CUDA-Q Applications¶
This page contains a number of different applications implemented using CUDA-Q. All notebooks can be found here. To run these applications without a local installation, run the CUDA-Q application Hub launchable.
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Krylov Subspace Methods
Learn how the Krylov method uses the Hadamard test to predict the ground state energy of molecules. Also learn how to implement the same approach with the
mqpu backend and simulate execution on multiple QPUs in parallel.
Quantum-Selected Configuration Interaction
Learn how the QSCI method uses the observe and sample primitives.
Sample-Based Krylov Quantum Diagonalization (SKQD)
Learn how to implement the Sample-Based Krylov Quantum Diagonalization (SKQD) algorithm to predict the ground state energy of molecules.
The Hadamard Test
Learn about the Hadamard test and how it can be used to estimate expectation values. This notebook also explores how the Hadamard test can be used for Krylov subspace method and accelerated with the
mqpu backend to evaluate execution on multiple simulated QPUs in parallel.
Trotterized Hamiltonian Simulation
Trotterization is an approximation to enable simulation of a Hamiltonian. Learn how this technique works and simulate the dynamics of the Heisenberg model.
Quantum Volume
Benchmarking the performance of quantum computers, especially between different qubit modalities, is challenging. One method is to experimentally perform the quantum volume test. Learn how this test is performed and how it is implemented in CUDA-Q.
Readout Error Mitigation
Quantum computers are limited by their noise, which corrupts the outcome of applications. Error mitigation is a technique used to compensate for such errors via postprocessing. Learn how to combat noise in this CUDA-Q readout error mitigation tutorial.
Quantum Enhanced Auxiliary Field Quantum Monte Carlo
Quantum Enhanced Auxiliary Field Quantum Monte Carlo is an advanced variational technique for simulating molecular energies. Learn how NVIDIA and BASF collaborated to implement this technique.
Shor's Algorithm
Learn how to code the famous Shor's algorithm to factor a product of primes using CUDA-Q.
Generating the Electronic Hamiltonian
Learn how to generate the electronic hamiltonian and convert it to qubit hamiltonian using CUDA-Q.
Entanglement Accelerates Quantum Simulation
Learn how entanglement growth can reduce the Trotter error of the first-order product formula (PF1), recovering the result from the paper using NVIDIA CUDA-Q.
Noisy Circuit Simulation with PTSBE
Pre-Trajectory Sampling with Batch Execution (PTSBE) is an efficient method for sampling from noisy quantum circuits. Rather than simulating the full density matrix, PTSBE pre-samples unique noise trajectories and batches many shots across them, yielding orders-of-magnitude speedups for large shot counts. Based on the SC25 paper by Patti et al. (https://arxiv.org/abs/2504.16297).