_index.yaml

# Copyright 2020 Google LLC. All Rights Reserved.
#
# 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
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# ==============================================================================
book_path: /qsim/_book.yaml
project_path: /qsim/_project.yaml
description: >
  Quantum circuit simulator qsim.
landing_page:
  custom_css_path: /site-assets/css/style.css
  rows:
  - classname: quantum-hero quantum-hero--qsim
    options:
    - hero
    - description-50
    - padding-large
    heading: qsim
    description: >
      Optimized quantum circuit simulators
    items:
    - description: >
        <h4>qsim</h4>
        <p>
        qsim is a full state-vector simulator written in C++. It uses gate
        fusion, AVX/FMA vectorized instructions, and multithreading using
        OpenMP to achieve state-of-the-art simulations of quantum circuits.
        qsim is integrated with Cirq and can be used to run simulations of up
        to 40 qubits on a 90-core Intel Xeon workstation.
        </p>
      buttons:
      - label: Get started with qsim in Cirq
        path: /qsim/tutorials/qsimcirq
        classname: button button-primary
        icon_name: open_in_new
      - label: GitHub repository for qsim
        path: //github.com/quantumlib/qsim
        classname: button button-white
        icon_name: open_in_new
      code_block: |
        <pre class = "prettyprint">
        import cirq
        import qsimcirq

        # Pick up to ~25 qubits to simulate (requires ~256MB of RAM)
        qubits = [cirq.GridQubit(i,j) for i in range(5) for j in range(5)]

        # Define a circuit to run
        # (Example is from the 2019 "Quantum Supremacy" experiement)
        circuit = (cirq.experiments.
            random_rotations_between_grid_interaction_layers_circuit(
            qubits=qubits, depth=16))

        # Measure qubits at the end of the circuit
        circuit.append(cirq.measure(*qubits, key='all_qubits'))

        # Simulate the circuit with qsim and return just the measurement values
        # just like you would with Cirq
        qsim_simulator = qsimcirq.QSimSimulator()
        qsim_results = qsim_simulator.run(circuit, repetitions=5)
        print('qsim results:')
        print(qsim_results)
        </pre>
  - classname: quantum-row quantum-row--yellow
    background: theme
    heading: "Features and updates"
    options:
    - cards
    items:
    - heading: "Cirq integration"
      image_path: /site-assets/images/cards/qsim-card-cirq-integrations.png
      description: >
        Cirq is a python framework for writing, simulating, and executing
        quantum programs. Cirq's built-in simulator is useful to around 20
        qubits. By using the `qsimcirq` simulator, one can boost the number of
        qubits simulated to be mostly limited by available RAM. Up to 40 qubits
        can be simulated on a 90-core Intel Xeon workstation.
      buttons:
      - label: "Learn more"
        path: /qsim/cirq_interface
    - heading: "Install qsim on GCP"
      image_path: /site-assets/images/cards/qsim-card-gcp.jpg
      description: >
        Learn how to simulate up to 38 qubits on Google Cloud's Compute Engine.
        qsim has a prepackaged docker image that allows easy deployment of qsim,
        Juypter, and Cirq onto a virtual machine.
      buttons:
      - label: "Learn more"
        path: /qsim/tutorials/gcp_before_you_begin
    - heading: "Upgrades to qsim"
      image_path: /site-assets/images/cards/qsim-card-schrodinger.png
      description: >
        To help researchers and developers develop quantum algorithms today, we
        have made updates to qsim that make it more performant and intuitive,
        and more "hardware-like".
      buttons:
      - label: "Learn more"
        path: https://opensource.googleblog.com/2021/11/Upgrading%20qsim%20Google%20Quantum%20AIs%20Open%20Source%20Quantum%20Simulator%20.html?linkId=138925083
    - heading: "Integrating qsim with NVIDIA's cuQuantum SDK"
      image_path: /qsim/images/qsim_nvidia.png
      description: >
        The integration between qsim and the NVIDIA cuQuantum SDK will enable
        qsim users to make the most of GPUs when developing quantum algorithms
        and applications.
      buttons:
      - label: "Learn more"
        path: https://opensource.googleblog.com/2021/11/qsim%20integrates%20with%20NVIDIA%20cuQuantum%20SDK%20to%20accelerate%20quantum%20circuit%20simulations%20on%20NVIDIA%20GPUs.html