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TARGETS.md

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CUDA Quantum target

A target is a specification of the desired platform and simulator / QPU. It can be specified in C++ as a compile time flag in C++ and in Python as a runtime flag. Alteratively, it can also be specified within the application code.

Simulation backends

  • state-vector (cuStateVec)
  • density-matrix (dm)
  • tensor-network (cuTensorNet)

Hardware backends

  • CPU only
  • Single GPU
  • Multi-GPU
  • Multi-QPU

Targets

  • default
    • qpp, CPU-based and multithreaded
      • In C++, nvq++ ghz_state.cpp -o a.out
      • In Python, python3 ghz_state.py
  • nvidia
    • custatevec with single-GPU
      • In C++, nvq++ --target nvidia ghz_state.cpp -o a.out
      • In Python, python3 --target nvidia ghz_state.py
  • nvidia-mqpu
    • custatevec with multi-QPU, realized through GPUs acting as virtul QPUs. 
      $ python --target nvidia-mqpu distribute_hamiltonian.py
  • nvidia-mgpu
    • Allows for Multi-Node, Multi-GPU distribution of the state vector using custatevec. 
      $ nvq++ --target nvidia-mgpu ghz.py -o a.out
$ mpirun -np 2 ./a.out
  • density-matrix-cpu
    • CPU-only multithreaded density matrix emulation
  • quantinuum
    - --target quantinuum
  • ionq
  • more!

To print a list of all the targets that are available for you on your machine:

import cudaq

targets = cudaq.get_targets()

for t in targets:
     print(t)