Quantum Computers are quickly becoming capable of solving certain tasks substantially faster than classical computers and the promise of quantum-driven advancements in research and economy continues to accelerate the development of quantum technology. However, most software development for quantum computers relies on the tedious manual implementation of quantum circuits on a very low level of abstraction, with tools such as the prominent IBM Qiskit SDK. In 2020, Silq, a quantum language to enable more intuitive and robust quantum development, was presented. While it substantially simplifies the write- and readability of quantum programs, Silq Code can only be run through its simulator on classical hardware. In comparison, Qiskit and its close integration with IBM’s Quantum Experience even enable users to run and evaluate quantum programs on physical quantum hardware. This paper proposes an automatic source-to-source translator for basic Silq Code and the extension of Qiskit by core concepts of Silq’s abstraction layers, such as Quantum Indexing and Quantum Control Flow.
Quantum Computers are quickly becoming capable of solving certain tasks substantially faster than classical computers and the promise of quantum-driven advancements in research and economy continues to accelerate the development of quantum technology. However, most software development for quantum computers relies on the tedious manual implementation of quantum circuits on a very low level of abstraction, with tools such as the prominent IBM Qiskit SDK. In 2020, Silq, a quantum language to enable more intuitive and robust quantum development, was presented. While it substantially simplifies the write- and readability of quantum programs, Silq Code can only be run through its simulator on classical hardware. In comparison, Qiskit and its close integration with IBM’s Quantum Experience even enable users to run and evaluate quantum programs on physical quantum hardware. This paper proposes an automatic source-to-source translator for basic Silq Code and the extension of Qiskit by core concepts of Silq’s abstraction layers, such as Quantum Indexing and Quantum Control Flow.
Quantum computing leverages the principles of quantum mechanics to perform computations far beyond the capabilities of classical systems, particularly in fields such as cryptography and optimization. However, current quantum programming languages often require low-level implementation, posing significant barriers for many developers due to their steep learning curve and limited abstraction. In response, we introduce Qutes, a high-level quantum programming language designed to simplify quantum algorithm development while maintaining the flexibility required for advanced applications. By abstracting complex quantum operations and allowing intuitive expressions through high-level constructs, Qutes enables users to write efficient quantum programs without extensive knowledge of quantum mechanics or circuit design. Built upon Qiskit, Qutes translates its syntax directly into executable quantum code, facilitating seamless integration with quantum hardware. This paper provides an overview of the language's architecture, core functionalities, and its ability to unify classical and quantum operations within a single framework. Additionally, we demonstrate Qutes' application in key quantum algorithms, showcasing its potential to make quantum programming more accessible and practical for a wider range of developers and researchers.
Abstract Quantum computing offers transformative capabilities by exploiting quantum mechanical principles to solve problems that are intractable for classical systems, particularly in areas like cryptography, optimization, and data analysis. However, most current quantum programming languages operate at a low level, requiring in-depth expertise in quantum mechanics and circuit theory, which presents a barrier to wider adoption. In this work, we introduce Qutes, a high-level quantum programming language that simplifies the development of quantum algorithms while preserving the flexibility needed for advanced applications. Qutes abstracts low-level quantum operations through intuitive syntax and high-level constructs, enabling developers to express complex algorithms without detailed circuit knowledge. Built atop Qiskit, Qutes transpiles seamlessly into executable code, ensuring compatibility with real quantum hardware. We present the architecture, language design, and hybrid classical-quantum integration of Qutes, and demonstrate its use through implementations of canonical quantum algorithms. Our results highlight Qutes’ potential to democratize quantum programming by lowering the entry threshold and accelerating prototyping for researchers and developers alike.
One of the greatest technological advancements in the last century lies in digital computer science. The idea of storing information and performing complicated calculations with the help of bits, i.e., 0 and 1. But due to a sudden surge in data, the classical computer system has been becoming weak in data processing. Quantum computers offer promising substantial speedup over classical computers for many applications. Quantum chip fabrication has made remarkable gains in recent years, with the number of qubits and fidelity growing. In general computing, a binary digit is the smallest unit of information or a bit. “In quantum computing, the term Qubit (Quantum Bit) serves the exact function of the term bit.” IBM Research released the IBMQ Experience in 2018, the first quantum computer that anyone can use and make accessible to a huge audience of different countries through cloud access. IBM also introduced the tool QISKIT (Quantum information software kit), which enables teachers, researchers, and developers to write coding and run their coding on quantum machines. It also includes different packages of quantum computing. In this paper, the author has discussed different steps to install qiskit. Mainly this paper focused on the “programming and application side of quantum computing.” Qiskit tools used in the python programming language. The “quantum circuits” are fabricated with the use of quantum gates and favorable algorithms with less execution time
One of the major obstacles to the adoption of quantum computing is the requirement to define quantum circuits at the quantum gate level. Many programmers are familiar with high-level or low-level programming languages but not quantum gates nor the low-level quantum logic required to derive useful results from quantum computers. The steep learning curve involved when progressing from quantum gates to complex simulations such as Shor's algorithm has proven too much for many developers. The purpose of this paper and the software presented within, addresses this challenge by providing a Software Development Kit (SDK), translation layer, emulator and a framework of techniques for executing Intel 8080/Z80 assembler on a quantum computer, i.e. all salient points of CPU execution, logic, arithmetic and bitwise manipulation will be executed on the quantum computer using quantum circuits. This provides a novel means of displaying the equivalency and interoperability of quantum and classical computers. Developers and researchers can use the SDK to write code in Intel 8080/Z80 assembler which is executed locally via traditional emulation and remotely on a quantum computer in parallel. The emulator features side-by-side code execution with visibility of the running quantum circuit and re-usable/overridable methods. This enables programmers to learn, reuse and contrast techniques for performing any traditional CPU based technique/instruction on a quantum computer; e.g. a programmer may know how to multiply and perform checks on a classical CPU but is not able to perform the same tasks in a quantum implementation, this SDK allows the programmer to pick and choose the methods they would like to use to fulfil their requirements. The SDK makes use of open-source software, specifically Python and Qiskit for the emulation, translation, API calls and execution of user supplied code or binaries.
Recent developments in quantum hardware indicate that systems featuring more than 50 physical qubits are within reach. At this scale, classical simulation will no longer be feasible and there is a possibility that such quantum devices may outperform even classical supercomputers at certain tasks. With the rapid growth of qubit numbers and coherence times comes the increasingly difficult challenge of quantum program compilation. This entails the translation of a high-level description of a quantum algorithm to hardware-specific low-level operations which can be carried out by the quantum device. Some parts of the calculation may still be performed manually due to the lack of efficient methods. This, in turn, may lead to a design gap, which will prevent the programming of a quantum computer. In this paper, we discuss the challenges in fully-automatic quantum compilation. We motivate directions for future research to tackle these challenges. Yet, with the algorithms and approaches that exist today, we demonstrate how to automatically perform the quantum programming flow from algorithm to a physical quantum computer for a simple algorithmic benchmark, namely the hidden shift problem. We present and use two tool flows which invoke RevKit. One which is based on ProjectQ and which targets the IBM Quantum Experience or a local simulator, and one which is based on Microsoft's quantum programming language Q#.
Quantum software development is challenged by quantum-mechanical semantics and fragmented software development kits (SDKs), which complicate cross-SDK code translation and execution. In this paper, we propose an LLM-based approach for translating executable programs between high-level quantum SDKs. We develop a quantum-aware translation workflow whose prompt design and validation reflect quantum semantics and accommodate the version evolution of quantum ecosystems. We conduct a preliminary evaluation of 4 LLM configurations on 43 tasks, translating Qiskit programs to PennyLane and Q#. LLMs achieve a zero-shot Pass@1 of up to 72.33%, while one-shot prompting further improves the translation effectiveness. These results suggest potential for quantum code translation by LLMs.