Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be encoded in a quantum circuit, a suitable device must be selected to execute it, and it must be compiled accordingly. This compilation step has a significant influence on the quality of the resulting solution. However, current state-of-the-art compilation tools treat the quantum circuit as a sequence of instructions without considering the actual application it realizes—wasting a yet untapped potential to increase the solution quality. In this work, a different approach is explored that explicitly incorporates the application considered and aims to optimize its solution quality during compilation. Initial results show the benefits of this approach: For an industry-inspired application of a quantum generative model, the proposed approach outperformed Qiskit’s most-optimized compilation scheme and led to better solution quality. Therefore, this work presents a first step towards application-aware compilation.
Quantum computing has made tremendous improvements in both software and hardware that have sparked interest in academia and industry to realize quantum computing applications. To this end, several steps are necessary: The underlying problem must be encoded in a quantum circuit, a suitable device must be selected to execute it, and it must be compiled accordingly. This compilation step has a significant influence on the quality of the resulting solution. However, current state-of-the-art compilation tools treat the quantum circuit as a sequence of instructions without considering the actual application it realizes—wasting a yet untapped potential to increase the solution quality. In this work, a different approach is explored that explicitly incorporates the application considered and aims to optimize its solution quality during compilation. Initial results show the benefits of this approach: For an industry-inspired application of a quantum generative model, the proposed approach outperformed Qiskit’s most-optimized compilation scheme and led to better solution quality. Therefore, this work presents a first step towards application-aware compilation.
Fueled by recent accomplishments in quantum computing hardware and software, an increasing number of problems from various application domains are being explored as potential use cases for this new technology. Similarly to classical computing, realizing an application on a particular quantum device requires the corresponding (quantum) circuit to be compiled so that it can be executed on the device. With a steadily growing number of available devices—each with their own advantages and disadvantages—and a wide variety of different compilation tools, the number of choices to consider when trying to realize an application is quickly exploding. Due to missing tool support and automation, especially end-users who are not quantum computing experts are easily left unsupported and overwhelmed. In this work, we propose a methodology that allows one to automatically select a suitable quantum device for a particular application and provides an optimized compiler for the selected device. The resulting framework—called the MQT Predictor —not only supports end-users in navigating the vast landscape of choices, it also allows mixing and matching compiler passes from various tools to create optimized compilers that transcend the individual tools. Evaluations of an exemplary framework instantiation based on more than 500 quantum circuits and seven devices have shown that—compared with both Qiskit’s and TKET’s most optimized compilation flows for all devices—the MQT Predictor produces circuits within the top-3 out of 14 baselines in more than 98% of cases while frequently outperforming any tested combination by up to 53% when optimizing for expected fidelity . Additionally, the framework is trained and evaluated for critical depth as another figure of merit to showcase its flexibility and generalizability—producing circuits within the top-3 in 89% of cases while frequently outperforming any tested combination by up to 400%. MQT Predictor is part of the Munich Quantum Toolkit (MQT) and publicly available as open-source on GitHub ( https://github.com/cda-tum/mqt-predictor ) and as an easy-to-use Python package ( https://pypi.org/p/mqt.predictor ).
Quantum circuit compilation is a critical step in realizing quantum algorithms on real quantum hardware. Traditional compilation methods often employ static optimization strategies, neglecting the unique constraints and characteristics of the target quantum device. This paper introduces a novel adaptive quantum circuit compilation framework designed to dynamically adjust circuit designs based on hardware-specific information, aiming to minimize gate count and, consequently, error rates. The core of the framework lies in integrating a hardware simulator directly within the compilation process. This allows the compiler to evaluate the performance of various circuit designs—considering factors like qubit connectivity, gate fidelity, and decoherence models—before committing to a final layout. The proposed approach represents a shift from static to adaptive optimization, providing a more nuanced and effective strategy for translating quantum algorithms into executable code on diverse quantum hardware platforms. This research demonstrates a significant potential improvement in quantum circuit efficiency and fidelity, addressing a key bottleneck in the advancement of quantum computing.
Traditional compiler optimization focuses on maximizing performance within the constraints of classical architectures. However, quantum computing presents a fundamentally different paradigm, demanding a new approach to software optimization. This paper proposes a hardware-aware compiler optimization technique, inspired by quantum computing principles, to bridge the gap between high-level programming languages and the specific characteristics of quantum hardware. The core idea is to translate high-level code into quantum circuits while explicitly considering factors like qubit connectivity, gate fidelity, and decoherence. This approach allows for optimization strategies tailored to the unique limitations and opportunities of quantum devices, potentially leading to significant performance improvements compared to generic quantum compilation methods. The compiler operates by analyzing the algorithmic complexity, identifying quantum-native operations, and strategically mapping them onto the available quantum hardware, thereby minimizing the impact of hardware constraints. This paper outlines the framework, key components, and potential optimizations within this hardware-aware compiler.
Realizing a conceptual quantum algorithm on an actual physical device necessitates the algorithm's quantum circuit description to undergo certain transformations in order to adhere to all constraints imposed by the hardware. In this regard, the individual high-level circuit components are first synthesized to the supported low-level gate-set of the quantum computer, before being mapped to the target's architecture-utilizing several optimizations in order to improve the compilation result. Specialized tools for this complex task exist, e.g., IBM's Qiskit, Google's Cirq, Microsoft's QDK, or Rigetti's Forest. However, to date, the circuits resulting from these tools are hardly verified, which is mainly due to the immense complexity of checking if two quantum circuits indeed realize the same functionality. In this paper, we propose an efficient scheme for quantum circuit equivalence checking-specialized for verifying results of the IBM Qiskit quantum circuit compilation flow. To this end, we combine characteristics unique to quantum computing, e.g., its inherent reversibility, and certain knowledge about the compilation flow into a dedicated equivalence checking strategy. Experimental evaluations confirm that the proposed scheme allows to verify even large circuit instances with tens of thousands of operations within seconds or even less, whereas state-of-the-art techniques frequently time-out or require substantially more runtime. A corresponding open source implementation of the proposed method is publicly available at https://github.com/iic-jku/qcec.
Any quantum computing application, once encoded as a quantum circuit, must be compiled before being executable on a quantum computer. Similar to classical compilation, quantum compilation is a sequential process with many compilation steps and numerous possible optimization passes. Despite the similarities, the development of compilers for quantum computing is still in its infancy—lacking mutual consolidation on the best sequence of passes, compatibility, adaptability, and flexibility. In this work, we take advantage of decades of classical compiler optimization and propose a reinforcement learning framework for developing optimized quantum circuit compilation flows. Through distinct constraints and a unifying interface, the framework supports the combination of techniques from different compilers and optimization tools in a single compilation flow. Experimental evaluations show that the proposed framework—set up with a selection of compilation passes from IBM’s Qiskit and Quantinuum’s TKET—significantly outperforms both individual compilers in 73% of cases regarding the expected fidelity. The framework is available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).
Any potential application of quantum computing, once encoded as a quantum circuit, needs to be compiled in order to be executed on a quantum computer. Deciding which qubit technology, which device, which compiler, and which corresponding settings are best for the considered problem—according to a measure of goodness—requires expert knowledge and is overwhelming for end-users from different domains trying to use quantum computing to their advantage. In this work, we treat the problem as a statistical classification task and explore the utilization of supervised machine learning techniques to optimize the compilation of quantum circuits. Based on that, we propose a framework that, given a quantum circuit, predicts the best combination of these options and, therefore, automatically makes these decisions for end-users. Experimental evaluations show that, considering a prototypical setting with 3000 quantum circuits, the proposed framework yields promising results: for more than three quarters of all unseen test circuits, the best combination of compilation options is determined. Moreover, for more than 95% of the circuits, a combination of compilation options within the top-three is determined—while the median compilation time is reduced by more than one order of magnitude. Furthermore, the resulting methodology not only provides end-users with a prediction of the best compilation options, but also provides means to extract explicit knowledge from the machine learning technique. This knowledge helps in two ways: it lays the foundation for further applications of machine learning in this domain and, also, allows one to quickly verify whether a machine learning algorithm is reasonably trained. The corresponding framework and the pre-trained classifier are publicly available on GitHub (https://github.com/cda-tum/MQTPredictor) as part of the Munich Quantum Toolkit (MQT).