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An Introduction to Parallel Quantum Computation and Circuit Cutting through QAOA for Max-Cut
DescriptionOne of the challenges in practical quantum computing is the limited number of qubits. Most problems of interest require far more qubits than currently are available -- whether through execution on an existing physical quantum device or through simulation on multiple graphical processing units (GPUs). Circuit cutting (sometimes referred to as circuit knitting) is a divide-and-conquer approach to break down a quantum circuit into smaller pieces, each of which may require fewer qubits than the original circuit. Often these smaller circuits can be executed in parallel before their output is stitched back together for an approximation of the original circuit execution. To our knowledge, no educational materials have been disseminated broadly that provide students the opportunity to actively learn this technique and experiment with running quantum algorithms in parallel.
This self-contained educational module walks students through a visual example of circuit cutting through the Max-Cut problem, using the Quantum Approximate Optimization Algorithm (QAOA). Students will experiment with many of the design decisions that researchers face when implementing circuit cutting. In addition to quantum computing learning objectives, students will gain transferable skills in high performance computing as they simulate large scale quantum algorithms on a GPU using Message Processing Interface (MPI).
This self-contained educational module walks students through a visual example of circuit cutting through the Max-Cut problem, using the Quantum Approximate Optimization Algorithm (QAOA). Students will experiment with many of the design decisions that researchers face when implementing circuit cutting. In addition to quantum computing learning objectives, students will gain transferable skills in high performance computing as they simulate large scale quantum algorithms on a GPU using Message Processing Interface (MPI).
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