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.
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.
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.
This paper is devoted to the development of quantum computing programming languages with simplified syntax and extended compilation. The key principles of creating an intuitive and functional syntax that lowers the entry barrier for programmers and specialists from related fields are considered. Simplification is achieved through high-level abstractions, declarative style, and automation of complex operations, such as qubit management and construction of quantum circuits. Extended compilation plays a central role in program optimization, error minimization, and adaptation to various quantum processor architectures. The integration of quantum and classical computing through hybrid approaches is also discussed. These innovations create the basis for the popularization of quantum technologies and their practical application in scientific and industrial problems.
Quantum computing hardware is advancing at a rapid pace, yet the lack of high-level programming abstractions remains a serious bottleneck in the development of new applications. Widely used frameworks still rely on gate-level circuit descriptions, causing the algorithm's functional intent to become lost in low-level implementation details, and hindering flexibility and reuse. While various high-level quantum programming languages have emerged in recent years - offering a significant step toward higher abstraction - many still lack support for classicallike expression syntax, and native constructs for useful quantum algorithmic idioms. This paper presents Qmod, a high-level quantum programming language designed to capture algorithmic intent in natural terms while delegating implementation decisions to automation. Qmod introduces quantum numeric variables and expressions, including digital fixed-point arithmetic tuned for compact representations and optimal resource usage. Beyond digital encoding, Qmod also supports nondigital expression modes phase and amplitude encoding - frequently exploited by quantum algorithms to achieve computational advantages. We describe the language's constructs, demonstrate practical usage examples, and outline future work on evaluating Qmod across a broader set of use cases.
Quantum computing hardware is advancing at a rapid pace, yet the lack of high-level programming abstractions remains a serious bottleneck in the development of new applications. Widely used frameworks still rely on gate-level circuit descriptions, causing the algorithm's functional intent to become lost in low-level implementation details, and hindering flexibility and reuse. While various high-level quantum programming languages have emerged in recent years - offering a significant step toward higher abstraction - many still lack support for classical-like expression syntax, and native constructs for useful quantum algorithmic idioms. This paper presents Qmod, a high-level quantum programming language designed to capture algorithmic intent in natural terms while delegating implementation decisions to automation. Qmod introduces quantum numeric variables and expressions, including digital fixed-point arithmetic tuned for compact representations and optimal resource usage. Beyond digital encoding, Qmod also supports non-digital expression modes - phase and amplitude encoding - frequently exploited by quantum algorithms to achieve computational advantages. We describe the language's constructs, demonstrate practical usage examples, and outline future work on evaluating Qmod across a broader set of use cases.
Classical computers are powerful but fundamentally mundane: they operate through explicit, step-by-step instructions that force programmers to think locally and procedurally. Quantum technologies, by contrast, resemble magical instruments, capable of acting on superpositions, correlations, and entire collections of states at once. Yet, despite their magical nature, quantum computers are still programmed as if they were ordinary machines, relying on long sequences of low-level operations that obscure the underlying algorithmic ideas. Magic, however, is not performed by improvisation alone: it requires a spellbook. In this paper we position Qutes as a book of spells for quantum programmers, providing high-level commands that capture complex quantum behaviour behind concise and expressive syntax. We focus on the manipulation of quantum arrays, which naturally arise in many algorithms but are notoriously cumbersome to manage at the circuit level. We introduce three new array-oriented spells in Qutes: global initialization, parallel pairwise comparison, and parallel (optionally controlled) swap. Individually, these spells encapsulate non-trivial quantum procedures; when combined, they enable programmers to express algorithms in a way that is immediate, readable, and surprisingly elegant. As a final demonstration, we show how these spells can be assembled to produce a remarkably compact implementation of Bubble Sort. While algorithmically simple, this example illustrates a broader message: when the right abstractions are available, quantum programming can feel less like engineering machinery and more like magic.
Quantum computing (QC) is an emerging computational paradigm that leverages the laws of quantum mechanics to perform elementary logic operations. Existing programming models for QC were designed with fault-tolerant hardware in mind, envisioning stand-alone applications. However, the susceptibility of near-term quantum computers to noise limits their stand-alone utility. To better leverage limited computational strengths of noisy quantum devices, hybrid algorithms have been suggested whereby quantum computers are used in tandem with their classical counterparts in a heterogeneous fashion. This modus operandi calls out for a programming model and a high-level programming language that natively and seamlessly supports heterogeneous quantum-classical hardware architectures in a single-source-code paradigm. Motivated by the lack of such a model, we introduce a language extension specification, called QCOR , which enables single-source quantum-classical programming. Programs written using the QCOR library–based language extensions can be compiled to produce functional hybrid binary executables. After defining QCOR’s programming model, memory model, and execution model, we discuss how QCOR enables variational, iterative, and feed-forward QC. QCOR approaches quantum-classical computation in a hardware-agnostic heterogeneous fashion and strives to build on best practices of high-performance computing. The high level of abstraction in the language extension is intended to accelerate the adoption of QC by researchers familiar with classical high-performance computing.