Qibotn is the tensor-network translation module for Qibo to support large-scale simulation of quantum circuits and acceleration.

To get started, python setup.py install to install the tools and dependencies.

Sample Codes

Single Node

The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo.

```
import numpy as np
from qibo import Circuit, gates
import qibo

# Below shows 3 ways of setting the computation_settings
# Note that for MPS_enabled and expectation_enabled parameters the accepted inputs are boolean or a dictionary with the format shown below.
# If computation_settings is not specified, the default setting is used in which all booleans will be False. 
# 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
}

# computation_settings = {
#    'MPI_enabled': False,
#    'MPS_enabled': False,
#    'NCCL_enabled': False,
#    'expectation_enabled': {
#        'pauli_string_pattern': "IXZ"
# }

# computation_settings = {
#     'MPI_enabled': False,
#     'MPS_enabled': True,
#     'NCCL_enabled': False,
#     'expectation_enabled': False
# }

qibo.set_backend(backend="qibotn", runcard=computation_settings)

# Construct the circuit
c = Circuit(2)
# Add some gates
c.add(gates.H(0))
c.add(gates.H(1))

# Execute the circuit and obtain the final state
result = c()

print(result.state())
```

Multi-Node

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 multi node on 4 GPU in cluster.

```
mpirun -n 4 --mca opal_common_ucx_opal_mem_hooks 1 --mca orte_base_help_aggregate 0 -mca btl ^openib  -hostfile $node_list python test.py
```