Are you interested in using Qaptiva HPC? Let us demonstrate how easy it is to perform quantum computing emulation in a HPC environment. We'll solve a Max-Cut combinatorial problem using the QAOA method as an example of Qaptiva HPC.
We connect to an HPC cluster equipped with Qaptiva HPC.
First, we create a quantum program with the Qaptiva library in Python. In the Python script, we generate a graph with 40 vertices on which we want to solve a Max-Cut problem.
The Max-Cut QAOA job is submitted to the distributed linear algebra (DLinAlg) simulator. We print the result of the simulation in the output file.
In the next phase, we write a SLURM batch script, to submit a job to the compute nodes. We configure the SLURM job to use 128 compute nodes for this simulation job in the batch parameters.
We load the Qaptiva HPC environment and execute the Python script we created during the first phase. Then, we can submit the SLURM job using the batch command, verify that the SLURM job has been added to the queue, and wait for the simulation job to complete on the computing nodes. Please hang tight while we finalise the simulation's processing on the cluster. We verify that the submitted SLURM job has been completed and that no jobs remain in the queue.
An output file and an error file has been generated by the SLURM job. We print the SLURM output file, which contains the simulation result.
Starting with Qaptiva HPC is quick, easy, and simple to use. Feel free to reach out to learn more about Qaptiva HPC.
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