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 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 Benchpress, a benchmarking suite for evaluating the performance and range of functionality of multiple quantum computing software development kits. This suite consists of a collection of over 1,000 tests measuring key performance metrics for a wide variety of operations on quantum circuits composed of up to 930 qubits and O(106) two-qubit gates, as well as an execution framework for running the tests over multiple quantum software packages in a unified manner. Here we give a detailed overview of the benchmark suite and its methodology and generate representative results over seven different quantum software packages. The flexibility of the Benchpress framework enables benchmarking that not only keeps pace with quantum hardware improvements but also can preemptively gauge the quantum circuit processing costs of future device architectures. In this Resource, the authors present an open-source extensible benchmark tool, Benchpress, for evaluating the performance of mainstream quantum computing software. Benchpress was demonstrated to perform over 1,000 tests with up to 930 qubits to compare the performance of quantum software, providing insight into how to best use current programming stacks.
Quantum computing promises significant advancements over classical computing by enabling exponential speedups for specific problems. Despite its potential, quantum software development remains challenging due to its complexity, cost, and susceptibility to errors. Aligned with the novel paradigm of Quantum Software Engineering (QSE), this paper introduces a tool designed to automate the generation of quantum software components from truth tables. The tool simplifies the development process by allowing users to specify expected output values for given inputs, which the tool then translates into the corresponding quantum code. This approach reduces the manual effort required and helps ensure accuracy in quantum program development. Our method not only streamlines quantum software generation but also optimizes the resulting quantum circuits by minimizing unnecessary computations. The paper provides a detailed overview of the algorithm, tool implementation, and potential applications, highlighting the tool's efficacy in facilitating more efficient quantum software development.
Quantum computers have been at the bleeding edge of computing technology for nearly 40 years and promise to make several classically-difficult problems tractable. However, despite many hardware and algorithm breakthroughs, the software infrastructure that compiles programs for these devices requires further development in order to produce sufficiently scalable code. In this work we present a novel approach to compiling more efficient quantum programs. Rather than mapping individual programs onto quantum hardware, we capture the input as a high-level mathematical transform and generate a multitude of architecture-compliant programs directly from that specification, allowing us to search over a much larger space of implementations to select the best output. Specifically, this is achieved by casting circuit generation as a sparse matrix factorization task and recursively searching over a host of divide-and-conquer decomposition rules. By generating programs from the top-down, we retain high-level information about the program we are compiling and can explore algorithm-specific rewrites that are nearly impossible to recognize given only a program stream. We implement the proposed framework [33] [34] with SPIRAL [18], a code generation platform founded on the GAP [42] computer algebra system; we ultimately demonstrate that SPIRAL is a promising supplemental tool for future quantum frameworks.
Abstract Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, these new capabilities need software solutions that are able to effectively harness its power. However, developers may face significant challenges when developing and executing quantum software due to the limited availability of quantum computer hardware, high computational demands of simulating quantum computers on classical systems, and complicated technology stack to enable currently available accelerators into development environments. These limitations make it difficult for the developer to create an efficient workflow for quantum software development. In this paper, we investigate the potential of using remote computational capabilities in an efficient manner to improve the workflow of quantum software developers, by lowering the barrier of moving between local execution and computationally more efficient remote hardware and offering speedup in execution with simulator surroundings. The goal is to allow the development of more complex circuits and to support an iterative software development approach. In our experiment, with the solution presented in this paper, we have obtained up to 5 times faster circuit execution runtime, and enabled qubit ranges from 21 to 29 qubits with a simple plug-and-play kernel for the Jupyter notebook.
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.
Programming quantum computers is a complex endeavor that requires highly specialized knowledge. Given the growing momentum of quantum computing, high-level synthesis of quantum circuits from well-known classical languages could significantly benefit the ecosystem, allowing specialists to focus on the parts of an application where quantum computing offers the greatest advantage, while letting the compiler handle the boilerplate code. To bridge this gap, we present a high-level synthesis framework that translates C programs into quantum circuits through a sequence of modular and structured Multi-Level Intermediate Representation (MLIR) dialects. These dialects expose classical control and data flow in a form compatible with quantum constraints, enabling reversible and unitary circuit generation. Our framework maps conditionals, loops, and arithmetic operations, supports configurable bitwidths, and allows retargetability to different quantum backends. Furthermore, we demonstrate a correlation between classical program metrics—such as instruction count and cyclomatic complexity—and the performance characteristics of the resulting quantum circuits. To validate our methodology, we quantify compilation resource usage, showing a linear scaling of memory footprint and a quadratic scaling of CPU time as a function of quantum circuit complexity. Our tool generates circuits in OpenQASM format, facilitating integration with current quantum programming frameworks.