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Design, Automation, and Test in Europe

Towards High-Level Synthesis of Quantum Circuits

Chao Lu, Christian Pilato, Kanad Basu

Published Apr 1, 2023• 1 citation
Publisher

Abstract

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.

DOI
10.23919/date56975.2023.10137010

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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.
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.
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.
Although the concept of quantum computing has existed for decades, the technology needed to successfully implement a quantum computing system has not yet reached the level of sophistication, reliability, and scalability necessary for commercial viability until very recently. Significant progress on this front was made in the past few years, with IBM planning to create a 1000-qubit chip by the end of 2023, and Google already claiming to have achieved quantum supremacy. Other major industry players such as Intel and Microsoft have also invested significant amounts of resources into quantum computing research. Any viable computing system requires both hardware and software to work together harmoniously in order to perform useful computations. While the achievements of IBM and other companies represent a large step forward for quantum hardware, many gaps remain to be filled with respect to the corresponding software. Specifically, there is currently no clear path towards a complete process for translating quantum algorithms into physical operations that are directly executable on quantum hardware. Such a process is analogous to a compiler that translates programs written in a high-level language into executable machine instructions on a conventional digital computer, and it is necessary if quantum computers are to be harnessed to perform practically useful computations. Existing work has addressed individual components of this process, but so far no unified method for translating the whole of a quantum algorithm into executable operations has been described. I make substantial progress towards filling this gap by describing a set of high-level and low-level quantum circuit design techniques, which when taken together reduce the need of a circuit designer to be concerned with low-level details. On the high-level side, I describe an approach or strategy to designing quantum oracles for Grover's algorithm that allows it to be applied to several types of problems. This approach involves designing oracles in terms of high-level blocks such as counters, multiplexers, comparators, and arbitrary Boolean functions. The implementations of these blocks in terms of lower-level quantum gates are demonstrated in a way that makes it clear that scaled-up versions of them can be generated in a completely automated fashion. For a specific class of problems, which I call state-space path planning problems, I also introduce a paradigm for quantum oracle design that involves representing the problem in terms of individual states and moves. Problems of this sort have applications in robotics and games. Low-level techniques that I introduce
A major hurdle to the deployment of quantum linear systems algorithms and recent quantum simulation algorithms lies in the difficulty to find inexpensive reversible circuits for arithmetic using existing hand coded methods. Motivated by recent advances in reversible logic synthesis, we synthesize arithmetic circuits using classical design automation flows and tools. The combination of classical and reversible logic synthesis enables the automatic design of large components in reversible logic starting from well-known hardware description languages such as Verilog. As a prototype example for our approach we automatically generate high quality networks for the reciprocal 1/x, which is necessary for quantum linear systems algorithms.
A major hurdle to the deployment of quantum linear systems algorithms and recent quantum simulation algorithms lies in the difficulty to find inexpensive reversible circuits for arithmetic using existing hand coded methods. Motivated by recent advances in reversible logic synthesis, we synthesize arithmetic circuits using classical design automation flows and tools. The combination of classical and reversible logic synthesis enables the automatic design of large components in reversible logic starting from well-known hardware description languages such as Verilog. As a prototype example for our approach we automatically generate high quality networks for the reciprocal 1/x1/x1/x, which is necessary for quantum linear systems algorithms.