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tankya2
2023-08-17 13:23:29 +08:00
parent 3fafe2b3ff
commit 89bdbfbe68
3 changed files with 89 additions and 77 deletions
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94
src/qibotn/MPSContractionHelper.py
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@@ -1,59 +1,64 @@
from cuquantum import contract, contract_path, CircuitToEinsum, tensor from cuquantum import contract, contract_path, CircuitToEinsum, tensor
class MPSContractionHelper: class MPSContractionHelper:
""" """
A helper class to compute various quantities for a given MPS. A helper class to compute various quantities for a given MPS.
Interleaved format is used to construct the input args for `cuquantum.contract`. Interleaved format is used to construct the input args for `cuquantum.contract`.
A concrete example on how the modes are populated for a 7-site MPS is provided below: A concrete example on how the modes are populated for a 7-site MPS is provided below:
0 2 4 6 8 10 12 14 0 2 4 6 8 10 12 14
bra -----A-----B-----C-----D-----E-----F-----G----- bra -----A-----B-----C-----D-----E-----F-----G-----
| | | | | | | | | | | | | |
1| 3| 5| 7| 9| 11| 13| 1| 3| 5| 7| 9| 11| 13|
| | | | | | | | | | | | | |
ket -----a-----b-----c-----d-----e-----f-----g----- ket -----a-----b-----c-----d-----e-----f-----g-----
15 16 17 18 19 20 21 22 15 16 17 18 19 20 21 22
The follwing compute quantities are supported: The follwing compute quantities are supported:
- the norm of the MPS. - the norm of the MPS.
- the equivalent state vector from the MPS. - the equivalent state vector from the MPS.
- the expectation value for a given operator. - the expectation value for a given operator.
- the equivalent state vector after multiplying an MPO to an MPS. - the equivalent state vector after multiplying an MPO to an MPS.
Note that for the nth MPS tensor (rank-3), the modes of the tensor are expected to be `(i,p,j)` Note that for the nth MPS tensor (rank-3), the modes of the tensor are expected to be `(i,p,j)`
where i denotes the bonding mode with the (n-1)th tensor, p denotes the physical mode for the qubit and where i denotes the bonding mode with the (n-1)th tensor, p denotes the physical mode for the qubit and
j denotes the bonding mode with the (n+1)th tensor. j denotes the bonding mode with the (n+1)th tensor.
Args: Args:
num_qubits: The number of qubits for the MPS. num_qubits: The number of qubits for the MPS.
""" """
def __init__(self, num_qubits): def __init__(self, num_qubits):
self.num_qubits = num_qubits self.num_qubits = num_qubits
self.bra_modes = [(2*i, 2*i+1, 2*i+2) for i in range(num_qubits)] self.bra_modes = [(2 * i, 2 * i + 1, 2 * i + 2) for i in range(num_qubits)]
offset = 2*num_qubits+1 offset = 2 * num_qubits + 1
self.ket_modes = [(i+offset, 2*i+1, i+1+offset) for i in range(num_qubits)] 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): def contract_norm(self, mps_tensors, options=None):
""" """
Contract the corresponding tensor network to form the norm of the MPS. Contract the corresponding tensor network to form the norm of the MPS.
Args: Args:
mps_tensors: A list of rank-3 ndarray-like tensor objects. 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 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. the physical mode, and then the bonding index to the i+1th tensor.
options: Specify the contract and decompose options. options: Specify the contract and decompose options.
Returns: Returns:
The norm of the MPS. The norm of the MPS.
""" """
interleaved_inputs = [] interleaved_inputs = []
for i, o in enumerate(mps_tensors): for i, o in enumerate(mps_tensors):
interleaved_inputs.extend([o, self.bra_modes[i], o.conj(), self.ket_modes[i]]) interleaved_inputs.extend(
interleaved_inputs.append([]) # output [o, self.bra_modes[i], o.conj(), self.ket_modes[i]]
)
interleaved_inputs.append([]) # output
return self._contract(interleaved_inputs, options=options).real return self._contract(interleaved_inputs, options=options).real
def contract_state_vector(self, mps_tensors, options=None): def contract_state_vector(self, mps_tensors, options=None):
@@ -61,10 +66,10 @@ class MPSContractionHelper:
Contract the corresponding tensor network to form the state vector representation of the MPS. Contract the corresponding tensor network to form the state vector representation of the MPS.
Args: Args:
mps_tensors: A list of rank-3 ndarray-like tensor objects. 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 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. the physical mode, and then the bonding index to the i+1th tensor.
options: Specify the contract and decompose options. options: Specify the contract and decompose options.
Returns: Returns:
An ndarray-like object as the state vector. An ndarray-like object as the state vector.
@@ -73,28 +78,30 @@ class MPSContractionHelper:
for i, o in enumerate(mps_tensors): for i, o in enumerate(mps_tensors):
interleaved_inputs.extend([o, self.bra_modes[i]]) interleaved_inputs.extend([o, self.bra_modes[i]])
output_modes = tuple([bra_modes[1] for bra_modes in self.bra_modes]) output_modes = tuple([bra_modes[1] for bra_modes in self.bra_modes])
interleaved_inputs.append(output_modes) # output interleaved_inputs.append(output_modes) # output
return self._contract(interleaved_inputs, options=options) return self._contract(interleaved_inputs, options=options)
def contract_expectation(self, mps_tensors, operator, qubits, options=None, normalize=False): def contract_expectation(
self, mps_tensors, operator, qubits, options=None, normalize=False
):
""" """
Contract the corresponding tensor network to form the state vector representation of the MPS. Contract the corresponding tensor network to form the state vector representation of the MPS.
Args: Args:
mps_tensors: A list of rank-3 ndarray-like tensor objects. 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 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. the physical mode, and then the bonding index to the i+1th tensor.
operator: A ndarray-like tensor object. operator: A ndarray-like tensor object.
The modes of the operator are expected to be output qubits followed by input qubits, e.g, 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. ``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. qubits: A sequence of integers specifying the qubits that the operator is acting on.
options: Specify the contract and decompose options. options: Specify the contract and decompose options.
normalize: Whether to scale the expectation value by the normalization factor. normalize: Whether to scale the expectation value by the normalization factor.
Returns: Returns:
An ndarray-like object as the state vector. An ndarray-like object as the state vector.
""" """
interleaved_inputs = [] interleaved_inputs = []
extra_mode = 3 * self.num_qubits + 2 extra_mode = 3 * self.num_qubits + 2
operator_modes = [None] * len(qubits) + [self.bra_modes[q][1] for q in qubits] operator_modes = [None] * len(qubits) + [self.bra_modes[q][1] for q in qubits]
@@ -105,19 +112,18 @@ class MPSContractionHelper:
if i in qubits: if i in qubits:
k_modes = (k_modes[0], extra_mode, k_modes[2]) k_modes = (k_modes[0], extra_mode, k_modes[2])
q = qubits.index(i) q = qubits.index(i)
operator_modes[q] = extra_mode # output modes operator_modes[q] = extra_mode # output modes
extra_mode += 1 extra_mode += 1
interleaved_inputs.extend([o.conj(), k_modes]) interleaved_inputs.extend([o.conj(), k_modes])
interleaved_inputs.extend([operator, tuple(operator_modes)]) interleaved_inputs.extend([operator, tuple(operator_modes)])
interleaved_inputs.append([]) # output interleaved_inputs.append([]) # output
if normalize: if normalize:
norm = self.contract_norm(mps_tensors, options=options) norm = self.contract_norm(mps_tensors, options=options)
else: else:
norm = 1 norm = 1
return self._contract(interleaved_inputs, options=options) / norm return self._contract(interleaved_inputs, options=options) / norm
def _contract(self, interleaved_inputs, options=None):
def _contract(self, interleaved_inputs, options=None):
path = contract_path(*interleaved_inputs, options=options)[0] path = contract_path(*interleaved_inputs, options=options)[0]
return contract(*interleaved_inputs, options=options, optimize={'path':path}) return contract(*interleaved_inputs, options=options, optimize={"path": path})

69
src/qibotn/MPSUtils.py
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@@ -2,73 +2,80 @@ import cupy as cp
from cuquantum.cutensornet.experimental import contract_decompose from cuquantum.cutensornet.experimental import contract_decompose
from cuquantum import contract from cuquantum import contract
def initial(num_qubits, dtype): def initial(num_qubits, dtype):
""" """
Generate the MPS with an initial state of |00...00> Generate the MPS with an initial state of |00...00>
""" """
state_tensor = cp.asarray([1, 0], dtype=dtype).reshape(1,2,1) state_tensor = cp.asarray([1, 0], dtype=dtype).reshape(1, 2, 1)
mps_tensors = [state_tensor] * num_qubits mps_tensors = [state_tensor] * num_qubits
return mps_tensors return mps_tensors
def mps_site_right_swap(
mps_tensors, def mps_site_right_swap(mps_tensors, i, **kwargs):
i,
**kwargs
):
""" """
Perform the swap operation between the ith and i+1th MPS tensors. Perform the swap operation between the ith and i+1th MPS tensors.
""" """
# contraction followed by QR decomposition # contraction followed by QR decomposition
a, _, b = contract_decompose('ipj,jqk->iqj,jpk', *mps_tensors[i:i+2], algorithm=kwargs.get('algorithm',None), options=kwargs.get('options',None)) a, _, b = contract_decompose(
mps_tensors[i:i+2] = (a, b) "ipj,jqk->iqj,jpk",
*mps_tensors[i : i + 2],
algorithm=kwargs.get("algorithm", None),
options=kwargs.get("options", None)
)
mps_tensors[i : i + 2] = (a, b)
return mps_tensors return mps_tensors
def apply_gate(
mps_tensors, def apply_gate(mps_tensors, gate, qubits, **kwargs):
gate,
qubits,
**kwargs
):
""" """
Apply the gate operand to the MPS tensors in-place. Apply the gate operand to the MPS tensors in-place.
Args: Args:
mps_tensors: A list of rank-3 ndarray-like tensor objects. mps_tensors: A list of rank-3 ndarray-like tensor objects.
The indices of the ith tensor are expected to be the bonding index to the i-1 tensor, The indices of the ith tensor are expected to be the bonding index to the i-1 tensor,
the physical mode, and then the bonding index to the i+1th tensor. the physical mode, and then the bonding index to the i+1th tensor.
gate: A ndarray-like tensor object representing the gate operand. gate: A ndarray-like tensor object representing the gate operand.
The modes of the gate is expected to be output qubits followed by input qubits, e.g, The modes of the gate is 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. ``A, B, a, b`` where ``a, b`` denotes the inputs and ``A, B`` denotes the outputs.
qubits: A sequence of integers denoting the qubits that the gate is applied onto. qubits: A sequence of integers denoting the qubits that the gate is applied onto.
algorithm: The contract and decompose algorithm to use for gate application. algorithm: The contract and decompose algorithm to use for gate application.
Can be either a `dict` or a `ContractDecomposeAlgorithm`. Can be either a `dict` or a `ContractDecomposeAlgorithm`.
options: Specify the contract and decompose options. options: Specify the contract and decompose options.
Returns: Returns:
The updated MPS tensors. The updated MPS tensors.
""" """
n_qubits = len(qubits) n_qubits = len(qubits)
if n_qubits == 1: if n_qubits == 1:
# single-qubit gate # single-qubit gate
i = qubits[0] i = qubits[0]
mps_tensors[i] = contract('ipj,qp->iqj', mps_tensors[i], gate, options=kwargs.get('options',None)) # in-place update mps_tensors[i] = contract(
"ipj,qp->iqj", mps_tensors[i], gate, options=kwargs.get("options", None)
) # in-place update
elif n_qubits == 2: elif n_qubits == 2:
# two-qubit gate # two-qubit gate
i, j = qubits i, j = qubits
if i > j: if i > j:
# swap qubits order # swap qubits order
return apply_gate(mps_tensors, gate.transpose(1,0,3,2), (j, i), **kwargs) return apply_gate(mps_tensors, gate.transpose(1, 0, 3, 2), (j, i), **kwargs)
elif i+1 == j: elif i + 1 == j:
# two adjacent qubits # two adjacent qubits
a, _, b = contract_decompose('ipj,jqk,rspq->irj,jsk', *mps_tensors[i:i+2], gate, algorithm=kwargs.get('algorithm',None), options=kwargs.get('options',None)) a, _, b = contract_decompose(
mps_tensors[i:i+2] = (a, b) # in-place update "ipj,jqk,rspq->irj,jsk",
*mps_tensors[i : i + 2],
gate,
algorithm=kwargs.get("algorithm", None),
options=kwargs.get("options", None)
)
mps_tensors[i : i + 2] = (a, b) # in-place update
else: else:
# non-adjacent two-qubit gate # non-adjacent two-qubit gate
# step 1: swap i with i+1 # step 1: swap i with i+1
mps_site_right_swap(mps_tensors, i, **kwargs) mps_site_right_swap(mps_tensors, i, **kwargs)
# step 2: apply gate to (i+1, j) pair. This amounts to a recursive swap until the two qubits are adjacent # step 2: apply gate to (i+1, j) pair. This amounts to a recursive swap until the two qubits are adjacent
apply_gate(mps_tensors, gate, (i+1, j), **kwargs) apply_gate(mps_tensors, gate, (i + 1, j), **kwargs)
# step 3: swap back i and i+1 # step 3: swap back i and i+1
mps_site_right_swap(mps_tensors, i, **kwargs) mps_site_right_swap(mps_tensors, i, **kwargs)
else: else:

3
src/qibotn/QiboCircuitConvertor.py
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@@ -44,8 +44,7 @@ class QiboCircuitToEinsum:
for key in qubits_frontier: for key in qubits_frontier:
out_list.append(qubits_frontier[key]) out_list.append(qubits_frontier[key])
operand_exp_interleave = [x for y in zip( operand_exp_interleave = [x for y in zip(operands, mode_labels) for x in y]
operands, mode_labels) for x in y]
operand_exp_interleave.append(out_list) operand_exp_interleave.append(out_list)
return operand_exp_interleave return operand_exp_interleave