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.
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.
Quantum computers are becoming a reality and numerous quantum computing applications with a near-term perspective (e.g., for finance, chemistry, machine learning, and optimization) and with a long-term perspective (e.g., for cryptography or unstructured search) are currently being investigated. However, designing and realizing potential applications for these devices in a scalable fashion requires automated, efficient, and user-friendly software tools that cater to the needs of end users, engineers, and physicists at every level of the entire quantum software stack. Many of the problems to be tackled in that regard are similar to design problems from the classical realm for which sophisticated design automation tools have been developed in the previous decades.The Munich Quantum Toolkit (MQT) is a collection of software tools for quantum computing developed by the Chair for Design Automation at the Technical University of Munich which explicitly utilizes this design automation expertise. Our overarching objective is to provide solutions for design tasks across the entire quantum software stack. This entails high-level support for end users in realizing their applications, efficient methods for the classical simulation, compilation, and verification of quantum circuits, tools for quantum error correction, support for physical design, and more. These methods are supported by corresponding data structures (such as decision diagrams or the ZX-calculus) and core methods (such as SAT encodings/solvers). All of the developed tools are available as open-source implementations and are hosted on github.com/cda-tum.Note: A live version of this document is available at mqt.readthedocs.io.
With quantum computers promising advantages even in the near-term NISQ era, there is a lively community that develops software and toolkits for the design of corresponding quantum circuits. Although the underlying problems are different, expertise from the design automation community, which developed sophisticated design solutions for the conventional realm in the past decades, can help here. In this respect, decision diagrams provide a promising foundation for tackling many design tasks such as simulation, synthesis, and verification of quantum circuits. However, users of the corresponding tools often do not have a proper background or an intuition about how these methods based on decision diagrams work and what their strengths and limits are. In this work, we first review the concepts of how decision diagrams can be employed, e.g., for the simulation and verification of quantum circuits. Afterwards, in an effort to make decision diagrams for quantum computing more accessible, we then present a visualization tool for quantum decision diagrams, which allows users to explore the behavior of decision diagrams in the design tasks mentioned above. Finally, we present decision diagram-based tools for simulation and verification of quantum circuits using the methods discussed above as part of the open-source Munich Quantum Toolkit (MQT)—a set of tools for quantum computing developed at the Technical University of Munich and the Johannes Kepler University Linz and released under the MIT license. More information about the corresponding tools is available at https://github.com/cda-tum/ddsim . By this, we provide an introduction of the concepts and tools for potential users who would like to work with them as well as potential developers aiming to extend them.
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 computing holds great promise for surpassing the limits of classical devices in many fields. Despite impressive developments, however, current research is primarily focused on qubits. At the same time, quantum hardware based on multi-level, qudit, systems offers a range of advantages, including expanded gate sets, higher information density, and improved computational efficiency, which might play a key role in overcoming not only the limitations of classical machines but also of current qubit-based quantum devices. However, working with qudits faces challenges not only in experimental control but particularly in algorithm development and quantum software. In this work, we introduce MQT Qudits, an open-source tool, which, as part of the Munich Quantum Toolkit (MQT), is built to assist in designing and implementing applications for mixed-dimensional qudit devices. We specify a standardized language for mixed-dimension systems and discuss circuit specification, compilation to hardware gate sets, efficient circuit simulation, and open challenges. MQT Qudits is available at github.com/cda-tum/mqt-qudits and on pypi at pypi.org/project/mqt.qudits.
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.
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).