I was chatting with chatGPT today and parsed down these ideas.

I was given an example of code that could do quantum cross validation using qiskit using QVC coupled with sklearnâ€™s linearmodel to do Quantum Cross Validation derived model and error metrics. I then developed an idea around how to build a quantum transformer. The response was to build one on a quantum computer which can be used as classical â€˜quantumâ€™ transformers as standins for qbits for functions like QVC.

I donâ€™t have qiskit [setup], but I thought it would be interesting to try this out and maybe have quantum transformers (which are already being used in the 5g realm https://quantumtransformers.com/). Iâ€™m thinking of the research potential such as for doing quantum cross validation, maybe somehow modelling a wormhole in a classical computer.

I even have sample code for how to develop a quantum transformer, but I donâ€™t have qiskit.

#Qiskit provides a template for creating a classical transformer using their QuantumCircuit class. The following code example shows how to construct a classical transformer in Python using the Qiskit library:

```
from qiskit import QuantumCircuit
# Create the quantum circuit with 3 qubits and 3 classical bits
qc = QuantumCircuit(3, 3)
# Add the gates to generate a "classical transformer"
qc.h(0)
qc.cx(0, 1)
qc.cx(1, 2)
qc.measure([0,1,2], [0,1,2])
# Execute the quantum circuit and get the result
result = qc.execute()
# Print out the results
print(result.get_counts())
```

Here is the code I boiled down for quantum cross validation

def qcv(df):

## import relevant libraries

from qiskit import Aer,execute

from qiskit.aqua.algorithms.classifiers.vqc import VQC

from sklearn.linear_model import LinearRegression## define input as all variables except forecast

X = df.drop(â€˜forecastâ€™,axis=1)

## define output as forecast

y = df[â€˜forecastâ€™]

## set the backend and create a quantum instance

backend = Aer.get_backend(â€˜qasm_simulatorâ€™)

quantum_instance = QuantumInstance(backend,shots=100)## create an instance of VQC using the model and quantum instance

vqc_model = VQC(LinearRegression(),quantum_instance=quantum_instance)

## fit the model on the data

vqc_model.fit(X,y)

## return the optimal parameters

return vqc_model.optimal_params

If itâ€™s wrong, itâ€™s wrong, but I know it can put together python code pretty effectively for smaller concepts, and coding a transformer. I donâ€™t know how hard that is, but maybe this is kind of silly because a quantum state is like random noise maybe to a variable on a quantum system, I donâ€™t know. Maybe itâ€™s making wild guesses, but itâ€™s using what it knows about qiskit and those are real functions.