NVIDIA cuQuantum
25.06.0

Contents

  • Overview
    • Introduction to quantum computing
      • Qubit
      • Multiple qubits
      • Quantum gates
      • Measurement
      • Quantum circuit
    • Quantum circuit simulation
    • References
    • Citing cuQuantum
  • Release Notes
    • cuQuantum SDK v25.06
    • cuQuantum SDK v25.03
    • cuQuantum SDK v24.11
    • cuQuantum SDK v24.08
    • cuQuantum SDK v24.03
    • cuQuantum SDK v23.10
    • cuQuantum SDK v23.06.1
    • cuQuantum SDK v23.06
    • cuQuantum SDK v23.03
    • cuQuantum SDK v22.11
    • cuQuantum SDK v22.07.1
    • cuQuantum SDK v22.07
    • cuQuantum SDK v22.05
    • cuQuantum SDK v22.03
    • cuQuantum SDK v0.1.1
    • cuQuantum SDK v0.1.0
  • Getting Started
    • Installing cuQuantum
      • From conda-forge
        • cuQuantum
        • cuQuantum Python
        • Specifying CUDA version
        • Individual components
        • MPI installation notes
        • Setting CUQUANTUM_ROOT
      • From PyPI
        • Version-specific wheels
        • Using meta-packages
      • From source
      • From NVIDIA DevZone
        • Using archive
        • Using system package managers
    • Installing cuQuantum with Frameworks
      • CUDA Quantum
      • Qiskit
        • conda-forge
        • PyPI
      • Cirq
        • conda-forge
        • Source
      • PennyLane
        • conda-forge
        • PyPI
    • Running the cuQuantum Appliance
      • At the command-line
        • With an interactive session
        • With a noninteractive session
        • With specific GPUs
      • Using remote hosts
        • Clarifying our assumptions
        • With DOCKER_HOST
        • With Docker contexts
      • Interacting with the remote container
        • Visual Studio Code
    • Running the NVIDIA Quantum Benchmarks
      • Usage
      • Installation
        • Bare-metal
        • Appliance
    • Dependencies
      • cuStateVec
      • cuTensorNet
      • cuDensityMat
      • cuQuantum Python
      • cuQuantum Appliance
  • cuDensityMat
    • Release Notes
      • cuDensityMat v0.2.0
      • cuDensityMat v0.1.0
      • cuDensityMat v0.0.5
    • Overview
      • Tensor spaces and quantum states
      • Quantum many-body operators (super-operators)
      • Workspace descriptor
      • Backward differentiation
      • Coupled quantum dynamics master equations (system of ODE)
      • Properties of quantum states
      • Multi-GPU multi-node execution
      • Citing cuQuantum
    • Examples
      • Compiling code
      • Code example (serial execution on a single GPU)
      • Code example (parallel execution on multiple GPUs)
      • Code example (serial execution with backward differentiation)
      • Useful tips
    • API Reference
      • cuDensityMat data types
        • cudensitymatHandle_t
        • cudensitymatStatus_t
        • cudensitymatComputeType_t
        • cudensitymatDistributedProvider_t
        • cudensitymatDistributedCommunicator_t
        • cudensitymatCallbackDevice_t
        • cudensitymatDifferentiationDir_t
        • CUDENSITYMAT_ALLOCATOR_NAME_LEN
        • cudensitymatWorkspaceDescriptor_t
        • cudensitymatMemspace_t
        • cudensitymatWorkspaceKind_t
        • cudensitymatState_t
        • cudensitymatStatePurity_t
        • cudensitymatElementaryOperator_t
        • cudensitymatElementaryOperatorSparsity_t
        • cudensitymatMatrixOperator_t
        • cudensitymatOperatorTerm_t
        • cudensitymatOperator_t
        • cudensitymatOperatorAction_t
        • cudensitymatExpectation_t
        • cudensitymatScalarCallback_t
        • cudensitymatTensorCallback_t
        • cudensitymatWrappedScalarCallback_t
        • cudensitymatWrappedTensorCallback_t
        • cudensitymatScalarGradientCallback_t
        • cudensitymatTensorGradientCallback_t
        • cudensitymatWrappedScalarGradientCallback_t
        • cudensitymatWrappedTensorGradientCallback_t
      • cuDensityMat functions
        • Library context management API
        • Distributed parallelization API
        • Workspace management API
        • Quantum state API
        • Quantum operator API
        • Operator expectation API
        • Versioning API
    • Acknowledgements
  • cuStateVec
    • Release Notes
      • cuStateVec v1.9.0
      • cuStateVec v1.8.0
      • cuStateVec v1.7.0
      • cuStateVec v1.6.0
      • cuStateVec v1.5.0
      • cuStateVec v1.4.1
      • cuStateVec v1.4.0
      • cuStateVec v1.3.0
      • cuStateVec v1.2.0
      • cuStateVec v1.1.0
      • cuStateVec v1.0.0
      • cuStateVec v0.1.1
      • cuStateVec v0.1.0
      • cuStateVec v0.0.1
    • Overview
      • API synchronization behavior
      • Using CUDA stream
      • Description of state vectors
      • Bit ordering
      • Supported data types
      • Math mode
      • Workspace
      • Gate fusion
      • Multi-GPU computation
      • Batched state vectors simulation
      • References
      • Citing cuQuantum
    • Examples
      • Compilation
      • Code example
      • Useful tips
    • Distributed Index Bit Swap API
      • About this document
      • Distributed state vector simulation
        • State vector distribution
        • Qubit reordering and distributed index bit swap
      • Requirements
      • API design
        • API design
        • Scheduling index bit swaps by using batch index
        • Swap state vector elements
        • Inter-process communication by custatevecCommunicator
        • Performance consideration
      • Example
    • Host State Vector Migration
      • About this document
      • custatevecSubSVMigrator API
        • Memory model of custatevecSubSVMigrator API
        • Possible scenarios
    • API Reference
      • cuStateVec data types
        • Opaque data structures
        • Enumerators
      • cuStateVec functions
        • Library management
        • Initialization
        • Gate application
        • Measurement
        • Expectation
        • Matrix property testing
        • Sampling
        • Accessor
        • Single-process qubit reordering
        • Multi-process qubit reordering
        • Sub state vector migration
  • cuTensorNet
    • Release Notes
      • cuTensorNet v2.8.0
      • cuTensorNet v2.7.0
      • cuTensorNet v2.6.0
      • cuTensorNet v2.5.0
      • cuTensorNet v2.4.0
      • cuTensorNet v2.3.0
      • cuTensorNet v2.2.1
      • cuTensorNet v2.2.0
      • cuTensorNet v2.1.0
      • cuTensorNet v2.0.0
      • cuTensorNet v1.1.1
      • cuTensorNet v1.1.0
      • cuTensorNet v1.0.1
      • cuTensorNet v1.0.0
      • cuTensorNet v0.1.0
      • cuTensorNet v0.0.1
    • Overview
      • Introduction to tensor networks
        • Tensor and tensor network
        • Description of tensor networks
        • Tensor network state specification and processing
        • Approximate tensor network algorithms
        • Projection Matrix Product State
      • Contraction optimizer
        • Graph partitioning
        • Slicing
        • Reconfiguration
        • Deferred rank simplification
      • Hyper-optimizer
      • Intermediate tensor(s) reuse
      • Approximation setting
        • SVD Options
        • Gate-split algorithm
      • Supported data types
      • References
      • Citing cuQuantum
    • Examples
      • Compiling code
      • Code example (serial)
        • Headers and data types
        • Define tensor network and tensor sizes
        • Allocate memory and initialize data
        • cuTensorNet handle and network descriptor
        • Optimal contraction order and slicing
        • Create workspace descriptor and allocate workspace memory
        • Contraction plan and auto-tune
        • Tensor network contraction execution
      • Code example (automatic slice-based distributed parallelization)
      • Code example (manual slice-based distributed parallelization)
      • Code example (tensorQR)
        • Define QR decomposition
        • Allocate memory and initialize data
        • Initialize cuTensorNet and create tensor descriptors
        • Query and allocate required workspace
        • Execution
        • Free resources
      • Code example (tensorSVD)
        • Define SVD decomposition
        • Setup SVD truncation parameters
        • Execution
      • Code example (GateSplit)
        • Define tensor operands
        • Execution
      • Code example (MPS factorization)
        • Define MPSHelper class
        • Setup MPS simulation setting
        • Allocate memory and initialize data
        • Setup gate split options
        • Query and allocate required workspace
        • Execution
        • Free resources
      • Code example (intermediate tensor reuse)
        • Caching/Reusing constant intermediate tensors
        • Headers and data types
        • Define tensor network and tensor sizes
        • Allocate memory, initialize data, initialize cuTensorNet handle
        • Mark constant tensors and create the network descriptor
        • Contraction order and slicing
        • Create workspace descriptor and allocate workspace memory
        • Contraction plan and auto-tune
        • Tensor network contraction execution
        • Free resources
      • Code example (gradients computation)
        • Computing gradients via backward propagation
        • Headers and data types
        • Define tensor network and tensor sizes
        • Allocate memory, initialize data, initialize cuTensorNet handle
        • Create the network descriptor and set gradient tensor IDs
        • Contraction order
        • Create workspace descriptor and allocate workspace memory
        • Contraction plan and auto-tune
        • Tensor network contraction execution and gradient computation
        • Free resources
      • Code example (amplitudes slice)
        • Computing tensor network state amplitudes
        • Headers and error handling
        • Define the tensor network state and the desired slice of state amplitudes
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate the amplitudes slice tensor on GPU
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Create the state amplitudes accessor
        • Configure the state amplitudes accessor
        • Prepare the computation of the amplitudes slice tensor
        • Set up the workspace
        • Compute the specified slice of state amplitudes
        • Free resources
      • Code example (expectation value)
        • Computing tensor network state expectation value
        • Headers and error handling
        • Define the tensor network state
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Construct a tensor network operator
        • Create the expectation value object
        • Configure the expectation value calculation
        • Prepare the expectation value calculation
        • Set up the workspace
        • Compute the requested expectation value
        • Free resources
      • Code example (marginal distribution)
        • Computing tensor network state marginal distribution tensor
        • Headers and error handling
        • Define the tensor network state and the desired marginal distribution tensor
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate the marginal distribution tensor on GPU
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Create the marginal distribution object
        • Configure the marginal distribution object
        • Prepare the computation of the marginal distribution tensor
        • Set up the workspace
        • Compute the marginal distribution tensor
        • Free resources
      • Code example (tensor network sampling)
        • Sampling the tensor network state
        • Headers and error handling
        • Define the tensor network state and the desired number of output samples to generate
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Create the tensor network state sampler
        • Configure the tensor network state sampler
        • Prepare the tensor network state sampler
        • Allocate the workspace buffer on GPU and setup the workspace
        • Perform sampling of the final quantum circuit state
        • Free resources
      • Code example (MPS amplitudes slice using simple update)
        • Computing Matrix Product State (MPS) Amplitudes
        • Headers and error handling
        • Define the tensor network state and the desired slice of state amplitudes
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate MPS tensors
        • Allocate the amplitudes slice tensor on GPU
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Create the state amplitudes accessor
        • Configure the state amplitudes accessor
        • Prepare the computation of the amplitudes slice tensor
        • Set up the workspace
        • Compute the specified slice of state amplitudes
        • Free resources
      • Code example (MPS expectation value)
        • Computing Matrix Product State expectation value
        • Headers and error handling
        • Define the tensor network state
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate MPS tensors
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Construct a tensor network operator
        • Create the expectation value object
        • Configure the expectation value calculation
        • Prepare the expectation value calculation
        • Set up the workspace
        • Compute the requested expectation value
        • Free resources
      • Code example (MPS marginal distribution)
        • Computing Matrix Product State marginal distribution tensor
        • Headers and error handling
        • Define the tensor network state and the desired marginal distribution tensor
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate MPS tensors
        • Allocate the marginal distribution tensor on GPU
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Create the marginal distribution object
        • Configure the marginal distribution object
        • Prepare the computation of the marginal distribution tensor
        • Set up the workspace
        • Compute the marginal distribution tensor
        • Free resources
      • Code example (MPS sampling)
        • Sampling the Matrix Product State
        • Headers and error handling
        • Define the tensor network state and the desired number of output samples to generate
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Allocate MPS tensors
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Create the tensor network state sampler
        • Configure the tensor network state sampler
        • Prepare the tensor network state sampler
        • Set up the workspace
        • Perform sampling of the final quantum circuit state
        • Free resources
      • Code example (MPS sampling QFT)
        • Sampling the Matrix Product State (QFT Circuit)
        • Headers and error handling
        • Define the tensor network state and the desired number of output samples to generate
        • Initialize the cuTensorNet library handle
        • Define quantum gates in GPU memory
        • Allocate MPS tensors in GPU memory
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Apply quantum gates
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Create the tensor network state sampler
        • Configure the tensor network state sampler
        • Prepare the tensor network state sampler
        • Set up the workspace
        • Perform sampling of the final quantum circuit state
        • Free resources
      • Code example (Projection MPS Circuit DMRG)
        • Projection MPS Circuit DMRG
        • Headers, error handling, and helper functions
        • Define the tensor network state and quantum circuit configuration
        • Initialize the cuTensorNet library handle
        • Define quantum gates on GPU
        • Create initial quantum state and apply gates
        • Set up and specify the MPS representation of the output state
        • Initialize the MPS in the vacuum state
        • Define MPS representation and allocate projection MPS tensors
        • Create state projection MPS
        • Perform DMRG optimization
        • Free resources
      • Code example (MPS sampling MPO)
        • Sampling the Matrix Product State (circuit with Matrix Product Operators)
        • Headers and error handling
        • Define the tensor network state and the desired number of output samples to generate
        • Initialize the cuTensorNet library handle
        • Define and allocate MPO tensors
        • Define and allocate MPS tensors
        • Allocate the scratch buffer on GPU
        • Create a pure tensor network state
        • Construct necessary MPO tensor network operators
        • Apply MPO tensor network operators to the quantum circuit state
        • Request MPS factorization for the final quantum circuit state
        • Configure MPS factorization procedure
        • Prepare the computation of MPS factorization
        • Compute MPS factorization
        • Create the tensor network state sampler
        • Configure the tensor network state sampler
        • Prepare the tensor network state sampler
        • Set up the workspace
        • Perform sampling of the final quantum circuit state
        • Free resources
      • Useful tips
    • API Reference
      • cuTensorNet data types
        • cutensornetHandle_t
        • cutensornetLoggerCallback_t
        • cutensornetLoggerCallbackData_t
        • cutensornetStatus_t
        • cutensornetComputeType_t
        • cutensornetDistributedCommunicator_t
        • cutensornetContractionOptimizerConfigAttributes_t
        • cutensornetContractionOptimizerInfoAttributes_t
        • cutensornetContractionAutotunePreferenceAttributes_t
        • cutensornetGraphAlgo_t
        • cutensornetMemoryModel_t
        • cutensornetOptimizerCost_t
        • cutensornetSmartOption_t
        • cutensornetNetworkDescriptor_t
        • cutensornetNetworkAttributes_t
        • cutensornetContractionPlan_t
        • cutensornetNodePair_t
        • cutensornetContractionPath_t
        • cutensornetContractionOptimizerConfig_t
        • cutensornetContractionOptimizerInfo_t
        • cutensornetContractionAutotunePreference_t
        • cutensornetSliceGroup_t
        • cutensornetDeviceMemHandler_t
        • CUTENSORNET_ALLOCATOR_NAME_LEN
        • cutensornetWorkspaceDescriptor_t
        • cutensornetWorksizePref_t
        • cutensornetMemspace_t
        • cutensornetWorkspaceKind_t
        • cutensornetTensorQualifiers_t
        • cutensornetSliceInfoPair_t
        • cutensornetSlicingConfig_t
        • cutensornetTensorDescriptor_t
        • cutensornetTensorSVDConfig_t
        • cutensornetTensorSVDConfigAttributes_t
        • cutensornetTensorSVDPartition_t
        • cutensornetTensorSVDNormalization_t
        • cutensornetTensorSVDAlgo_t
        • cutensornetGesvdjParams_t
        • cutensornetGesvdrParams_t
        • cutensornetGesvdjStatus_t
        • cutensornetGesvdpStatus_t
        • cutensornetTensorSVDInfo_t
        • cutensornetTensorSVDInfoAttributes_t
        • cutensornetGateSplitAlgo_t
        • cutensornetState_t
        • cutensornetStatePurity_t
        • cutensornetBoundaryCondition_t
        • cutensornetStateAttributes_t
        • cutensornetStateMPOApplication_t
        • cutensornetStateMPSGaugeOption_t
        • cutensornetNetworkOperator_t
        • cutensornetStateAccessor_t
        • cutensornetAccessorAttributes_t
        • cutensornetStateExpectation_t
        • cutensornetExpectationAttributes_t
        • cutensornetStateMarginal_t
        • cutensornetMarginalAttributes_t
        • cutensornetStateSampler_t
        • cutensornetSamplerAttributes_t
        • cutensornetStateProjectionMPS_t
        • cutensornetMPSEnvBounds_t
        • cutensornetStateProjectionMPSOrthoOption_t
        • cutensornetStateProjectionMPSAttributes_t
        • cudaDataType_t
      • cuTensorNet functions
        • Handle management API
        • Network descriptor API
        • Tensor descriptor API
        • Contraction optimizer API
        • Contraction plan API
        • Workspace management API
        • Network contraction API
        • Gradient computation API
        • Slice group API
        • Approximate tensor network execution API
        • Tensor SVD config API
        • Tensor SVD info API
        • Distributed parallelization API
        • Tensor network state API
        • State projection MPS API
        • Memory management API
        • Error management API
        • Logger API
        • Versioning API
    • Acknowledgements
  • cuQuantum Python
    • Release Notes
      • cuQuantum Python v25.06.0
      • cuQuantum Python v25.03.0
      • cuQuantum Python v24.11.0
      • cuQuantum Python v24.08.0
      • cuQuantum Python v24.03.0
      • cuQuantum Python v23.10.0
      • cuQuantum Python v23.06.0
      • cuQuantum Python v23.03.0
      • cuQuantum Python v22.11.0.1
      • cuQuantum Python v22.11.0
      • cuQuantum Python v22.07.1
      • cuQuantum Python v22.07.0
      • cuQuantum Python v22.05.0
      • cuQuantum Python v22.03.0
      • cuQuantum Python v0.1.0.1
      • cuQuantum Python v0.1.0.0
    • Overview
      • Introduction
        • Command line support
      • Compatibility policy
      • Citing cuQuantum
    • Utility APIs
      • API reference
        • Python objects & constants
    • Low-level Python Bindings
      • Naming & calling convention
      • Memory management
        • Pointer and data lifetime
        • User-provided memory pools
      • Usage example
      • API reference
        • cuDensityMat (cuquantum.bindings.cudensitymat)
        • cuStateVec (cuquantum.bindings.custatevec)
        • cuTensorNet (cuquantum.bindings.cutensornet)
    • Quantum Dynamics APIs
      • WorkStream
      • Tensor spaces and quantum states
      • Quantum many-body operators (super-operators)
        • Elementary operators
        • Matrix operators
        • Composing symbolic expressions with operators
      • Batching
      • Backward differentiation
      • Usage examples
        • General usage example
        • MGMN usage example
        • Dense elementary operator example
        • Multidiagonal elementary operator example
        • Full matrix operator example
        • Lindbladian example
        • Backward differentiation example with scalar callback
        • Backward differentiation example with tensor callback
      • API reference
        • Context API
        • Callback API
        • Operator API
        • State API
    • Tensor Network APIs
      • Contraction
        • Introduction
        • Usage example
        • Call blocking behavior
        • Stream semantics
        • Resource management
        • External memory management
      • Decomposition
        • Introduction
        • Usage example
      • CircuitToEinsum converter
        • Introduction
        • Usage example
      • Tensor network simulator
        • Introduction
        • Caching feature
        • MPI support
      • API reference
        • Objects
        • Python functions
        • Tensor submodule
        • Experimental submodule
    • Code of Conduct
      • Overview
      • Our Pledge
      • Our Standards
      • Our Responsibilities
      • Scope
      • Enforcement
      • Attribution
    • Contributing
  • cuQuantum Appliance
    • Overview
      • Prerequisites
      • Running the NVIDIA cuQuantum Appliance with Cirq or Qiskit
      • Known issues
        • For tags: *23.10-*-arm64
      • Software in the container
        • Default user environment
        • MPI
      • Important change notices
        • version == 25.03
        • version == 24.08
        • version == 24.03
      • Security scanning notices
        • Version 25.03 security scanning results summary
        • Appliance version end of life summary
      • Documentation
      • Additional Resources
      • License Agreement
        • Citing cuQuantum
    • Release Notes
      • cuQuantum Appliance 25.06
        • Improvements
        • Driver Requirements
      • cuQuantum Appliance 25.03
        • New features
        • Improvements
        • Resolved issue
        • Driver Requirements
      • cuQuantum Appliance 24.08
        • Resolved issue
        • Driver Requirements
      • cuQuantum Appliance 24.03
        • Resolved issue
        • Driver Requirements
      • cuQuantum Appliance 23.10
        • New Features
        • Driver Requirements
      • cuQuantum Appliance 23.06
        • Resolved issues
        • Driver requirements
      • cuQuantum Appliance 23.03
        • Resolved issues
        • New sample
        • Driver requirements
      • cuQuantum Appliance 22.11
        • New features
        • Driver requirements
      • cuQuantum Appliance 22.07-Cirq
        • New features
        • Driver requirements
      • cuQuantum Appliance 22.03-Cirq
        • Resolved issues
      • cuQuantum Appliance 22.02-Cirq
        • Contents of the cuQuantum Appliance 22.02-Cirq container
        • Driver requirements
        • GPU Requirements
        • Known Limitations
    • Cirq
      • Notice
      • API reference
        • qsimcirq.QSimOptions
    • Qiskit
      • Getting started
      • Selecting simulator
      • cusvaer-specific options
      • Modifications for Qiskit Aer options
      • Interoperability with mpi4py
      • Limitations
    • cusvaer
      • New features
      • Qiskit backend for distributed simulations
      • Distributed state vector simulation
      • Using CPU and GPU memory to allocate state vector
      • Running simulations in GB200 and GH200 clusters
      • Using cusvaer
      • MPI libraries
      • cusvaer options
        • Device selection
        • Multi-process simulation
        • Specifying device network structure
        • CPU memory utilization
        • Other options
      • Custom instruction
        • set_state_simple(state)
        • save_state_simple()
      • Example of cusvaer option configurations
      • Exception
      • cusvaer environmental variable
        • UBACKEND_USE_FABRIC_HANDLE
      • Interoperability with mpi4py
    • Acknowledgements
  • Software License Agreement
    • NVIDIA cuQuantum SDK
    • NVIDIA cuQuantum Python
NVIDIA cuQuantum
  • cuDensityMat: A High-Performance Library for Analog Quantum Dynamics Computations
  • API Reference
  • View page source

API ReferenceΒΆ

This reference describes all API components of the cuDensityMat library.

  • cuDensityMat data types
    • cudensitymatHandle_t
    • cudensitymatStatus_t
    • cudensitymatComputeType_t
    • cudensitymatDistributedProvider_t
    • cudensitymatDistributedCommunicator_t
    • cudensitymatCallbackDevice_t
    • cudensitymatDifferentiationDir_t
    • CUDENSITYMAT_ALLOCATOR_NAME_LEN
    • cudensitymatWorkspaceDescriptor_t
    • cudensitymatMemspace_t
    • cudensitymatWorkspaceKind_t
    • cudensitymatState_t
    • cudensitymatStatePurity_t
    • cudensitymatElementaryOperator_t
    • cudensitymatElementaryOperatorSparsity_t
    • cudensitymatMatrixOperator_t
    • cudensitymatOperatorTerm_t
    • cudensitymatOperator_t
    • cudensitymatOperatorAction_t
    • cudensitymatExpectation_t
    • cudensitymatScalarCallback_t
    • cudensitymatTensorCallback_t
    • cudensitymatWrappedScalarCallback_t
    • cudensitymatWrappedTensorCallback_t
    • cudensitymatScalarGradientCallback_t
    • cudensitymatTensorGradientCallback_t
    • cudensitymatWrappedScalarGradientCallback_t
    • cudensitymatWrappedTensorGradientCallback_t
  • cuDensityMat functions
    • Library context management API
    • Distributed parallelization API
    • Workspace management API
    • Quantum state API
    • Quantum operator API
    • Operator expectation API
    • Versioning API
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NVIDIA cuQuantum v: 25.06.0