QVecOpt: An Efficient Storage and Computing Opti-mization Framework for Large-scale Quantum State Simulation

TL;DR

QVecOpt is a novel framework that significantly improves large-scale quantum state simulation efficiency on classical computers by integrating multiple optimization strategies, enabling simulations of more qubits with reduced memory and computational bottlenecks.

Contribution

The paper introduces QVecOpt, a comprehensive optimization framework that enhances quantum state simulation scalability and efficiency through amplitude pairing, cache, block storage, and parallel strategies.

Findings

Reduces single-qubit gate traversal complexity from O(2^n) to O(1)

Achieves nearly tenfold efficiency improvement in simulations of 16-29 qubits

Breaks memory bottlenecks of existing quantum simulation tools

Abstract

In response to the challenges in large-scale quantum state simulation on classical computing platforms, including memory limits, frequent disk I/O, and high computational complexity, this study builds upon a previously proposed hierarchical storage-based quantum simulation system and introduces an optimization framework, the Quantum Vector Optimization Framework (QVecOpt). QVecOpt integrates four strategies: amplitude pairing, cache optimization, block storage optimization, and parallel optimization. These collectively enhance state vector storage and computational scheduling. The amplitude pairing mechanism locates relevant amplitude pairs via bitwise XOR, reducing traversal complexity of single-qubit gates from $O(2^n)$ to $O(1)$. Cache optimization pre-allocates buffers and loads only required data, cutting disk I/O. Block storage optimization partitions the state vector for on-demand loading and local updates, reducing redundant access. Parallel optimization distributes the state vector across nodes for collaborative computation, achieving near-linear speedup. Complexity analysis shows that, compared with hierarchical storage simulation, the method reduces state vector traversals for single-qubit gates from $2^n$ to 1, removing the main bottleneck. It also lowers computational and I/O complexity from $O(2^n)$ to $O(2^n/C)$ and $O(2^n/B)$. In simulations of 16-29 qubits, efficiency improves nearly tenfold, breaking the memory bottleneck of existing tools and enabling high-bit quantum circuit simulations beyond traditional methods. This work provides an efficient, scalable solution for classical simulation of large-scale quantum computation with significant academic and practical value.