Welcome to Qristal – The Quantum Brilliance SDK

Qristal is a full-stack library for designing, optimizing, simulating and running hybrid quantum programs.

Features

• Execute quantum algorithms directly on QB’s next-generation room temperature quantum accelerators.

• High-level quantum subroutines for solving practical problems including optimization, chemistry and machine learning.

• A suite of world-class quantum circuit simulators, including GPU-capable and HPC-aware tensor network simulators.

• Accurate noise models and emulation of Quantum Brilliance’s hardware.

Components and Licensing

This documentation covers both the Qristal Software Development Kit and its Emulator plugin.

Qristal is released under the open source Apache License, Version 2.0.

The Emulator plugin for Qristal can be obtained in binary form from Quantum Brilliance.

Basic Usage

• Getting started
  • Quickstart
  • Installation
    • Dependencies
    • Installing from source
  • Contributing
    • Asking Questions
    • Reporting and Discussing Issues
    • Contributing Code
  • Emulator
    • Qristal Emulator Getting Started Guide
      • 1. Check that your system meets minimum requirements
      • 2. Install Docker
      • 3. Log in to the Gitlab Docker registry
      • 4. Get the Qristal Emulator Image
      • 5. Activate the image using your license key
      • 6. Test that your image is successfully activated
      • 7. Run some other examples
      • 8. Test out the Jupyter Lab interface, and try the exercise
      • 9. Write your own quantum programs!
      • 10. Known issues
• Examples
  • Python
  • C++
    • In depth: vqeeCalculator
      • Compiling
      • Limitations
      • JSON input and output
  • C++ with CUDA Quantum
• Command-line interface
• Backends
  • Hardware backends
    • Quantum Brilliance
    • Amazon Braket Rigetti: aws-braket (Rigetti)
  • State-vector simulators
    • Quantum Brilliance state vector: qsim
    • Microsoft sparse state vector: sparse-sim
    • Quantum++ state-vector simulator: qpp and cudaq:qpp
    • cuQuantum state-vector simulators: custatevec:fp32 and custatevec:fp64
    • Aer state vector: aer (statevector)
    • Amazon Braket SV1: aws-braket (SV1)
  • Tensor network simulators
    • Quantum Brilliance Matrix-Product State (MPS): qb-mps and cudaq:qb_mps
    • Quantum Brilliance Matrix-Product Density Operator: qb-mpdo and cudaq:qb_mpdo
    • Quantum Brilliance purification: qb-purification and cudaq:qb_purification
    • TNQVM-ExaTN MPS: tnqvm
    • Aer MPS: aer (matrix_product_state)
    • Amazon Braket TN1: aws-braket (TN1)
  • Density matrix simulators
    • CUDA Quantum density matrix simulator: cudaq:dm
    • Amazon Braket DM1: aws-braket (DM1)
    • Aer density matrix: aer (density_matrix)
• Qristal Emulator Getting Started Guide
  • 1. Check that your system meets minimum requirements
  • 2. Install Docker
  • 3. Log in to the Gitlab Docker registry
  • 4. Get the Qristal Emulator Image
  • 5. Activate the image using your license key
  • 6. Test that your image is successfully activated
  • 7. Run some other examples
  • 8. Test out the Jupyter Lab interface, and try the exercise
  • 9. Write your own quantum programs!
  • 10. Known issues

Advanced Topics

• Noise models
  • Summary of built-in noise models
    • default
    • qb-nm1
    • qb-nm2
    • qb-nm3
    • qb-qdk1
    • qb-dqc2
  • Examples showing the use of a built-in noise model
    • C++
    • Python
• User defined noise models
  • Modifying the default noise model
  • Noise channels
• Parametrized Quantum Circuits
  • Example in Qristal
    • Defining a parametrized circuit
    • Executing the circuit
    • Obtaining results
• Variational Quantum Eigensolver (VQE)
  • Example
  • Python API
    • Importing the module
    • Setting up the parameters for VQE
    • Setting up the optimization algorithm
    • Stochastic gradient algorithms
      • ADAM [Adaptive Momentum estimator]
    • Heuristic algorithms
      • CMA-ES [Covariance Matrix Adapter Evolution Strategy]
    • Gradient-based algorithms
      • L-BFGS
    • Gradient-free algorithms
      • Nelder-Mead
    • Triggering the VQE execution
    • Accessing the results
    • Viewing the convergence trace
    • Attributes related to the convergence trace
  • C++ API
  • General VQE information
    • Quantum kernels in Qristal
    • Hamiltonian
    • Ansätze
      • theta
      • User defined ansatz
      • Built in ansätze
    • Convention of mapping spin orbitals onto qubits
    • Molecular geometries in Qristal
  • How to make function calls for a Python-level optimizer?
• Nextflow
  • Installing Nextflow
    • Prerequisites
    • Downloading Nextflow
  • Structure of a Nextflow project
    • nextflow.config
    • main.nf
    • bin/
    • work/
  • Executing a Nextflow pipeline and generating a memory usage and execution time report
  • Example demonstrating asynchronous execution over multiple workers (shot partitioning)
    • main-partitioned.nf
    • Example OpenQASM file: ex-nf1.oqm

Reference

• Quantum Gates

Source Code Documentation

• C++ API
  • Class Hierarchy
  • File Hierarchy
  • Full API
• Python API
  • The session class
  • The Circuit class
  • Noise Modelling
  • Placement