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Grouped codes to higher level functions

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This commit is contained in:
tankya2
2023-04-21 16:20:49 +08:00
parent 0eb186b43b
commit fa11a0ffac
1 changed files with 37 additions and 37 deletions
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74
src/qibotn/QiboCircuitConvertor.py
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@@ -19,33 +19,13 @@ class QiboCircuitToEinsum:
def __init__(self, circuit, dtype="complex128"):
self.backend = cp
self.dtype = getattr(self.backend, dtype)
self.gate_tensors = []
gates_qubits = []
for gate in circuit.queue:
gate_qubits = gate.control_qubits + gate.target_qubits
gates_qubits.extend(gate_qubits)
# self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on
# https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32
required_shape = self.op_shape_from_qubits(len(gate_qubits))
self.gate_tensors.append(
(
cp.asarray(gate.matrix).reshape(required_shape),
gate_qubits,
)
)
# self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit.
self.active_qubits = np.unique(gates_qubits)
self.init_basis_map(self.backend, dtype)
self.init_intermediate_circuit(circuit)
def state_vector_operands(self):
input_tensor_count = len(self.active_qubits)
input_bitstring = "0" * len(self.active_qubits)
input_operands = self._get_bitstring_tensors(
"0" * input_tensor_count, self.dtype, backend=self.backend
)
input_operands = self._get_bitstring_tensors(input_bitstring)
(
mode_labels,
@@ -69,21 +49,13 @@ class QiboCircuitToEinsum:
return operand_exp_interleave
def _init_mode_labels_from_qubits(self, qubits):
frontier_dict = {}
n = len(qubits)
for x in range(n):
frontier_dict[qubits[x]] = x
return [[i] for i in range(n)], frontier_dict, n
frontier_dict = {qubits[x]: x for x in range(n)}
mode_labels = [[i] for i in range(n)]
return mode_labels, frontier_dict, n
def _get_bitstring_tensors(self, bitstring, dtype=np.complex128, backend=cp):
asarray = backend.asarray
state_0 = asarray([1, 0], dtype=dtype)
state_1 = asarray([0, 1], dtype=dtype)
basis_map = {"0": state_0, "1": state_1}
operands = [basis_map[ibit] for ibit in bitstring]
return operands
def _get_bitstring_tensors(self, bitstring):
return [self.basis_map[ibit] for ibit in bitstring]
def _parse_gates_to_mode_labels_operands(
self, gates, qubits_frontier, next_frontier
@@ -108,3 +80,31 @@ class QiboCircuitToEinsum:
(qubit_states,input_output) * qubits_involved
"""
return (2, 2) * nqubits
def init_intermediate_circuit(self, circuit):
self.gate_tensors = []
gates_qubits = []
for gate in circuit.queue:
gate_qubits = gate.control_qubits + gate.target_qubits
gates_qubits.extend(gate_qubits)
# self.gate_tensors is to extract into a list the gate matrix together with the qubit id that it is acting on
# https://github.com/NVIDIA/cuQuantum/blob/6b6339358f859ea930907b79854b90b2db71ab92/python/cuquantum/cutensornet/_internal/circuit_parser_utils_cirq.py#L32
required_shape = self.op_shape_from_qubits(len(gate_qubits))
self.gate_tensors.append(
(
cp.asarray(gate.matrix).reshape(required_shape),
gate_qubits,
)
)
# self.active_qubits is to identify qubits with at least 1 gate acting on it in the whole circuit.
self.active_qubits = np.unique(gates_qubits)
def init_basis_map(self, backend, dtype):
asarray = backend.asarray
state_0 = asarray([1, 0], dtype=dtype)
state_1 = asarray([0, 1], dtype=dtype)
self.basis_map = {"0": state_0, "1": state_1}