qblaze: An Efficient and Scalable Sparse Quantum Simulator

Classical simulation of quantum circuits is critical for the development of implementations of quantum algorithms: it does not require access to specialized hardware, facilitates debugging by allowing direct access to the quantum state, and is the only way to test on inputs that are too big for curr...

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Vydáno v:Proceedings of ACM on programming languages Ročník 9; číslo OOPSLA2; s. 444 - 470
Hlavní autoři: Venev, Hristo, Udomsrirungruang, Thien, Dimitrov, Dimitar, Gehr, Timon, Vechev, Martin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 09.10.2025
Témata:
Computing methodologies
Quantum mechanic simulation
Shared memory algorithms
quantum circuit simulation
parallel algorithms
sparse state vector
ISSN:2475-1421, 2475-1421
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Shrnutí:Classical simulation of quantum circuits is critical for the development of implementations of quantum algorithms: it does not require access to specialized hardware, facilitates debugging by allowing direct access to the quantum state, and is the only way to test on inputs that are too big for current NISQ computers. Many quantum algorithms rely on invariants that result in sparsity in the state vector. A sparse state vector simulator only computes with non-zero amplitudes. For important classes of algorithms, this results in an asymptotic improvement in simulation time. While promising prior work has investigated ways to exploit sparsity, it is still unclear what is the best way to scale sparse simulation to modern multi-core architectures. In this work, we address this challenge and present qblaze, a highly optimized sparse state vector simulator based on (i) a compact sorted array representation, and (ii) new, easily parallelizable and highly-scalable algorithms for all quantum operations. Our extensive experimental evaluation shows that qblaze is often orders-of-magnitude more efficient than prior sparse state vector simulators even on a single thread, and also that qblaze scales well to a large number of CPU cores. Overall, our work enables testing quantum algorithms on input sizes that were previously out of reach.
ISSN:2475-1421
2475-1421
DOI:10.1145/3763066