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final-qibotn/src/qibotn/mps_contraction_helper.py
2024-03-04 18:29:35 +08:00

119 lines
5.0 KiB
Python

from cuquantum import contract, contract_path
# Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb
class MPSContractionHelper:
"""A helper class to compute various quantities for a given MPS.
Interleaved format is used to construct the input args for `cuquantum.contract`.
Reference: https://github.com/NVIDIA/cuQuantum/blob/main/python/samples/cutensornet/tn_algorithms/mps_algorithms.ipynb
The following compute quantities are supported:
- the norm of the MPS.
- the equivalent state vector from the MPS.
- the expectation value for a given operator.
- the equivalent state vector after multiplying an MPO to an MPS.
Parameters:
num_qubits: The number of qubits for the MPS.
"""
def __init__(self, num_qubits):
self.num_qubits = num_qubits
self.bra_modes = [(2 * i, 2 * i + 1, 2 * i + 2) for i in range(num_qubits)]
offset = 2 * num_qubits + 1
self.ket_modes = [
(i + offset, 2 * i + 1, i + 1 + offset) for i in range(num_qubits)
]
def contract_norm(self, mps_tensors, options=None):
"""Contract the corresponding tensor network to form the norm of the
MPS.
Parameters:
mps_tensors: A list of rank-3 ndarray-like tensor objects.
The indices of the ith tensor are expected to be bonding index to the i-1 tensor,
the physical mode, and then the bonding index to the i+1th tensor.
options: Specify the contract and decompose options.
Returns:
The norm of the MPS.
"""
interleaved_inputs = []
for i, o in enumerate(mps_tensors):
interleaved_inputs.extend(
[o, self.bra_modes[i], o.conj(), self.ket_modes[i]]
)
interleaved_inputs.append([]) # output
return self._contract(interleaved_inputs, options=options).real
def contract_state_vector(self, mps_tensors, options=None):
"""Contract the corresponding tensor network to form the state vector
representation of the MPS.
Parameters:
mps_tensors: A list of rank-3 ndarray-like tensor objects.
The indices of the ith tensor are expected to be bonding index to the i-1 tensor,
the physical mode, and then the bonding index to the i+1th tensor.
options: Specify the contract and decompose options.
Returns:
An ndarray-like object as the state vector.
"""
interleaved_inputs = []
for i, o in enumerate(mps_tensors):
interleaved_inputs.extend([o, self.bra_modes[i]])
output_modes = tuple([bra_modes[1] for bra_modes in self.bra_modes])
interleaved_inputs.append(output_modes) # output
return self._contract(interleaved_inputs, options=options)
def contract_expectation(
self, mps_tensors, operator, qubits, options=None, normalize=False
):
"""Contract the corresponding tensor network to form the expectation of
the MPS.
Parameters:
mps_tensors: A list of rank-3 ndarray-like tensor objects.
The indices of the ith tensor are expected to be bonding index to the i-1 tensor,
the physical mode, and then the bonding index to the i+1th tensor.
operator: A ndarray-like tensor object.
The modes of the operator are expected to be output qubits followed by input qubits, e.g,
``A, B, a, b`` where `a, b` denotes the inputs and `A, B'` denotes the outputs.
qubits: A sequence of integers specifying the qubits that the operator is acting on.
options: Specify the contract and decompose options.
normalize: Whether to scale the expectation value by the normalization factor.
Returns:
An ndarray-like object as the state vector.
"""
interleaved_inputs = []
extra_mode = 3 * self.num_qubits + 2
operator_modes = [None] * len(qubits) + [self.bra_modes[q][1] for q in qubits]
qubits = list(qubits)
for i, o in enumerate(mps_tensors):
interleaved_inputs.extend([o, self.bra_modes[i]])
k_modes = self.ket_modes[i]
if i in qubits:
k_modes = (k_modes[0], extra_mode, k_modes[2])
q = qubits.index(i)
operator_modes[q] = extra_mode # output modes
extra_mode += 1
interleaved_inputs.extend([o.conj(), k_modes])
interleaved_inputs.extend([operator, tuple(operator_modes)])
interleaved_inputs.append([]) # output
if normalize:
norm = self.contract_norm(mps_tensors, options=options)
else:
norm = 1
return self._contract(interleaved_inputs, options=options) / norm
def _contract(self, interleaved_inputs, options=None):
path = contract_path(*interleaved_inputs, options=options)[0]
return contract(*interleaved_inputs, options=options, optimize={"path": path})