Quantum Geometric Learning (QGL) combines quantum mechanics, differential geometry, and machine learning to analyze and manipulate quantum systems while preserving their geometric properties.
No, QGL runs on classical computers. It provides efficient simulations of quantum systems using advanced classical algorithms and optimizations.
- C compiler with C11 support
- CMake 3.12 or higher
- BLAS/LAPACK libraries
- Optional: CUDA for GPU acceleration
- Optional: Intel MKL for optimized math operations
Common issues:
- Missing dependencies:
# Install required packages (Ubuntu/Debian)
sudo apt-get install build-essential cmake liblapack-dev libblas-dev- AVX2 not supported:
# Disable AVX2 in CMakeLists.txt
set(COMPILER_SUPPORTS_AVX2 OFF)- CUDA not found:
# Disable GPU support
cmake -DUSE_GPU=OFF ..# Configure with GPU support
cmake -DUSE_GPU=ON ..Ensure you have CUDA toolkit installed and compatible GPU hardware.
// Create a 2-qubit system
quantum_geometric_tensor* qgt = create_quantum_tensor(2, 2, QGT_MEM_HUGE_PAGES);
// Initialize states
initialize_qubit_state(qgt, 0, QGT_QUBIT_INIT);
initialize_qubit_state(qgt, 1, QGT_QUBIT_INIT);// Apply Hadamard gate
apply_hadamard(qgt, 0, QGT_OP_VECTORIZED);
// Create entanglement
apply_cnot(qgt, 0, 1, QGT_OP_VECTORIZED);double probability;
measure_qubit_state(qgt, 0, &probability, QGT_QUBIT_MEASURE);Physical constraints ensure that quantum states remain valid and follow physical laws:
PhysicalConstraints constraints = {
.energy_threshold = 1.0,
.symmetry_tolerance = 1e-6
};
apply_physical_constraints(qgt, &constraints);- Use vectorized operations:
evolve_quantum_state(qgt, time_step, QGT_OP_VECTORIZED);- Enable GPU acceleration:
quantum_geometric_tensor* qgt = create_quantum_tensor(dim, spins, QGT_OP_GPU_OFFLOAD);- Use huge pages for large systems:
quantum_geometric_tensor* qgt = create_quantum_tensor(dim, spins, QGT_MEM_HUGE_PAGES);Common performance issues:
- Not using vectorized operations
- Not enabling compiler optimizations
- System too large for available memory
- Not using GPU for large computations
Operations automatically use multiple threads when appropriate:
// Will use multiple threads if beneficial
update_metric(qgt, QGT_OP_PARALLEL);Always free resources:
// Free quantum tensor
free_quantum_tensor(qgt);
// Free tensor network
physicsml_ttn_destroy(ttn);- Use tensor networks for efficient representation
- Enable huge pages for better memory performance
- Consider GPU acceleration for large computations
Always check return values:
quantum_geometric_tensor* qgt = create_quantum_tensor(dim, spins, flags);
if (!qgt) {
// Handle allocation failure
return 1;
}
qgt_error_t err = apply_physical_constraints(qgt, &constraints);
if (err != QGT_SUCCESS) {
// Handle constraint error
free_quantum_tensor(qgt);
return 1;
}QGT_SUCCESS: Operation completed successfullyQGT_ERROR_INVALID_ARGUMENT: Invalid parameterQGT_ERROR_OUT_OF_MEMORY: Memory allocation failedQGT_ERROR_COMPUTATION_FAILED: Numerical computation failed
- Run your program
- Open
examples/visualization/quantum_visualization.html - Load the generated state data
- Explore the interactive visualization
Yes, the library provides data export functions:
export_state_data(qgt, "state_data.json", QGT_EXPORT_JSON);Create custom operations using the provided API:
qgt_error_t custom_operation(quantum_geometric_tensor* qgt) {
// Implement custom logic
return QGT_SUCCESS;
}// Create network
TreeTensorNetwork* ttn = create_geometric_network(qgt, bond_dim);
// Optimize network
optimize_geometric_network(ttn, qgt, learning_rate, max_iterations);Check the examples/ directory:
examples/beginner/for basic examplesexamples/quantum_evolution_example.cfor time evolutionexamples/spin_system_example.cfor spin systems
File an issue on GitHub with:
- Minimal code to reproduce the issue
- System information
- Error messages or unexpected behavior
- Steps to reproduce
- Read the Beginner's Guide
- Study the Theory
- Check the API Documentation
- Join our community discussions