Pennylane circuits backend

PennyLane based quantum circuit backend.

Utils

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source

instruction_name_to_pennylane_name

 instruction_name_to_pennylane_name (name:str)

Maps instruction names to PennyLane names.

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source

ParametrizedPennylaneCircuit

 ParametrizedPennylaneCircuit (circuit:pennylane.workflow.qnode.QNode,
                               params:torch.Tensor)

Backend

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source

CircuitsPennylaneBackend

 CircuitsPennylaneBackend ()

A backend for PennyLane.

Test

from genQC.platform.tokenizer.circuits_tokenizer import CircuitTokenizer

genqc <-> backend

tensor = torch.tensor([
                [3, 0, -2, 0, 1],
                [0, 0,  2, 0, 1],
                [0, 3, -2, 3, 0],
            ], dtype=torch.int32)

params_tensor = torch.tensor([       # ... [max_params, time]
                    [1, 1, 1, 1,  0.9],
                ])

vocabulary   = {"cp":1, "ccx":2, "rx":3}
tokenizer    = CircuitTokenizer(vocabulary)
instructions = tokenizer.decode(tensor, params_tensor)

instructions.print()

CircuitInstruction(name='rx', control_nodes=[], target_nodes=[0], params=[12.566370964050293])
CircuitInstruction(name='rx', control_nodes=[], target_nodes=[2], params=[12.566370964050293])
CircuitInstruction(name='ccx', control_nodes=[0, 2], target_nodes=[1], params=[12.566370964050293])
CircuitInstruction(name='rx', control_nodes=[], target_nodes=[2], params=[12.566370964050293])
CircuitInstruction(name='cp', control_nodes=[], target_nodes=[0, 1], params=[11.9380521774292])

backend = CircuitsPennylaneBackend()

qc = backend.genqc_to_backend(instructions, flip_qubit_order=False)
backend.draw(qc);