LogosQ: A High-Performance and Type-Safe Quantum Computing Library in Rust
Shiwen An, Jiayi Wang, Konstantinos Slavakis

TL;DR
LogosQ is a Rust-based quantum computing library that offers high performance, compile-time safety, and advanced optimization techniques, significantly outperforming existing Python and Julia frameworks while ensuring numerical stability in quantum simulations.
Contribution
This work introduces LogosQ, the first high-performance, type-safe quantum computing library in Rust that combines novel optimizations with compile-time correctness guarantees.
Findings
Speedups of up to 900x in state preparation and 2-5x in variational workloads.
Achieved chemical accuracy in VQE experiments on molecular hydrogen.
Outperforms Python frameworks and is competitive with Q sharp in performance.
Abstract
Developing robust and high performance quantum software is challenging due to the dynamic nature of existing Python-based frameworks, which often suffer from runtime errors and scalability bottlenecks. In this work, we present LogosQ, a high performance backend agnostic quantum computing library implemented in Rust that enforces correctness through compile time type safety. Unlike existing tools, LogosQ leverages Rust static analysis to eliminate entire classes of runtime errors, particularly in parameter-shift rule gradient computations for variational algorithms. We introduce novel optimization techniques, including direct state-vector manipulation, adaptive parallel processing, and an FFT optimized Quantum Fourier Transform, which collectively deliver speedups of up to 900 times for state preparation (QFT) and 2 to 5 times for variational workloads over Python frameworks (PennyLane,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Machine Learning in Materials Science · Parallel Computing and Optimization Techniques
