Lazy Qubit Reordering for Accelerating Parallel State-Vector-based Quantum Circuit Simulation
Yusuke Teranishi, Shoma Hiraoka, Wataru Mizukami, Masao Okita,, Fumihiko Ino

TL;DR
This paper introduces novel GPU-based scheduling methods for quantum circuit simulation that significantly reduce communication overhead and accelerate key procedures like quantum state update and expectation value computation.
Contribution
It presents a new qubit reordering strategy with intentional delays and an extended scheduling method for GPU clusters, improving simulation efficiency.
Findings
Up to 54x acceleration in quantum state update
Up to 606x acceleration in expectation value computation
Reduced communication time by 15% in GPU cluster systems
Abstract
This paper proposes two quantum operation scheduling methods for accelerating parallel state-vector-based quantum circuit simulation using multiple graphics processing units (GPUs). The proposed methods reduce all-to-all communication caused by qubit reordering (QR), which can dominate the overhead of parallel simulation. Our approach eliminates redundant QRs by introducing intentional delays in QR communications such that multiple QRs can be aggregated into a single QR. The delays are carefully introduced based on the principles of time-space tiling, or a cache optimization technique for classical computers, which we use to arrange the execution order of quantum operations. Moreover, we present an extended scheduling method for the hierarchical interconnection of GPU cluster systems to avoid slow inter-node communication. We develop these methods tailored for two primary procedures in…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum and electron transport phenomena · Quantum Information and Cryptography
