Quantum Transformer?
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 sklearn’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 ‘quantum’ transformers as standins for qbits for functions like QVC.I don’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 realmhttps://quantumtransformers.com/). I’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 don’t have qiskit.#Qiskitprovides 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 validationdef qcv(df):import relevant librariesfrom qiskit import Aer,executefrom qiskit.aqua.algorithms.classifiers.vqc import VQCfrom sklearn.linear_model import LinearRegressiondefine input as all variables except forecastX = df.drop(‘forecast’,axis=1)define output as forecasty = df[‘forecast’]set the backend and create a quantum instancebackend = Aer.get_backend(‘qasm_simulator’)quantum_instance = QuantumInstance(backend,shots=100)create an instance of VQC using the model and quantum instancevqc_model = VQC(LinearRegression(),quantum_instance=quantum_instance)fit the model on the datavqc_model.fit(X,y)return the optimal parametersreturn vqc_model.optimal_paramsIf it’s wrong, it’s wrong, but I know it can put together python code pretty effectively for smaller concepts, and coding a transformer.  I don’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 don’t know.  Maybe it’s making wild guesses, but it’s using what it knows about qiskit and those are real functions.