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VQompress

Methods for state preparation via Quantum compression

Usage:

See the compress.py's main block for an example of a multi-process experimental run (across a grid of parameters), or notebooks/Test compress for a full Jupyter notebook example.

cross_validate_qnn_depth(target_circuit, n_shots, n_iters, n_layers, run=0):

inputs:
target_circuit: QuantumCircuit object encoding your state preparation

n_shots: integer number of samples used when evaluating training circuit on one set of parameters

n_iter:  integer number of SPSA optimization steps (number of parameter updates)

n_layers: the number of layers in the learned quantum circuit. One layer has 
          a tiling of single qubit rotations followed by a tiling of two qubit 
          entangling operations. 
n_runs: number of simulations to run in parrallel
returns:
 xr.Dataset object encoding the parameters and fidelities for various QNN circuit depths

def build_compression_model(registers, model_parameters):

inputs:
registers: QuantumRegisters to be used in compressed circuit

model_parameters: ndarray of parameters for learned model (obtained from cross_validate_qnn_depth)
returns
compressed_circuit: QuantumCircuit object

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qiskit hackathon project. State preparation via Quantum compression

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