Quantum programs are notoriously difficult to code and verify due to unintuitive quantum knowledge associated with quantum programming. Automated tools relieving the tedium and errors associated with low-level quantum details would hence be highly desirable. In this paper, we initiate the study of program synthesis for quantum unitary programs that recursively define a family of unitary circuits for different input sizes, which are widely used in existing quantum programming languages. Specifically, we present QSynth, the first quantum program synthesis framework, including a new inductive quantum programming language, its specification, a sound logic for reasoning, and an encoding of the reasoning procedure into SMT instances. By leveraging existing SMT solvers, QSynth successfully synthesizes 10 quantum unitary programs including quantum arithmetic programs, quantum eigenvalue inversion, quantum teleportation and Quantum Fourier Transformation, which can be readily transpiled to executable programs on major quantum platforms, e.g., Q#, IBM Qiskit, and AWS Braket.
Quantum programs are notoriously difficult to code and verify due to unintuitive quantum knowledge associated with quantum programming. Automated tools relieving the tedium and errors associated with low-level quantum details would hence be highly desirable. In this paper, we initiate the study of program synthesis for quantum unitary programs that recursively define a family of unitary circuits for different input sizes, which are widely used in existing quantum programming languages. Specifically, we present QSynth, the first quantum program synthesis framework, including a new inductive quantum programming language, its specification, a sound logic for reasoning, and an encoding of the reasoning procedure into SMT instances. By leveraging existing SMT solvers, QSynth successfully synthesizes 10 quantum unitary programs including quantum arithmetic programs, quantum eigenvalue inversion, quantum teleportation and Quantum Fourier Transformation, which can be readily transpiled to executable programs on major quantum platforms, e.g., Q#, IBM Qiskit, and AWS Braket.
A key challenge of quantum programming is uncomputation: the reversible deallocation of qubits. And while there has been much recent progress on automating uncomputation, state-of-the-art methods are insufficient for handling today's expressive quantum programming languages. A core reason is that they operate on primitive quantum circuits, while quantum programs express computations beyond circuits, for instance, they can capture families of circuits defined recursively in terms of uncomputation and adjoints. In this paper, we introduce the first modular automatic approach to synthesize correct and efficient uncomputation for expressive quantum programs. Our method is based on two core technical contributions: (i) an intermediate representation (IR) that can capture expressive quantum programs and comes with support for uncomputation, and (ii) modular algorithms over that IR for synthesizing uncomputation and adjoints. We have built a complete end-to-end implementation of our method, including an implementation of the IR and the synthesis algorithms, as well as a translation from an expressive fragment of the Silq programming language to our IR and circuit generation from the IR. Our experimental evaluation demonstrates that we can handle programs beyond the capabilities of existing uncomputation approaches, while being competitive on the benchmarks they can handle. More broadly, we show that it is possible to benefit from the greater expressivity and safety offered by high-level quantum languages without sacrificing efficiency.
A key challenge of quantum programming is uncomputation: the reversible deallocation of qubits. And while there has been much recent progress on automating uncomputation, state-of-the-art methods are insufficient for handling today's expressive quantum programming languages. A core reason is that they operate on primitive quantum circuits, while quantum programs express computations beyond circuits, for instance, they can capture families of circuits defined recursively in terms of uncomputation and adjoints. In this paper, we introduce the first modular automatic approach to synthesize correct and efficient uncomputation for expressive quantum programs. Our method is based on two core technical contributions: (i) an intermediate representation (IR) that can capture expressive quantum programs and comes with support for uncomputation, and (ii) modular algorithms over that IR for synthesizing uncomputation and adjoints. We have built a complete end-to-end implementation of our method, including an implementation of the IR and the synthesis algorithms, as well as a translation from an expressive fragment of the Silq programming language to our IR and circuit generation from the IR. Our experimental evaluation demonstrates that we can handle programs beyond the capabilities of existing uncomputation approaches, while being competitive on the benchmarks they can handle. More broadly, we show that it is possible to benefit from the greater expressivity and safety offered by high-level quantum languages without sacrificing efficiency.
In recent years, there has been a proliferation of quantum algorithms, primarily due to their exponential speedup over their classical counterparts. Quantum algorithms find applications in various domains, including machine learning, molecular simulation, and cryptography. However, extensive knowledge of linear algebra and quantum mechanics are required to program a quantum computer, which might not be feasible for traditional software programmers. Moreover, current quantum programming paradigm is difficult to scale and integrate quantum circuits to achieve complex functionality. To this end, in this paper, we introduce QHLS, a quantum high-level synthesis (HLS) framework. To the best of our knowledge, this is the first HLS framework for quantum circuits. The proposed QHLS allows quantum programmers to start with high-level behavioral descriptions (e.g., C, C++) and automatically generate the corresponding quantum circuit; thus, reducing the complexity of programming a quantum computer. Our experimental results demonstrate the success of QHLS in translating high-level behavioral software programs containing arithmetic, logical, and conditional statements.
Current quantum programs are mainly designed at the level of quantum gates acting on individual qubits; on a large scale and for complex problems this may involve a high cognitive load on the programmer, making the program specification nontrivial and error-prone. In this context, providing quantum programming with higher abstraction mechanisms will assist in making this task more manageable and robust against design errors. In this work, a conceptual framework is addressed following the notion of the whole quantum computation as a structure composed of quantum registers representing each an undivided entity. Thus, computation progresses through semantically well-defined transformations that act on, or entangle, quantum registers, thereby modifying the global state. Ultimately, the program reaches the desired state by following a specific composition strategy. With this in mind, high-level syntax is presented through an algebraic formalism that bridges them with their low-level semantics. Proposed syntax is based on certain well-know operations used on quantum algorithms that apply phase shifts upon logical condition satisfaction or leverage on parallel evaluation. Based solely on the formalized operations, a quantum satisfiability modulo theories (SMT) solver can be designed. At its core, this work contributes to establishing some methodological principles towards realizing a high-level quantum structured programming.
Quantum computing has been shown to provide exponential performance improvements in several tasks, such as cryptography, healthcare, etc. This article presents a new framework for quantum high-level synthesis, called QHLS, that aims to facilitate programmers using quantum computers. Currently, quantum-computer programmers need extensive linear algebra and quantum mechanics knowledge, which can be challenging for traditional software programmers. Additionally, the current quantum programming paradigm is not scalable, and it can be difficult to combine quantum circuits to create a more complex functionality. QHLS addresses these issues by enabling the automatic generation of quantum circuit descriptions directly from high-level behavioral specifications (using languages like C or C++). This simplifies the programming of a quantum computer, making it more accessible to a wider range of programmers. Our experiments show that QHLS can successfully translate high-level software programs containing various types of statements (such as arithmetic, logical, and conditional operations) into functionally equivalent quantum circuits.
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.