#3

Hi @Miguel_Fernandez!

There is currently an open PR on our GitHub repo, adding a new prob() measurement function. You can install this branch using pip:

pip install git+https://github.com/XanaduAI/PennyLane.git@prob_fn

Once this branch is installed, you can import this new experimental measurement function and use it in a QNode like so:

import numpy as np
import pennylane as qml
from pennylane.beta import prob

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit(x):
    qml.Hadamard(wires=0)
    qml.RY(x, wires=0)
    qml.RX(x, wires=1)
    qml.CNOT(wires=[0, 1])
    return prob(wires=[0])

Note:

  • prob() must accept a wires argument, which specifies for which wires the marginal probability is returned.

  • This feature is experimental, and might change before the next release!

  • Currently, it only supports the default.qubit device, and the PennyLane-qiskit plugin under some modification, but additional device support will be coming soon.

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#4

Thanks Josh, that made the trick!

1 Like
#5

I am running prob() in a known algorithm, and the results I get are consistent with basis vectors ordering {00,10,01,11}, while I am used to {00,01,10,11}. Is this a desired feature, a bug, or its just by happenstance that I’ve seen this second ordering?

#6

Thanks for the update @Miguel_Fernandez — let us look into this and double check the ordering!

#9

Hi @Miguel_Fernandez, I’ve tried the following minimal example,

dev = qml.device("default.qubit", wires=2)

@qml.qnode(dev)
def circuit():
    qml.Hadamard(wires=1)
    qml.CNOT(wires=[0, 1])
    return prob(wires=[0, 1])

# expected probability, using [00, 01, 10, 11]
# ordering, is [0.5, 0.5, 0, 0]

res = circuit()
expected = np.array([0.5, 0.5, 0, 0])
assert np.allclose(res, expected, atol=tol, rtol=0)

and it seems to agree with [00, 01, 10, 11] ordering:

>>> import numpy as np
>>> H = np.array([[1, 1], [1, -1]])/np.sqrt(2)
>>> CNOT = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1], [0, 0, 1, 0]])
>>> state = CNOT @ np.kron(np.eye(2), H) @ np.array([1, 0, 0, 0])
>>> expected = np.abs(state)**2
>>> expected
array([0.5, 0.5, 0. , 0. ])

Could you share a small example that shows {00,10,01,11}? It might be a edge-case related bug in the code that we are not taking into account.

#10

Thanks for looking into this. When these discrepancies arise, its normally my fault. I am double-debugging my code, but I cant catch it yet. Will let you know in any sense

#11

The first screenshot is a toy Quantum Phase Estimation in Qiskit. The second the same in PennyLane. The only difference being my implementation of the cu1 gate, its matrix definition also included

Captura1
Captura1360×536 30.9 KB

Captura2
Captura21369×635 117 KB

#12

Hi @Miguel_Fernandez, as odd as it looks, I believe PennyLane and Qiskit are returning the exact same result.

The disagreement results from Qiskit not implementing the same convention as commonly seen in quantum information papers and textbook; typically, the top qubit (labelled 0) in a circuit is the leftmost integer within the ket; |q_0 q_1 \cdots q_{n-1} q_n\rangle.

For example, for a 2-qubit system originally in the ground state |00\rangle, applying an X gate to qubit 0 in this convention gives |10\rangle.

This can be observed in PennyLane:

@qml.qnode(dev)
def func():
     qml.PauliX(wires=0)
     return qml.probs(wires=0)

>>> func()
array([0., 0., 1., 0.])

where we are using the ordering (|00\rangle, |01\rangle,|10\rangle,|11\rangle).

Qiskit instead uses the convention such that the top qubit (qubit 0) is now the rightmost integer within the ket: |q_{n-1}q_{n}\cdots q_1q_0\rangle. In this convention, applying an X gate to qubit 0 gives |01\rangle.

Running the exact same program in Qiskit, we can see this reversal:

>>> q = qiskit.QuantumRegister(2, 'q')
>>> c = qiskit.ClassicalRegister(2, 'c')
>>> qpe = qiskit.QuantumCircuit(q, c)
>>> qpe.x(q[0])
>>> backend = qiskit.BasicAer.get_backend('statevector_simulator')
>>> job = qiskit.execute(qpe, backend)
>>> result = job.result()
>>> result.get_statevector().real
array([0., 1., 0., 0.])

where we are again using the ordering (|00\rangle, |01\rangle,|10\rangle,|11\rangle).

To convert from the Qiskit convention to the more standard/textbook convention (as used by PennyLane), all that needs to be done is reverse the basis state or permute the statevector/probabilities :slightly_smiling_face:

#13

Hi Josh, many thanks!
Quite disturbing, I need to revise all my qiskit work until now

#14

No worries Miguel! Note that I noticed a couple of typos in my comment, and I’ve just updated them to be correct.

#15

Note also that this is why the matrix representation of the CNOT gate in Qiskit is non-standard:

CNOT=\begin{bmatrix}1& 0 & 0& 0\\ 0 & 0& 0& 1\\0& 0 & 1& 0 \\ 0 & 1& 0 & 0\end{bmatrix}

Compare this to the standard CNOT representation using the convention used in PennyLane/textbooks:

CNOT=\begin{bmatrix}1& 0 & 0& 0\\ 0 & 1& 0& 0\\0& 0 & 0& 1\\ 0 & 0& 1 & 0\end{bmatrix}

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#16

Hi Josh,

it appears that:
! There is currently an open PR on our GitHub repo, adding a new prob() measurement function. You can install this branch using pip: pip install git+https://github.com/XanaduAI/PennyLane.git@prob_fn Once this branch is installed, you can import this new experimental measuremen…

no longer works, and prob() is also not part of the 0.10 release, at least not that I can find. What’s the status?

#17

Ignore that question. It is probs() and part of the main branch

#18

Hi @RonMeiburg, yes, it got renamed to qml.probs() before the pull request was merged!

Here is a link to the documentation in the latest stable version of PennyLane: https://pennylane.readthedocs.io/en/stable/code/api/pennylane.probs.html

#19

Thanks, I found the docs