多 shot 量子计算模拟 GPU 加速的高效技术
Efficient techniques to GPU Accelerations of Multi-Shot Quantum Computing Simulations
AI总结：
本文针对 GPU 上多 shot 量子计算模拟效率低的问题，提出将多个 shot 聚合到单个 GPU 内核并引入 shot 分支减少计算与内存开销的优化方法，相比此前实现实现了 10 倍加速。
摘要：
量子计算机正在变得可用于计算众多应用。然而，在经典计算机上模拟量子计算仍然要求很高但很有用，因为当前的量子计算机由于计算机资源、硬件限制、不稳定性和噪声而受到限制。提高经典计算机中量子计算模拟的性能将有助于量子计算机及其算法的发展。在经典计算机上进行量子计算模拟需要很长的执行时间，尤其是对于具有大量量子比特的量子电路，或者当为噪声模拟或带有中间测量的电路模拟大量 shot 时。图形处理单元（GPU）通过利用其计算能力和高带宽内存，适合加速量子计算机模拟，并且它们在模拟相对较大的量子比特电路方面具有很大优势。然而，GPU 在模拟带有噪声的多 shot 运行时效率低下，因为随机性阻碍了高度并行化。此外，由于 GPU 内核执行中的大量开销，GPU 在模拟具有少量量子比特的电路时存在劣势。在本文中，我们介绍了在 GPU 上进行多 shot 模拟的优化技术。我们将多次模拟的 shot 聚集到单个 GPU 内核执行中，通过调度由噪声引起的随机性来减少开销。此外，我们引入了 shot 分支，以减少多 shot 模拟的计算量和内存使用量。通过使用这些技术，我们相比之前的实现实现了 x10 的加速。
英文摘要：
Quantum computers are becoming practical for computing numerous applications. However, simulating quantum computing on classical computers is still demanding yet useful because current quantum computers are limited because of computer resources, hardware limits, instability, and noises. Improving quantum computing simulation performance in classical computers will contribute to the development of quantum computers and their algorithms. Quantum computing simulations on classical computers require long performance times, especially for quantum circuits with a large number of qubits or when simulating a large number of shots for noise simulations or circuits with intermediate measures. Graphical processing units (GPU) are suitable to accelerate quantum computer simulations by exploiting their computational power and high bandwidth memory and they have a large advantage in simulating relatively larger qubits circuits. However, GPUs are inefficient at simulating multi-shots runs with noises because the randomness prevents highly parallelization. In addition, GPUs have a disadvantage in simulating circuits with a small number of qubits because of the large overheads in GPU kernel execution. In this paper, we introduce optimization techniques for multi-shot simulations on GPUs. We gather multiple shots of simulations into a single GPU kernel execution to reduce overheads by scheduling randomness caused by noises. In addition, we introduce shot-branching that reduces calculations and memory usage for multi-shot simulations. By using these techniques, we speed up x10 from previous implementations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
