The Julia programming language is popular in the technical computing community due to a design that combines performance-oriented characteristics with expressive high-level code. While Python is popular among developers of existing quantum networking simulation software, leveraging Julia's features offers an alternative to current approaches that necessitate bridging Python with low-level languages like C/C++ for performance. This paper details the approach to constructing a Julia package based on the SQUANCH Python library in an attempt to evaluate the potential of Julia as a means of development.
The Julia programming language is popular in the technical computing community due to a design that combines performance-oriented characteristics with expressive high-level code. While Python is popular among developers of existing quantum networking simulation software, leveraging Julia's features offers an alternative to current approaches that necessitate bridging Python with low-level languages like C/C++ for performance. This paper details the approach to constructing a Julia package based on the SQUANCH Python library in an attempt to evaluate the potential of Julia as a means of development.
Quantum computing research relies heavily on numerical simulation, linear algebra, optimization, and scientific programming. While Python has become the dominant language for quantum computing frameworks such as Qiskit, Cirq, and PennyLane, Julia has emerged as a promising alternative due to its high-performance execution model and modern language design. This paper investigates the advantages of Julia over Python for quantum computing applications. We compare the two languages from theoretical, architectural, and practical perspectives, focusing on execution speed, memory efficiency, parallelization, numerical accuracy, and suitability for large-scale quantum simulation. Furthermore, we present an experimental study based on the Quantum Approximate Optimization Algorithm (QAOA) applied to the MaxCut problem. The benchmark was implemented independently in both Qiskit/Python and Julia and executed on the same hardware platform. Preliminary experimental results demonstrate a significant reduction in execution time when using Julia, with an observed speedup of approximately 3.9 x. Source code for both implementations has been made publicly available through the author's GitHub repository to facilitate reproducibility and further research.
We present Q u a n t u m T o o l b o x . j l , an open-source Julia package for simulating open quantum systems. Designed with a syntax familiar to users of Q u T i P (Quantum Toolbox in Python), it harnesses Julia's high-performance ecosystem to deliver fast and scalable simulations. The package includes a suite of time-evolution solvers supporting distributed computing and GPU acceleration, enabling efficient simulation of large-scale quantum systems. We also show how Q u a n t u m T o o l b o x . j l can integrate with automatic differentiation tools, making it well-suited for gradient-based optimization tasks such as quantum optimal control. Benchmark comparisons demonstrate substantial performance gains over existing frameworks. With its flexible design and computational efficiency, Q u a n t u m T o o l b o x . j l serves as a powerful tool for both theoretical studies and practical applications in quantum science.
Currently, quantum technologies are at the forefront of scientific thought. Large corporations are creating their own quantum supercomputers, developing quantum analogues of classical algorithms, and research is being conducted in the field of quantum cryptography. But since quantum computers have not yet become widespread, the question becomes relevant: how can ordinary users, scientists and researchers keep up with the development of science? One possible solution is to use various kinds of tools to emulate quantum computing on a local non-quantum computer. In addition, there is also the opportunity to have several qubits of IBM's quantum supercomputer available. As a rule, such tools are implemented in the form of libraries for various programming languages. Due to the fact that working with real quantum computers is available only to a narrow circle of researchers, emulators are simply necessary to test hypotheses or algorithms. This article examined the most popular quantum emulators used for quantum computing and allowing emulating the process of a quantum computer. Work was carried out to study quantum emulators, to identify and describe their individual characteristics, to make recommendations for a more convenient start to work with them, as well as to describe their advantages and disadvantages. A review of several libraries for the JavaScript, Python, C / C ++ languages was made, as well as a tool with a web interface (Quantum Programming Studio) and a set of tools from Microsoft (Microsoft Quantum Development Kit), the main language of which is Q #, is examined. At the end of the article, a conclusion is made regarding the considered tools.
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
Approximating molecular wave functions involves heavy numerical effort; therefore, codes for such tasks are written completely or partially in efficient languages such as C, C++, and Fortran. While these tools are dominant throughout quantum chemistry packages, the efficient development of new methods is often hindered by the complexity associated with code development. In order to ameliorate this scenario, some software packages take a dual approach where a simpler, higher-level language, such as Python, substitutes the traditional ones wherever performance is not critical. Julia is a novel, dynamically typed, programming language that aims to solve this two-language problem. It gained attention because of its modern and intuitive design, while still being highly optimized to compete with “low-level” languages. Recently, some chemistry-related projects have emerged exploring the capabilities of Julia. Herein, we introduce the quantum chemistry package Fermi.jl, which contains the first implementations of post-Hartree–Fock methods written in Julia. Its design makes use of many Julia core features, including multiple dispatch, metaprogramming, and interactive usage. Fermi.jl is a modular package, where new methods and implementations can be easily added to the existing code. Furthermore, it is designed to maximize code reusability by relying on general functions with specialized methods for particular cases. The feasibility of the project is explored through evaluating the performance of popular ab initio methods. It is our hope that this project motivates the usage of Julia within the community and brings new contributions into Fermi.jl.
In the field of electronic structure theory, many optimizations, ranging from accelerator offloading to exploitation of modern programming constructs, have been used to improve the performance of quantum chemistry software. However, one area that has remained largely unexplored is the space of novel programming languages that can provide unique benefits to quantum chemistry software development. One such programming language is Julia, an interpreted language designed to achieve the performance of high-performance, statically compiled languages. To assess the viability of Julia, the restricted Hartree-Fock method was implemented in a new quantum chemistry software package, JuliaChem. JuliaChem is a novel quantum chemistry software designed to take advantage of Julia as much as possible while achieving runtimes comparable to that of more established quantum chemistry software packages. Performance benchmarks against the popular quantum chemistry software package GAMESS, using the S22 test set of molecules, indicate that JuliaChem displays performance that is competitive with that of GAMESS. This shows that Julia could be a potentially useful tool for the field of electronic structure theory.