Quantum Emulators: CPU, single GPU and multiple GPUs performance comparison

Digital quantum computing promises new pathways for problem solving. However, currently available gate-based quantum computers still suffer from short decoherence timings and noisy results. In the meantime, quantum emulators can offer a way to test and refine quantum algorithms. They reproduce classically the behavior of quantum computers at the cost of large memory requirements: the memory needed doubles with each additional qubits emulated. Emulators can be set-up on a large range of machines, from laptops to supercomputers. In this paper, we compare the capabilities of quantum emulators built on the EuroHPC Karolina server using one CPU, one GPU or multiple GPUs in a single node. In terms of the number of qubits emulated, we show that while only 20+ qubits can be emulated using a single CPU, a single GPU brings this number to 30+. We also compare the timing performances, both for randomly generated quantum circuits and for Shor’s factorization algorithm. For randomly generated circuits with same depth and width, the emulators using a GPU and eight GPUs respectively are up to 20 and 180 times faster than the CPU-based emulator. For Shor’s algorithm which can have more than a million gates, the emulators using a GPU and eight GPUs respectively are up to 11 and 20 times faster than the CPU-based emulator. This benchmark of different types of quantum emulators on the Karolina server helps to choose the right parameters and make the most of the Karolina resources for future scientific work involving quantum emulation.