69 lines
2.0 KiB
Markdown
69 lines
2.0 KiB
Markdown
Qibotn is the tensor-network translation module for Qibo to support large-scale simulation of quantum circuits and acceleration.
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To get started, `python setup.py install` to install the tools and dependencies.
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# Sample Codes
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## Single Node
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The code below shows an example of how to activate the Cuquantum TensorNetwork backend of Qibo.
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<pre>
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```
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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 3 ways of setting 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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# 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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# computation_settings = {
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# 'MPI_enabled': False,
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# 'MPS_enabled': True,
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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(backend="qibotn", runcard=computation_settings)
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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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</pre>
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## Multi-Node
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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 multi node on 4 GPU in cluster.
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<pre>
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```
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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
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```
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</pre> |