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Design, Automation, and Test in Europe

tweedledum: A Compiler Companion for Quantum Computing

Bruno Schmitt, Giovanni De Micheli

Published Mar 14, 2022• 9 citations
Publisher

Abstract

This work presents tweedledum-an extensible open-source library aiming at narrowing the gap between high-level algorithms and physical devices by enhancing the expressive power of existing frameworks. For example, it allows designers to insert classical logic (defined at a high abstraction level, e.g., a Python function) directly into quantum circuits. We describe its design principles, concrete implementation, and, in particular, the library's core: An intuitive and flexible intermediate representation (IR) that supports different abstraction levels across the same circuit structure.

DOI
10.23919/date54114.2022.9774510

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This work presents tweedledum-an extensible open-source library aiming at narrowing the gap between high-level algorithms and physical devices by enhancing the expressive power of existing frameworks. For example, it allows designers to insert classical logic (defined at a high abstraction level, e.g., a Python function) directly into quantum circuits. We describe its design principles, concrete implementation, and, in particular, the library's core: An intuitive and flexible intermediate representation (IR) that supports different abstraction levels across the same circuit structure.
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.
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.
Abstract Quantum computing is aimed to solve tasks, which are believed to be exponentially hard to existing computational devices and tools. A prominent example of such classically hard problems is simulating complex quantum many-body systems, in particular, for quantum chemistry. However, solving realistic quantum chemistry problems with quantum computers encounters various difficulties, which are related, first, to limited computational capabilities of existing quantum devices and, second, to the efficiency of algorithmic approaches. In the present work, we address the algorithmic side of quantum chemistry applications by introducing a Python 3 code library, whose primary objective is to speed up the development of variational quantum algorithms for electronic structure problems. We describe the various features and capabilities of this library, including its ease in constructing customized versions of variational quantum algorithms. We elucidate how the developed library allows one to design quantum circuits and enable for the efficient execution of quantum algorithms. Furthermore, the library facilitates the integration of classical and quantum algorithms for hybrid computations and helps to realize the cross-verification of data with traditional computational methods, thereby enhancing the overall reliability of quantum chemistry simulations.
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.
In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing.
In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing.