CUDA-Q Circuit Simulation Backends

The simulators available in CUDA-Q are grouped in the figure below. The following sections follow the structure of the figure and provide additional technical details and code examples for using each circuit simulator.

Simulators In CUDA-Q

Simulator Name	Method	Purpose	Processor(s)	Precision(s)	N Qubits
qpp-cpu	State Vector	Testing and small applications	CPU	double	< 28
nvidia *	State Vector	General purpose (default); Trajectory simulation for noisy circuits	Single GPU	single (default) / double	< 33 / 32 (64 GB)
nvidia, option=mgpu *	State Vector	Large-scale simulation	multi-GPU multi-node	single (default) / double	33+
tensornet *	Tensor Network	Shallow-depth (low-entanglement) and high width circuits (exact)	multi-GPU multi-node	double	Thousands
tensornet-mps *	Matrix Product State	Square-shaped circuits (approximate)	Single GPU	double	Hundreds
fermioniq	Matrix Product State	Square-shaped circuits (approximate)	Single GPU	double	Hundreds
nvidia, option=mqpu *	State Vector	Asynchronous distribution across multiple simulated QPUs to speedup applications	multi-GPU multi-node	single (default) / double	< 33 / 32 (64 GB)
remote-mqpu *	State Vector / Tensor Network	Combine mqpu with other backend like tensornet and mgpu	varies	varies	varies
Trajectory Noisy Simulation	works with all simulators marked *	Noisy trajectory simulations	multi-GPU multi-node	double	varies
density-matrix-cpu	Density Matrix	Noisy simulations	CPU	double	< 14
stim	Stabilizer	QEC simulation	CPU	N/A	Thousands +
orca-photonics	State Vector	Photonics	CPU	double	Varies on qudit level

• State Vector Simulators
  • CPU
  • Single-GPU
  • Multi-GPU multi-node
• Tensor Network Simulators
  • Multi-GPU multi-node
  • Matrix product state
  • Fermioniq
• Multi-QPU Simulators
  • Simulate Multiple QPUs in Parallel
  • Multi-QPU + Other Backends
• Noisy Simulators
  • Trajectory Noisy Simulation
  • Density Matrix
  • Stim
• Photonics Simulators
  • orca-photonics