pytket-cutensornet

pytket-cutensornet is an extension to pytket that allows pytket circuits and expectation values to be simulated using cuTensorNet.

cuTensorNet is a high-performance library for tensor network computations, developed by NVIDIA. It is part of the cuQuantum SDK – a high-performance library aimed at quantum circuit simulations on the NVIDIA GPU chips.

We provide two core functionalities:

• Full tensor network contraction: use tk_to_tensor_network to translate a pytket circuit to a TensorNetwork and obtain expectation values and amplitudes via full tensor network contraction using cuQuantum’s optimised contraction path.

• Matrix Product State (MPS): use simulate to simulate a pytket circuit, returning an MPS representation of the output state, of which you can then get_amplitude or calculate inner products with other MPS via vdot.

Currently, only single-GPU calculations are supported, but a multi-GPU execution will be implemented in the due course using mpi4py library.

pytket-cutensornet is available for Python 3.10, 3.11 and 3.12 on Linux. In order to use it, you need access to a Linux machine with an NVIDIA GPU of Compute Capability +7.0 (check it here) and first install cuQuantum Python following their installation instructions. This will include the necessary dependencies such as CUDA toolkit. Then, to install pytket-cutensornet, run:

pip install pytket-cutensornet

• API documentation
  • General state (exact) simulation
    • GeneralState
      • GeneralState.sample()
      • GeneralState.get_amplitude()
      • GeneralState.get_statevector()
      • GeneralState.expectation_value()
      • GeneralState.destroy()
    • GeneralBraOpKet
      • GeneralBraOpKet.contract()
      • GeneralBraOpKet.destroy()
    • Pytket backend
      • CuTensorNetShotsBackend
        • CuTensorNetShotsBackend.process_circuits()
        • CuTensorNetShotsBackend.backend_info
      • CuTensorNetStateBackend
        • CuTensorNetStateBackend.process_circuits()
        • CuTensorNetStateBackend.backend_info
  • Structured state evolution
    • Library handle
      • CuTensorNetHandle
        • CuTensorNetHandle.handle
        • CuTensorNetHandle.device_id
        • CuTensorNetHandle.destroy()
    • Simulation
      • simulate()
      • SimulationAlgorithm
        • SimulationAlgorithm.TTNxGate
        • SimulationAlgorithm.MPSxGate
        • SimulationAlgorithm.MPSxMPO
      • Config
        • Config.__init__()
    • Classes
      • StructuredState
        • StructuredState.is_valid()
        • StructuredState.apply_gate()
        • StructuredState.apply_unitary()
        • StructuredState.apply_scalar()
        • StructuredState.apply_qubit_relabelling()
        • StructuredState.vdot()
        • StructuredState.sample()
        • StructuredState.measure()
        • StructuredState.postselect()
        • StructuredState.expectation_value()
        • StructuredState.get_fidelity()
        • StructuredState.get_statevector()
        • StructuredState.get_amplitude()
        • StructuredState.get_bits()
        • StructuredState.get_qubits()
        • StructuredState.get_byte_size()
        • StructuredState.get_device_id()
        • StructuredState.update_libhandle()
        • StructuredState.copy()
      • TTNxGate
        • TTNxGate.__init__()
      • MPSxGate
        • MPSxGate.__init__()
        • MPSxGate.add_qubit()
      • MPSxMPO
        • MPSxMPO.__init__()
    • Miscellaneous
      • prepare_circuit_mps()
• Changelog
  • 0.10.0 (October 2024)
  • 0.9.0 (October 2024)
  • 0.8.0 (September 2024)
  • 0.7.1 (July 2024)
  • 0.7.0 (July 2024)
  • 0.6.1 (April 2024)
  • 0.6.0 (April 2024)
  • 0.5.4 (January 2024)
  • 0.5.3 (January 2024)
  • 0.5.2 (December 2023)
  • 0.4.0 (October 2023)
  • 0.3.0 (September 2023)
  • 0.2.1 (August 2023)
  • 0.2.0 (July 2023)
  • 0.1.0 (June 2023)

Example Notebooks

• GeneralState Tutorial
  • Introduction
    
  • GeneralState
    
  • Parameterised circuits
    
  • GeneralBraOpKet
    
  • Backends
    
• Matrix Product State (MPS) Tutorial
  • Introduction
    
  • Basic functionality and exact simulation
    
    • Obtain an amplitude from an MPS
      
    • Sampling from an MPS
      
    • Inner products
      
    • Mid-circuit measurements and classical control
      
    • Two-qubit gates acting on non-adjacent qubits
      
  • Approximate simulation
    
  • Contraction algorithms
  • Using the logger
• Tree Tensor Network (TTN) Tutorial
  • Introduction
    
  • How to use
    
  • Obtain an amplitude from a TTN
    
  • Sampling from a TTN
    
  • Approximate simulation
    
  • Contraction algorithms
  • Using the logger

Useful links

• Issue tracker
• PyPi