We introduce Chisel-Q, a high-level functional language for generating quantum circuits. Chisel-Q permits quantum computing algorithms to be constructed using the meta-language features of Scala and its embedded DSL Chisel. With Chisel-Q, designers of quantum computing algorithms gain access to high-level, modern language features and abstractions. We describe a synthesis flow that transforms Chisel-Q into an explicit quantum circuit in the Quantum Assembly Language (QASM) format. We also discuss several optimizations to reduce the generated hardware cost. The Chisel-Q tool includes resource and performance estimation which can be used to compare different implementations of the same functionality. We compare the output of the generic Chisel-Q synthesis flow with hand-tuned versions of well-known quantum circuits.
We introduce Chisel-Q, a high-level functional language for generating quantum circuits. Chisel-Q permits quantum computing algorithms to be constructed using the meta-language features of Scala and its embedded DSL Chisel. With Chisel-Q, designers of quantum computing algorithms gain access to high-level, modern language features and abstractions. We describe a synthesis flow that transforms Chisel-Q into an explicit quantum circuit in the Quantum Assembly Language (QASM) format. We also discuss several optimizations to reduce the generated hardware cost. The Chisel-Q tool includes resource and performance estimation which can be used to compare different implementations of the same functionality. We compare the output of the generic Chisel-Q synthesis flow with hand-tuned versions of well-known quantum circuits.
Abstract We present a computational method to automatically design the n -qubit realisations of quantum algorithms. Our approach leverages a domain-specific language (DSL) that enables the construction of quantum circuits via modular building blocks, making it well-suited for evolutionary search. In this DSL quantum circuits are abstracted beyond the usual gate-sequence description and scale automatically to any problem size. This enables us to learn the algorithm structure rather than a specific unitary implementation. We demonstrate our method by automatically designing three known quantum algorithms—the Quantum Fourier Transform, the Deutsch-Jozsa algorithm, and Grover’s search. Remarkably, we were able to learn the general implementation of each algorithm by considering examples of circuits containing at most 5-qubits. Our method proves robust, as it maintains performance across increasingly large search spaces. Convergence to the relevant algorithm is achieved with high probability and with moderate computational resources.
This paper presents a parameterized hardware generator for quantum gate emulation, designed to accelerate quantum circuit simulation through specialized hardware architectures. The generator is implemented in the Chisel hardware construction language, enabling automatic production of synthesizable SystemVerilog code from user-defined design parameters such as floating-point precisions, pipeline depth, and the number of qubits. Functional validation shows the accuracy of the generated designs, while field-programmable gate array (FPGA)-based prototyping demonstrates efficient performance for the emulation of 1 to 4-qubit quantum gates. The results highlight the potential of hardware generation frameworks to provide scalable and flexible acceleration for future quantum circuit research.
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.
Quantum assembly languages are machine-independent languages that traditionally describe quantum computation in the circuit model. Open quantum assembly language (OpenQASM 2) was proposed as an imperative programming language for quantum circuits based on earlier QASM dialects. In principle, any quantum computation could be described using OpenQASM 2, but there is a need to describe a broader set of circuits beyond the language of qubits and gates. By examining interactive use cases, we recognize two different timescales of quantum-classical interactions: real-time classical computations that must be performed within the coherence times of the qubits, and near-time computations with less stringent timing. Since the near-time domain is adequately described by existing programming frameworks, we choose in OpenQASM 3 to focus on the real-time domain, which must be more tightly coupled to the execution of quantum operations. We add support for arbitrary control flow as well as calling external classical functions. In addition, we recognize the need to describe circuits at multiple levels of specificity, and therefore we extend the language to include timing, pulse control, and gate modifiers. These new language features create a multi-level intermediate representation for circuit development and optimization, as well as control sequence implementation for calibration, characterization, and error mitigation.
Quantum computing holds transformative potential, but its adoption is hindered by the complexity of generating efficient, hardware-specific code. This work presents a modular, extensible compiler framework that bridges high-level quantum languages with diverse hardware architectures. The framework consists of three modules: a front-end for parsing quantum code into a hardware-agnostic intermediate representation (IR), an optimization module for enhancing quantum circuits through gate synthesis, qubit routing, and error mitigation, and a back-end for generating hardware-specific instructions. Major contributions include a hardware-agnostic IR for cross-platform compatibility, optimization techniques to reduce gate complexity and noise, and hardware-specific adaptations to improve execution fidelity. A practical demonstration optimizes quantum circuits, highlighting the impact of hardware constraints. Comparative analysis of IBM Quantum and IonQ platforms underscores the role of qubit connectivity and noise resilience in algorithmic performance. This scalable framework enhances quantum software development and efficient hardware utilization.
Abstract Quantum computers promise to transform our notions of computation by offering a completely new paradigm. To achieve scalable quantum computation, optimizing compilers and a corresponding software design flow will be essential. We present a software architecture for compiling quantum programs from a high-level language program to hardware-specific instructions. We describe the necessary layers of abstraction and their differences and similarities to classical layers of a computer-aided design flow. For each layer of the stack, we discuss the underlying methods for compilation and optimization. Our software methodology facilitates more rapid innovation among quantum algorithm designers, quantum hardware engineers, and experimentalists. It enables scalable compilation of complex quantum algorithms and can be targeted to any specific quantum hardware implementation.