Variational Quantum Algorithms and dynamic circuits are hybrid programs that interleave quantum operations with classical control flow, mid-circuit measurement, typed classical data, and parameter binding. Building such programs by hand is demanding, requiring expertise in quantum mechanics, circuit design, and a quantum SDK, so low-code platforms have been proposed to capture them as graphical models that a pipeline transforms into code. The pipeline this thesis builds on emits OpenQASM 3, but that representation becomes useful only once a mainstream SDK can execute it. The obstacle is layered. An SDK’s circuit representation holds a subset of OpenQASM 3, and its documented importer parses a smaller subset still. For these hybrid programs the importer cannot ingest the typed classical declarations and expressions they carry, even where the SDK’s own representation can hold them. This thesis closes that gap with a structure-preserving export step that lowers the pipeline’s OpenQASM 3 into executable SDK code, building the target’s representation directly rather than routing through its importer. A complementary backend extension carries structured loops into that representation rather than unrolling them, which matters for loops driven by measurement. The exporter is provider-based and aims to be SDK-independent. Qiskit is the realized target, chosen because its representation can hold the hybrid structure these programs need, and a second, proof-of-concept provider reaches Amazon Braket. Per-construct, end-to-end, and scenario tests confirm that supported models compile to executable Qiskit programs, with structured classical control flow, typed classical data, and symbolic parameters preserved at circuit level.
Variational Quantum Algorithms and dynamic circuits are hybrid programs that interleave quantum operations with classical control flow, mid-circuit measurement, typed classical data, and parameter binding. Building such programs by hand is demanding, requiring expertise in quantum mechanics, circuit design, and a quantum SDK, so low-code platforms have been proposed to capture them as graphical models that a pipeline transforms into code. The pipeline this thesis builds on emits OpenQASM 3, but that representation becomes useful only once a mainstream SDK can execute it. The obstacle is layered. An SDK’s circuit representation holds a subset of OpenQASM 3, and its documented importer parses a smaller subset still. For these hybrid programs the importer cannot ingest the typed classical declarations and expressions they carry, even where the SDK’s own representation can hold them. This thesis closes that gap with a structure-preserving export step that lowers the pipeline’s OpenQASM 3 into executable SDK code, building the target’s representation directly rather than routing through its importer. A complementary backend extension carries structured loops into that representation rather than unrolling them, which matters for loops driven by measurement. The exporter is provider-based and aims to be SDK-independent. Qiskit is the realized target, chosen because its representation can hold the hybrid structure these programs need, and a second, proof-of-concept provider reaches Amazon Braket. Per-construct, end-to-end, and scenario tests confirm that supported models compile to executable Qiskit programs, with structured classical control flow, typed classical data, and symbolic parameters preserved at circuit level.
The development of quantum programs is becoming a reality due to the rapid advancement of quantum computing. Over the past few years, a multitude of hardware platforms, algorithms, and programming languages have emerged to support this paradigm. By the very nature of Quantum Mechanics principles, there is an enormous change of philosophy when building quantum programs, which operate in a probabilistic space, unlike the deterministic behaviour shown by classical programming languages. These conceptual differences can be overcome by using techniques and tools of Software Engineering. In this paper, we apply Model-Driven Engineering techniques in a systematic way to ease the generation of quantum programs and we apply it to solve the satisfiability problem, very important in many engineering domains like verification of discrete systems and test of integrated circuits. To that aim, we contribute with a metamodel for representing quantum circuits and a model-to-text transformation to generate working IBM Qiskit code. This model-driven infrastructure is employed to automatically generate quantum programs from SAT equations through a model-to-model transformation that embeds Grover’s algorithm. Besides, we provide formulas for calculating the number of required quantum elements from SAT equations, crucial in the current context of limited quantum resources. The interoperability with other tools and the extensibility to target additional quantum platforms is guaranteed thanks to the use of a model-based toolchain. We cover several usage scenarios to validate the approach, providing exemplary SAT equations, the generated Qiskit code and the results of executing this code in IBM Quantum infrastructure.
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.
Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Variational Quantum Eigensolvers (VQE), yet transpiling these programs across frameworks remains challenging due to inconsistent support for control flow and measurement semantics. We present a transpilation pipeline that converts OpenQASM 3.0 programs with classical control structures (conditionals and bounded loops) into optimized CUDA-Q C++ kernels, leveraging CUDA-Q's native mid-circuit measurement and host-language control flow to translate dynamic patterns without static circuit expansion. Our open-source framework is validated on comprehensive test suites derived from IBM Quantum's classical feedforward guide, including conditional reset, if-else branching, multi-bit predicates, and sequential feedforward, and on VQE-style parameterized circuits with runtime parameter optimization. Experiments show that the resulting CUDA-Q kernels reduce circuit depth by avoiding branch duplication, improve execution efficiency via low-latency classical feedback, and enhance code readability by directly mapping OpenQASM 3.0 control structures to C++ control flow, thereby bridging OpenQASM 3.0's portable circuit specification with CUDA-Q's performance-oriented execution model for NISQ-era applications requiring dynamic circuit capabilities.
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.
As quantum software stacks scale up to support future fault-tolerant quantum hardware and algorithms, quantum compilation is becoming an increasingly important component of the stack. How do we ensure that our software stacks support dynamic algorithms, including patterns such as mid-circuit measurement feedforward and repeat-until-success, with hundreds of logical qubits and billions of quantum operations? To do so, we must re-think how we represent quantum programs beyond straight-line circuits, to representations that include classical structure and dynamism, and make this the default representation to consider when performing quantum compilation. In this work, we present important patterns and algorithms from fault-tolerant quantum applications which admit a structured representation that we argue is crucial to preserve, and set a challenge to the community to compile such representations without unrolling them into straight-line quantum circuits. We also explore the status quo of structured program support in quantum software, and ask ourselves the rhetorical question: how much more efficient can we make quantum compilation tooling when we take into account the additional information from classical structure?
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.