NVIDIA CUDA-QX
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Getting Started

  • Installation Guide
    • Installation Methods
      • pip install
      • Docker Container
      • Building from Source
    • Installing PyTorch

Libraries

  • CUDA-Q QEC - Quantum Error Correction Library
    • Overview
    • Core Components
    • QEC Code Framework cudaq::qec::code
      • Class Structure
      • Implementing a New Code
      • Example: Steane Code
      • Implementing a New Code in Python
      • Key Points
      • Using the Code Framework
    • Pre-built QEC Codes
      • Steane Code
      • Repetition Code
    • Decoder Framework cudaq::qec::decoder
      • Class Structure
      • Implementing a New Decoder in C++
      • Example: Lookup Table Decoder
      • Implementing a Decoder in Python
      • Key Features
      • Usage Example
    • Pre-built QEC Decoders
      • Quantum Low-Density Parity-Check Decoder
      • Tensor Network Decoder
    • Real-Time Decoding
      • Key Features
      • Workflow and Terminology
    • Numerical Experiments
      • Conventions
      • Memory Circuit Experiments
        • Function Variants
        • Return Values
        • Example Usage
        • Additional Noise Models
  • CUDA-Q Solvers Library
    • Overview
    • Core Components
    • Operator Infrastructure
      • Molecular Hamiltonian Options
      • Example Usage
    • Variational Quantum Eigensolver (VQE)
    • VQE Examples
      • Basic Usage
      • Custom Optimization
      • Shot-based Simulation
    • ADAPT-VQE
      • Key Features
      • Basic Usage
      • Advanced Usage
        • Custom Optimization Settings
      • Available Operator Pools
      • Available Ansatz
      • Algorithm Parameters
      • Results Analysis

Examples

  • CUDA-Q QEC by Example
    • Code-Capacity QEC
      • CUDA-Q QEC Implementation
      • Code Explanation
    • Circuit-Level QEC
      • CUDA-Q QEC Implementation
    • Decoders
      • Detector Error Model
      • Generating a Multi-Round Parity Check Matrix
      • Creating New QEC codes
      • Getting Started with the NVIDIA QLDPC Decoder
        • Belief Propagation Methods
        • Usage Example
      • Exact Maximum Likelihood Decoding with NVIDIA Tensor Network Decoder
      • Deploying AI Decoders with TensorRT
    • Real-Time Decoding
      • Workflow Overview
      • Real-Time Decoding Example
      • Configuration
        • Step 1: Generate Detector Error Model
        • Step 2: Configure and Save Decoder
        • Step 3: Load Configuration
        • Step 4: Use in Quantum Kernels
      • Backend Selection
        • Simulation Backend
        • Quantinuum Hardware Backend
      • Compilation and Execution Examples
        • Common Use Cases
        • C++ Compilation
        • Python Execution
        • Complete Workflow Example
        • Debugging and Environment Variables
        • Decoder Selection
      • Troubleshooting
        • Configuration Upload Failures (Quantinuum Backend)
      • See Also
  • CUDA-Q Solvers by Example
    • Molecular-Hamiltonians
      • Molecular Orbitals and Hamiltonians
      • Natural Orbitals from MP2
      • CASSCF Orbitals
      • For open-shell systems
    • ADAPT-VQE
    • VQE
      • CUDA-Q Solvers Implementation
      • Code Explanation
    • QAOA
      • CUDA-Q Solvers Implementation
      • Code Explanation
    • GQE
      • CUDA-Q Solvers Implementation

API Reference

  • CUDA-QX Namespaces and Core Library C++ API
    • Namespaces
    • Core
  • CUDA-Q QEC C++ API
    • Code
    • Detector Error Model
    • Decoder Interfaces
    • Built-in Decoders
      • NVIDIA QLDPC Decoder
        • nv_qldpc_decoder
      • Sliding Window Decoder
        • sliding_window
    • Real-Time Decoding
      • Core Decoding Functions
      • Configuration API
      • Helper Functions
    • Parity Check Matrix Utilities
    • Common
  • CUDA-Q QEC Python API
    • Code
    • Detector Error Model
    • Decoder Interfaces
    • Built-in Decoders
      • NVIDIA QLDPC Decoder
        • nv_qldpc_decoder
      • Sliding Window Decoder
        • sliding_window
      • Tensor Network Decoder
        • cudaq_qec.plugin.decoders.tensor_network_decoder.TensorNetworkDecoder
    • Real-Time Decoding
      • Core Decoding Functions
        • cudaq_qec.qec.enqueue_syndromes()
        • cudaq_qec.qec.get_corrections()
        • cudaq_qec.qec.reset_decoder()
      • Configuration API
        • cudaq_qec.configure_decoders()
        • cudaq_qec.configure_decoders_from_file()
        • cudaq_qec.configure_decoders_from_str()
        • cudaq_qec.finalize_decoders()
      • Helper Functions
        • cudaq_qec.pcm_to_sparse_vec()
        • cudaq_qec.pcm_from_sparse_vec()
        • cudaq_qec.generate_timelike_sparse_detector_matrix()
    • Common
    • Parity Check Matrix Utilities
  • CUDA-Q Solvers C++ API
  • CUDA-Q Solvers Python API
    • jordan_wigner()
    • bravyi_kitaev()
    • MolecularHamiltonian
      • MolecularHamiltonian.energies
      • MolecularHamiltonian.hamiltonian
      • MolecularHamiltonian.hpq
      • MolecularHamiltonian.hpqrs
      • MolecularHamiltonian.n_electrons
      • MolecularHamiltonian.n_orbitals
    • get_operator_pool()
    • optimize()
    • ObserveExecutionType
      • ObserveExecutionType.function
      • ObserveExecutionType.gradient
      • ObserveExecutionType.name
      • ObserveExecutionType.value
    • ObserveIteration
      • ObserveIteration.parameters
      • ObserveIteration.result
      • ObserveIteration.type
    • vqe()
    • adapt_vqe()
    • uccsd()
    • single_excitation()
    • double_excitation()
    • get_num_uccsd_parameters()
    • get_uccsd_excitations()
    • get_uccgsd_pauli_lists()
    • uccgsd()
    • get_num_qaoa_parameters()
    • gqe()
    • cudaq_solvers.gqe_algorithm.gqe.get_default_config()
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