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 ).
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 ).
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).
Leveraging quantum computers for optimization problems holds promise across various application domains. Nevertheless, utilizing respective quantum computing solvers requires describing the optimization problem according to the Quadratic Unconstrained Binary Optimization (QUBO) formalism and selecting a proper solver for the application of interest with a reasonable setting. Both demand significant proficiency in quantum computing, QUBO formulation, and quantum solvers, a background that usually cannot be assumed by end users who are domain experts rather than quantum computing specialists. While tools aid in QUBO formulations, support for selecting the best-solving approach remains absent. This becomes even more challenging because selecting the best solver for a problem heavily depends on the problem itself. In this work, we are accepting this challenge and propose a predictive selection approach, which aids end users in this task. To this end, the solver selection task is first formulated as a classification task that is suitable to be solved by supervised machine learning. Based on that, we then propose strategies for adjusting solver parameters based on problem size and characteristics. Experimental evaluations, considering more than 500 different QUBO problems, confirm the benefits of the proposed solution. In fact, we show that in more than 70% of the cases, the best solver is selected, and in about 90% of the problems, a solver in the top two, i.e., the best or its closest suboptimum, is selected. This exploration proves the potential of machine learning in quantum solver selection and lays the foundations for its automation, broadening access to quantum optimization for a wider range of users. The pre-trained classifier is integrated into the MQT Quantum Auto Optimizer (MQT QAO) framework, publicly available on GitHub (https://github.com/cda-tum/mqt-qao) as part of the Munich Quantum Toolkit (MQT).
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).
Quantum computing is an emerging technology that has the potential to revolutionize fields such as cryptography, machine learning, optimization, and quantum simulation. However, a major challenge in the realization of quantum algorithms on actual machines is ensuring that the gates in a quantum circuit (i.e., corresponding operations) match the topology of a targeted architecture so that the circuit can be executed while, at the same time, the resulting costs (e.g., in terms of the number of additionally introduced gates, fidelity, etc.) are kept low. This is known as the quantum circuit mapping problem. This summary paper provides an overview of QMAP-an open-source tool that is part of the Munich Quantum Toolkit (MQT) and offers efficient, automated, and accessible methods for tackling this problem. To this end, the paper first briefly reviews the problem. Afterwards, it shows how QMAP can be used to efficiently map quantum circuits to quantum computing architectures from both a user's and a developer's perspective. QMAP is publicly available as open-source at https://github.com/cda-tum/qmap.
In order to implement a quantum computing application, problem instances must be encoded into a quantum circuit and then compiled for a specific platform. The lengthy compilation process is a key bottleneck in this workflow, especially for problems that arise repeatedly with a similar yet distinct structure (each of which requires a new compilation run thus far). In this paper, we aim to overcome this bottleneck by proposing a comprehensive pre-compilation technique that tries to minimize the time spent for compiling recurring problems while preserving the solution quality as much as possible. The following concepts underpin the proposed approach: Beginning with a problem class and a corresponding quantum algorithm, a predictive encoding scheme is applied to encode a representative problem instance into a general-purpose quantum circuit for that problem class. Once the real problem instance is known, the previously constructed circuit only needs to be adjusted-with (nearly) no compilation necessary. Experimental evaluations on QAOA for the MaxCut problem as well as a case study involving a satellite mission planning problem show that the proposed approach significantly reduces the compilation time by several orders of magnitude compared to Qiskit's compilation schemes while maintaining comparable compiled circuit quality. All implementations are available on GitHub (https://github.com!cda-tum!mqt-problemsolver) as part of the Munich Quantum Toolkit (MQT).
Quantum software tools for a wide variety of design tasks on and across different levels of abstraction are crucial in order to eventually realize useful quantum applications. This requires practical and relevant benchmarks for new software tools to be empirically evaluated and compared to the current state of the art. Although benchmarks for specific design tasks are commonly available, the demand for an overarching cross-level benchmark suite has not yet been fully met and there is no mutual consolidation in how quantum software tools are evaluated thus far. In this work, we propose the MQT Bench benchmark suite (as part of the Munich Quantum Toolkit , MQT) based on four core traits: (1) cross-level support for different abstraction levels, (2) accessibility via an easy-to-use web interface (https://www.cda.cit.tum.de/mqtbench/) and a Python package, (3) provision of a broad selection of benchmarks to facilitate generalizability, as well as (4) extendability to future algorithms, gate-sets, and hardware architectures. By comprising more than 70,000 benchmark circuits ranging from 2 to 130 qubits on four abstraction levels, MQT Bench presents a first step towards benchmarking different abstraction levels with a single benchmark suite to increase comparability, reproducibility, and transparency.