Memory Management Strategies for Software Quantum Simulators

Abstract

Software quantum simulators are essential tools for designing and testing quantum algorithms on classical computing architectures, especially given the current limitations of physical quantum hardware. This work focuses on studying and evaluating memory management strategies for scalable quantum state simulation. We examine full-state representation, dynamic state pruning, shared-memory parallelization with OpenMP, distributed memory execution using MPI, and error-bounded floating-point compression with ZFP. These techniques are implemented in a prototype simulator and assessed using the quantum Fourier transform as a benchmark, with performance compared against leading open-source simulators such as Intel-QS, QuEST, and qsim. The results show the trade-offs between computational overhead and memory efficiency, and demonstrate that hybrid approaches combining distributed memory and compression can significantly extend the number of qubits that can be simulated. This work contributes practical insights for improving the scalability of software quantum simulators on classical hardware through optimized memory usage.