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.

# Supported Computation

Tensor network contractions to:
- dense vectors
- expecation values of given Pauli string

The supported 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.

# Sample Codes
## Single Node
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
import qibo

# Below shows how to set 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
}


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())
```

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"
}

# Dense vector computation using multi node through MPI
computation_settings = {
    'MPI_enabled': True,
    'MPS_enabled': False,
    'NCCL_enabled': False,
    'expectation_enabled': False
}
```

## 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 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
```