chore: Pre-commit all files once more

README.md

@@ -5,26 +5,33 @@ To get started, `python setup.py install` to install the tools and dependencies.
 # Supported Computation
 
 Tensor Network Types:
+
 - Tensornet (TN)
 - Matrix Product States (MPS)
 
 Tensor Network contractions to:
+
 - dense vectors
 - expecation values of given Pauli string
 
 The supported HPC configurations are:
+
 - single-node CPU
 - single-node GPU or GPUs
 - multi-node multi-GPU with Message Passing Interface (MPI)
 - multi-node multi-GPU with NVIDIA Collective Communications Library (NCCL)
 
 Currently, the supported tensor network libraries are:
- - [cuQuantum](https://github.com/NVIDIA/cuQuantum), an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows.
- - [quimb](https://quimb.readthedocs.io/en/latest/), an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks.
+
+- [cuQuantum](https://github.com/NVIDIA/cuQuantum), an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows.
+- [quimb](https://quimb.readthedocs.io/en/latest/), an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks.
 
 # Sample Codes
+
 ## Single-Node Example
+
 The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo.
+
 ```py
 import numpy as np
 from qibo import Circuit, gates
@@ -36,20 +43,22 @@ import qibo
 # This will trigger the dense vector computation of the tensornet.
 
 computation_settings = {
-    'MPI_enabled': False,
-    'MPS_enabled': {
-                "qr_method": False,
-                "svd_method": {
-                    "partition": "UV",
-                    "abs_cutoff": 1e-12,
-                },
-            } ,
-    'NCCL_enabled': False,
-    'expectation_enabled': False
+    "MPI_enabled": False,
+    "MPS_enabled": {
+        "qr_method": False,
+        "svd_method": {
+            "partition": "UV",
+            "abs_cutoff": 1e-12,
+        },
+    },
+    "NCCL_enabled": False,
+    "expectation_enabled": False,
 }
 
 
-qibo.set_backend(backend="qibotn", platform="cutensornet", runcard=computation_settings)  #cuQuantum
+qibo.set_backend(
+    backend="qibotn", platform="cutensornet", runcard=computation_settings
+)  # cuQuantum
 # qibo.set_backend(backend="qibotn", platform="qutensornet", runcard=computation_settings) #quimb
 
 
@@ -70,25 +79,26 @@ Other examples of setting the computation_settings
 ```py
 # Expectation computation with specific Pauli String pattern
 computation_settings = {
-   'MPI_enabled': False,
-   'MPS_enabled': False,
-   'NCCL_enabled': False,
-   'expectation_enabled': {
-       'pauli_string_pattern': "IXZ"
+    "MPI_enabled": False,
+    "MPS_enabled": False,
+    "NCCL_enabled": False,
+    "expectation_enabled": {
+        "pauli_string_pattern": "IXZ",
+    },
 }
 
 # Dense vector computation using multi node through MPI
 computation_settings = {
-    'MPI_enabled': True,
-    'MPS_enabled': False,
-    'NCCL_enabled': False,
-    'expectation_enabled': False
+    "MPI_enabled": True,
+    "MPS_enabled": False,
+    "NCCL_enabled": False,
+    "expectation_enabled": False,
 }
 ```
 
 ## Multi-Node Example
-Multi-node is enabled by setting either the MPI or NCCL enabled flag to True in the computation settings. Below shows the script to launch on 2 nodes with 2 GPUs each. $node_list contains the IP of the nodes assigned.
 
+Multi-node is enabled by setting either the MPI or NCCL enabled flag to True in the computation settings. Below shows the script to launch on 2 nodes with 2 GPUs each. $node_list contains the IP of the nodes assigned.
 
 ```sh
 mpirun -n 4 -hostfile $node_list python test.py

src/qibotn/backends/cpu.py

@@ -1,8 +1,6 @@
-import numpy as np
-
 from qibo.backends.numpy import NumpyBackend
-from qibo.states import CircuitResult
 from qibo.config import raise_error
+from qibo.states import CircuitResult
 
 
 class QuTensorNet(NumpyBackend):
@@ -60,7 +58,6 @@ class QuTensorNet(NumpyBackend):
 
         Returns:
             xxx.
-
         """
 
         import qibotn.eval_qu as eval

src/qibotn/eval_qu.py

@@ -3,9 +3,15 @@ import quimb.tensor as qtn
 from qibo.models import Circuit as QiboCircuit
 
 
-def from_qibo(circuit: QiboCircuit, is_mps: False, psi0=None, method='svd',
-              cutoff=1e-6, cutoff_mode='abs'):
-    """Create a tensornetwork representation of the circuit"""
+def from_qibo(
+    circuit: QiboCircuit,
+    is_mps: False,
+    psi0=None,
+    method="svd",
+    cutoff=1e-6,
+    cutoff_mode="abs",
+):
+    """Create a tensornetwork representation of the circuit."""
 
     nqubits = circuit.nqubits
     gate_opt = {}
@@ -30,19 +36,17 @@ def from_qibo(circuit: QiboCircuit, is_mps: False, psi0=None, method='svd',
 
 
 def init_state_tn(nqubits, init_state_sv):
-
-    """Create a matrixproductstate directly from a dense vector"""
+    """Create a matrixproductstate directly from a dense vector."""
 
     dims = tuple(2 * np.ones(nqubits, dtype=int))
 
     return qtn.tensor_1d.MatrixProductState.from_dense(init_state_sv, dims)
 
 
-def dense_vector_tn_qu(qasm: str, initial_state, is_mps,  backend="numpy"):
-    """Evaluate QASM with Quimb
+def dense_vector_tn_qu(qasm: str, initial_state, is_mps, backend="numpy"):
+    """Evaluate QASM with Quimb.
 
     backend (quimb): numpy, cupy, jax. Passed to ``opt_einsum``.
-
     """
     circuit = QiboCircuit.from_qasm(qasm)
     if initial_state is not None:

tests/test_quimb_backend.py

@@ -1,5 +1,6 @@
 import copy
 import os
+
 import config
 import numpy as np
 import pytest
@@ -8,8 +9,7 @@ from qibo.models import QFT
 
 
 def create_init_state(nqubits):
-    init_state = np.random.random(2**nqubits) + \
-        1j * np.random.random(2**nqubits)
+    init_state = np.random.random(2**nqubits) + 1j * np.random.random(2**nqubits)
     init_state = init_state / np.sqrt((np.abs(init_state) ** 2).sum())
     return init_state
 
@@ -20,10 +20,11 @@ def qibo_qft(nqubits, init_state, swaps):
     return circ_qibo, state_vec
 
 
-@pytest.mark.parametrize("nqubits, tolerance, is_mps",
-                         [(1, 1e-6, True), (2, 1e-6, False), (5, 1e-3, True), (10, 1e-3, False)])
+@pytest.mark.parametrize(
+    "nqubits, tolerance, is_mps",
+    [(1, 1e-6, True), (2, 1e-6, False), (5, 1e-3, True), (10, 1e-3, False)],
+)
 def test_eval(nqubits: int, tolerance: float, is_mps: bool):
-
     """Evaluate circuit with Quimb backend.
 
     Args:
@@ -41,20 +42,18 @@ def test_eval(nqubits: int, tolerance: float, is_mps: bool):
     init_state_tn = copy.deepcopy(init_state)
 
     # Test qibo
-    qibo.set_backend(backend=config.qibo.backend,
-                     platform=config.qibo.platform)
-   
-    qibo_circ, result_sv= qibo_qft(nqubits, init_state, swaps=True)
-    
+    qibo.set_backend(backend=config.qibo.backend, platform=config.qibo.platform)
+
+    qibo_circ, result_sv = qibo_qft(nqubits, init_state, swaps=True)
 
     # Convert to qasm for other backends
     qasm_circ = qibo_circ.to_qasm()
 
     # Test quimb
     result_tn = qibotn.eval_qu.dense_vector_tn_qu(
-            qasm_circ, init_state_tn, is_mps, backend=config.quimb.backend
-        ).flatten()
-   
+        qasm_circ, init_state_tn, is_mps, backend=config.quimb.backend
+    ).flatten()
 
-    assert np.allclose(result_sv, result_tn,
-                       atol=tolerance), "Resulting dense vectors do not match"
+    assert np.allclose(
+        result_sv, result_tn, atol=tolerance
+    ), "Resulting dense vectors do not match"