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150 lines
3.8 KiB
Markdown
150 lines
3.8 KiB
Markdown
# Qibotn
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The tensor network translation module for Qibo to support large-scale simulation of quantum circuits and acceleration.
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## Supported Computation
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Tensor Network Types:
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- Tensornet (TN)
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- Matrix Product States (MPS)
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Tensor Network contractions to:
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- dense vectors
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- expectation values of given Pauli strings or Pauli-sum observables
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The supported HPC configurations are:
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- single-node CPU
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- single-node GPU or GPUs
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- multi-node multi-GPU with Message Passing Interface (MPI)
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- multi-node multi-GPU with NVIDIA Collective Communications Library (NCCL)
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Currently, the supported tensor network libraries are:
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- [cuQuantum](https://github.com/NVIDIA/cuQuantum), an NVIDIA SDK of optimized libraries and tools for accelerating quantum computing workflows.
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- [quimb](https://quimb.readthedocs.io/en/latest/), an easy but fast python library for ‘quantum information many-body’ calculations, focusing primarily on tensor networks.
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## CPU expectation benchmarks
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Use the library APIs directly:
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```py
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import qibotn
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records = qibotn.run_cpu_benchmark_cases(
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ansatz="mps",
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nqubits=40,
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nlayers=10,
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bond=2048,
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circuits=("brickwall_cnot",),
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observables=("ring_xz",),
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)
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```
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For generic TN use `ansatz="tn"`. Contest/custom runners are available as
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`qibotn.run_contest_tn_case`, `qibotn.run_custom_tn_expectation`,
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`qibotn.run_contest_mps_case`, and `qibotn.run_vidal_validation_cases`.
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## Installation
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To get started:
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```sh
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pip install qibotn
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```
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to install the tools and dependencies. A few extras are provided, check `pyproject.toml` in
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case you need them.
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<!-- TODO: describe extras, after Poetry adoption and its groups -->
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## Contribute
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To contribute, please install using poetry:
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```sh
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git clone https://github.com/qiboteam/qibotn.git
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cd qibotn
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poetry install
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```
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## Sample Codes
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### Single-Node Example
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The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo.
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```py
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import numpy as np
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from qibo import Circuit, gates
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import qibo
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# Below shows how to set the computation_settings
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# Note that for MPS_enabled and expectation_enabled parameters the accepted inputs are boolean or a dictionary with the format shown below.
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# If computation_settings is not specified, the default setting is used in which all booleans will be False.
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# This will trigger the dense vector computation of the tensornet.
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computation_settings = {
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"MPI_enabled": False,
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"MPS_enabled": {
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"qr_method": False,
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"svd_method": {
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"partition": "UV",
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"abs_cutoff": 1e-12,
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},
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},
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"NCCL_enabled": False,
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"expectation_enabled": False,
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}
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qibo.set_backend(
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backend="qibotn", platform="cutensornet", runcard=computation_settings
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) # cuQuantum
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# qibo.set_backend(backend="qibotn", platform="qutensornet", runcard=computation_settings) #quimb
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# Construct the circuit
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c = Circuit(2)
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# Add some gates
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c.add(gates.H(0))
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c.add(gates.H(1))
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# Execute the circuit and obtain the final state
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result = c()
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print(result.state())
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```
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Other examples of setting the computation_settings
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```py
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# Expectation computation with specific Pauli String pattern
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computation_settings = {
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"MPI_enabled": False,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": {
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"pauli_string_pattern": "IXZ",
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},
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}
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# Dense vector computation using multi node through MPI
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computation_settings = {
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"MPI_enabled": True,
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"MPS_enabled": False,
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"NCCL_enabled": False,
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"expectation_enabled": False,
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}
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```
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### Multi-Node Example
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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.
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```sh
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mpirun -n 4 -hostfile $node_list python test.py
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```
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