Qibotn

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

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, an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows.
• quimb, an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks.

Installation

To get started:

pip install .

to install the tools and dependencies. A few extras are provided, check pyproject.toml in case you need them.

Sample Codes

Single-Node Example

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 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", platform="cutensornet", runcard=computation_settings
)  # cuQuantum
# qibo.set_backend(backend="qibotn", platform="QuimbBackend", runcard=computation_settings) #quimb


# 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

# 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 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.

mpirun -n 4 -hostfile $node_list python test.py