We propose a high-performance state-vector emulator of a gate-based quantum processors developed in the Rust programming language. It supports OpenQASM 2.0 programming language for quantum circuits specification and has a user-friendly Python based API. We present a wide range of numerical benchmarks of the emulator. We expect that our emulator will be used for design and validation of new quantum algorithms.
We propose a high-performance state-vector emulator of a gate-based quantum processors developed in the Rust programming language. It supports OpenQASM 2.0 programming language for quantum circuits specification and has a user-friendly Python based API. We present a wide range of numerical benchmarks of the emulator. We expect that our emulator will be used for design and validation of new quantum algorithms.
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.
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.
Researchers and industries are increasingly drawn to quantum computing for its computational potential. However, validating new quantum algorithms is challenging due to current quantum device limitations. Software simulators are time and memory-intensive, making hardware emulators an attractive alternative. This article introduces a digital architecture, designed to emulate quantum computing on low-tier Field-Programmable Gate Arrays (FPGAs), supporting Clifford+T and rotational gate sets. It simplifies and accelerates quantum algorithm verification using a RISC-like structure and efficiently handling sparse quantum gates. A dedicated compiler translates OpenQASM 2.0 into RISC-like instructions. The architecture is validated against the Qiskit state vector simulator, successfully emulating sixteen qubits on a Xilinx Kria KV260 SoM.
As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.
Although, there have been considerable advances in quantum technology, a great scale quantum computer will not be available until about a few years. In the meantime, it requires quantum emulators which allow to study the existing quantum algorithms and the design of new ones. We propose a flexible method to emulate quantum circuits which reduces design and processing time. In this context, we propose a design framework that takes advantage of field-programmable gate array (FPGA) parallel processing technology in combination with high-level programming tools provided by Vivado. In order to show the design method working Quantum Fourier Transform (QFT) is implemented. Every step of the design is described and circuit performance is studied for different cases of input vector data sizes.
In recent years, there has been a growing interest in the development of quantum emulation. However, existing studies often struggle to achieve broad applicability, high performance, and efficient resource and memory utilization. To address these challenges, we provide HPQEA, a quantum emulator based on the state-vector emulation approach. HPQEA includes three main features: a high-performance computing core, an optimized controlled-NOT gate computation strategy, and effective utilization of high-bandwidth memory. Verification and evaluation on the Alveo U280 board show that HPQEA can emulate quantum circuits with up to 30 qubits while maintaining high fidelity and low mean square error. It outperforms comparable FPGA-based systems by producing faster execution, supporting a wider range of algorithms, and requiring low hardware resources. Furthermore, it exceeds the Nvidia A100 in normalized gate speed for systems with up to 20 qubits. These results demonstrate the scalability and efficiency of HPQEA as a platform for emulating quantum algorithms.