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arXiv:2512.23183

LogosQ: A High-Performance and Type-Safe Quantum Computing Library in Rust

Shiwen An, Jiayi Wang, Konstantinos Slavakis

quant-phcs.SESubmitted Dec 29, 2025• Announced Dec 30, 2025• 0 citations• Revised Dec 30, 2025 (v2)
v1 · Dec 29, 2025v2 · Dec 30, 2025
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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, Qiskit), 6 to 22 times over Julia implementations (Yao), and competitive performance with Q sharp. Beyond performance, we validate numerical stability through variational quantum eigensolver (VQE) experiments on molecular hydrogen and XYZ Heisenberg models, achieving chemical accuracy even in edge cases where other libraries fail. By combining the safety of systems programming with advanced circuit optimization, LogosQ establishes a new standard for reliable and efficient quantum simulation.

Comments
https://github.com/zazabap/LogosQ https://github.com/zazabap/LogosQBenchmarks https://crates.io/crates/logosq https://logosqbook.vercel.app/

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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, Qiskit), 6 to 22 times over Julia implementations (Yao), and competitive performance with Q sharp. Beyond performance, we validate numerical stability through variational quantum eigensolver (VQE) experiments on molecular hydrogen and XYZ Heisenberg models, achieving chemical accuracy even in edge cases where other libraries fail. By combining the safety of systems programming with advanced circuit optimization, LogosQ establishes a new standard for reliable and efficient quantum simulation.
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's 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× for state preparation (QFT) and 2-5× for variational workloads over Python frameworks (PennyLane, Qiskit), 6-22× over Julia implementations (Yao.jl), and competitive performance with Q#. Beyond performance, we validate numerical stability through variational quantum eigensolver (VQE) experiments on molecular hydrogen and XYZ Heisenberg models, achieving chemical accuracy even in edge cases where other libraries fail. By combining the safety of systems programming with advanced circuit optimization, LogosQ establishes a new standard for reliable and efficient quantum simulation.
We present a new software package for efficient quantum circuit generation, designed to achieve optimal runtime performance. Despite being in an early stage of development, our implementation demonstrates significant advantages over existing tools. Using the quantum Fourier transform (QFT) as a benchmark, we show that our backend can generate circuits for systems with up to 2000 qubits faster than widely used frameworks such as Qiskit and Q#. This improvement is particularly relevant for applications where classical preprocessing time, including circuit generation, must be minimized to not diminish any potential quantum advantage - for example, in combinatorial optimization tasks. Additionally, our software provides high-level primitives for bit- and integer-level manipulations, offering a simplified interface for integration with high-level quantum programming languages.
We present Neural Quantum Manifold Forging (NQF), a novel algorithmic paradigm implemented in Rust, designed to overcome the intrinsic limitations of the Variational Quantum Eigensolver (VQE) in the Noisy Intermediate-Scale Quantum (NISQ) era. Unlike static approaches, NQF employs a native auto-differentiation engine and dynamic graphs to heuristically 'forge' the quantum circuit topology in real-time, effectively minimizing circuit depth and decoherence. By leveraging aligned memory allocation (SIMD/AVX-512) and massive parallelism via Rayon, NQF demonstrates up to a 12x processing speedup compared to Python-based frameworks. Validated results from the simulation of the Hydrogen (H2) molecule indicate a massive reduction in logic gate count while maintaining chemical accuracy, establishing NQF as the new standard for scalable quantum simulations.
Academic quantum computing platforms often face unique challenges in executing quantum workloads due to fragmented software environments and limited engineering support. Unlike commercial ecosystems, academic devices typically evolve without full-stack integration in mind, making it difficult to run complex applications—such as variational quantum algorithms (VQA)—reliably and efficiently. Issues such as incompatible software layers and lack of automated job management significantly increase the overhead of theory-experiment collaboration. To address these challenges, we develop a modular, end-to-end workflow that decouples application-layer code from low-level hardware control, automates circuit submission and result collection, and supports fine-grained circuit-level job scheduling and recovery. The architecture employs a dual-end application programming interface (API) design, enabling robust operation across unstable or resource-constrained hardware backends. For practical use, the framework is lightweight and user-friendly, allowing rapid prototyping of full-stack workflows using basic Python tools. We validate this workflow on a high-fidelity trapped-ion quantum computer by demonstrating a variational quantum eigensolver (VQE) experiment with a classically bootstrapped ansatz initialization technique. The system successfully executed over 60,000 circuits across multiple molecular test cases with minimal human intervention, highlighting the framework’s effectiveness in enabling reproducible, resilient quantum experimentation in academic settings.
We develop a high-performance simulator for variational quantum eigensolver (VQE), the major innovations include: (1) A differentiable matrix product state (MPS) based VQE simulator that seamlessly integrates MPS into the automatic differentiation framework, which overcomes the exponential memory growth of state-vector simulator and can efficiently calculate gradients with a cost independent of the number of parameters; (2) A dynamic scheme to distribute the gradient calculations to achieve good load balance; (3) A parallel adaptive VQE which integrates our differentiable MPS simulator to further enhance the simulation performance; (4) Study of real chemical systems with convergence to chemical accuracy using our simulator, achieving nearly linearly strong and weak scaling for chemical systems with up to 100 qubits. Our simulator provides an ideal test ground for VQE and paves the way of benchmarking large-scale VQE experiments on near-term quantum computers.