quantum-circuit-simulator.yaml

# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

---
blueprint_name: quantum-circuit

# Please review https://cloud.google.com/compute/docs/regions-zones
# for availability of A2 machine types
vars:
  project_id:  ## Set project id here
  deployment_name: qsim-demo
  region: us-central1
  zone: us-central1-f

# Documentation for each of the modules used below can be found at
# https://github.com/GoogleCloudPlatform/hpc-toolkit/blob/main/modules/README.md

deployment_groups:
- group: primary
  modules:
  - id: network1
    source: modules/network/vpc
  - id: quantum-simulator-setup
    source: modules/scripts/startup-script
    settings:
      runners:
      - type: shell
        destination: install-qsim.sh
        content: |
          #!/bin/bash
          # This script implements https://quantumai.google/qsim/tutorials/gcp_gpu
          set -e -o pipefail
          curl -O https://raw.githubusercontent.com/GoogleCloudPlatform/compute-gpu-installation/main/linux/install_gpu_driver.py
          python3 install_gpu_driver.py
          curl -O https://repo.anaconda.com/miniconda/Miniconda3-py39_4.12.0-Linux-x86_64.sh
          bash Miniconda3-py39_4.12.0-Linux-x86_64.sh -b -p /opt/conda
          source /opt/conda/bin/activate base
          conda init --system
          # following channel ordering is important! use strict_priority!
          # cuquantum comes from cuquantum label in nvidia channel
          # libcutensor comes from main (default) label in nvidia channel
          # cuda and all toolkit comes from cuda-11.5.2 label in nvidia channel
          # everything else comes from conda-forge
          conda config --system --set channel_priority strict
          conda config --system --remove channels defaults
          conda config --system --add channels conda-forge
          conda config --system --add channels nvidia
          conda config --system --add channels nvidia/label/cuda-11.5.2
          conda config --system --add channels nvidia/label/cuquantum-22.07.1
          conda update -n base conda --yes
          conda create -n qsim python=3.9 --yes
          conda install -n qsim cuda cuquantum make cmake cxx-compiler=1.5.1 --yes
          echo "cuda ==11.5.*" > /opt/conda/envs/qsim/conda-meta/pinned
          conda clean -p -t --yes
          conda activate qsim
          pip install pybind11 cirq
          git clone https://github.com/quantumlib/qsim.git /opt/qsim
          cd /opt/qsim
          export CUQUANTUM_DIR=/opt/conda/envs/qsim
          make
          pip install .
      - type: data
        destination: /var/tmp/qsim-example.py
        content: |
          import sys
          import time
          import cirq, cirq_google
          import qsimcirq

          def sim(width: int, height: int, reps: int, use_gpu: bool, gpu_mode: int):
              rqc_fn = cirq.experiments.random_rotations_between_grid_interaction_layers_circuit
              qvm_fn = cirq_google.engine.create_default_noisy_quantum_virtual_machine

              qubits = cirq.GridQubit.rect(width, height, 3, 2)
              circuit = rqc_fn(qubits, depth=10, seed=0) + cirq.measure(*qubits, key="final_state")

              processor_id = "weber"
              qsim_options = qsimcirq.QSimOptions(use_gpu=use_gpu, gpu_mode=gpu_mode)
              # we do not recommend using seed=0 in production usage; in this
              # example it helps compare performance between runs
              qvm = qvm_fn(processor_id, qsimcirq.QSimSimulator, seed=0, qsim_options=qsim_options)

              start = time.time()
              results = qvm.get_sampler(processor_id).run(circuit, repetitions=reps)
              print(results)
              print(f"elapsed: {time.time() - start:.03f}s")


          if __name__ == "__main__":
              width, height, reps = 5, 5, 10

              print("This series of simulations should last approximately 1 minute on an A2 series VM\n")
              print("Running on CPU:")
              sys.stdout.flush()
              sim(width=width, height=height, reps=reps, use_gpu=False, gpu_mode=0)
              print("\nRunning on GPU (CUDA):")
              sys.stdout.flush()
              sim(width=width, height=height, reps=reps, use_gpu=True, gpu_mode=0)
              print("\nRunning on GPU (CUDA + cuQuantum):")
              sys.stdout.flush()
              sim(width=width, height=height, reps=reps, use_gpu=True, gpu_mode=1)
      - type: shell
        destination: run-qsim.sh
        content: |
          #!/bin/bash -i
          # The -i above (for interactive) is required so that conda command will be accessible.
          # this script demonstrates how to run the qsim example application and
          # also "warms up" the GPU to give reliable performance metrics
          conda activate qsim
          python /var/tmp/qsim-example.py
  - id: qsimvm
    source: modules/compute/vm-instance
    use:
    - network1
    - quantum-simulator-setup
    settings:
      machine_type: n1-standard-32
      guest_accelerator:
      - type: nvidia-tesla-t4
        count: 1
      instance_image:
        project: ubuntu-os-cloud
        family: ubuntu-2004-lts
  - id: wait
    source: community/modules/scripts/wait-for-startup
    settings:
      instance_name: ((module.qsimvm.name[0]))
      timeout: 2400