We introduce AriaQuanta, a powerful and flexible tool for designing, simulating, and implementing quantum circuits. This open-source software is designed to make it easy for users of all experience levels to learn and use quantum computing. The first version includes a compiler for implementing various quantum circuits and algorithms. Additionally, parametric circuits allow for the implementation of variational quantum algorithms, and various noise models are available for simulating noisy circuits. We performed numerous numerical simulations on AriaQuanta in various applications, including quantum algorithms and noisy circuits. The results, compared with popular counterparts, demonstrate the high performance of AriaQuanta.
We introduce AriaQuanta, a powerful and flexible tool for designing, simulating, and implementing quantum circuits. This open-source software is designed to make it easy for users of all experience levels to learn and use quantum computing. The first version includes a compiler for implementing various quantum circuits and algorithms. Additionally, parametric circuits allow for the implementation of variational quantum algorithms, and various noise models are available for simulating noisy circuits. We performed numerous numerical simulations on AriaQuanta in various applications, including quantum algorithms and noisy circuits. The results, compared with popular counterparts, demonstrate the high performance of AriaQuanta.
Developing state-of-the-art classical simulators of quantum circuits is of utmost importance to test and evaluate early quantum technology and understand the true potential of full-blown error-corrected quantum computers. In the past few years, multiple theoretical and numerical advances have continuously pushed the boundary of what is classically simulable, hence the development of a plethora of tools which are often limited to a specific purpose or designed for a particular hardware (e.g. CPUs vs. GPUs). Moreover, such tools are typically developed using tailored languages and syntax, which makes it hard to compare results from, and create hybrid approaches using, different simulation techniques. To support unified and optimized use of these techniques across platforms, we developed HybridQ , a highly extensible platform designed to provide a common framework to integrate multiple state-of-the-art techniques to run on a variety of hardware. The philosophy behind its development has been driven by three main pillars: Easy to Use, Easy to Extend, and Use the Best Available Technology. The powerful tools of HybridQ allow users to manipulate, develop, and extend noiseless and noisy circuits for different hardware architectures. HybridQ supports large-scale high-performance computing (HPC) simulations, automatically balancing workload among different processor nodes and enabling the use of multiple backends to maximize parallel efficiency. Everything is then glued together by a simple and expressive language that allows seamless switching from one technique to another as well as from one hardware to the next, without the need to write lengthy translations, thus greatly simplifying the development of new hybrid algorithms and techniques.
We introduce ProjectQ, an open source software effort for quantum computing. The first release features a compiler framework capable of targeting various types of hardware, a high-performance simulator with emulation capabilities, and compiler plug-ins for circuit drawing and resource estimation. We introduce our Python-embedded domain-specific language, present the features, and provide example implementations for quantum algorithms. The framework allows testing of quantum algorithms through simulation and enables running them on actual quantum hardware using a back-end connecting to the IBM Quantum Experience cloud service. Through extension mechanisms, users can provide back-ends to further quantum hardware, and scientists working on quantum compilation can provide plug-ins for additional compilation, optimization, gate synthesis, and layout strategies.
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.
An electronic design and evaluation tool for quantum circuit design is presented. It allows easy implementation of quantum algorithms based on the circuit model of quantum computation. The layout of an ideal circuit network can be designed in the logical layer and then automatically get converted into an encoded form closer to the physical layer. The possibility to select and apply the desired quantum error-correcting code enables to study a particular circuit from the aspect of error correction. Various constructions of the same logical network can be designed in order to compare the gate loads of different implementations and encoding or decoding schemas. It is a well structured software tool enabling expansion of other useful functions for realistic studies on quantum computation.
A circuit design interface for simulation of quantum circuits is presented. This simple but powerful tool aims to enable and improve experimentation in the field of quantum computing, either for professional or amateur usage. It provides an intuitive, automated, easy-to-use environment for building algorithms using the circuit model of quantum computation, while freeing the experimenter from technical procedures, such as coding or program installations. It provides a wide range of tools to the user, from a universal gate-set, to custom gate definitions, post-selected measurements, complex compositions of sub-circuits in a recursive way, good modularity and nice output results. Moreover, it is combined with popular backend simulators due to its circuit-parsing capabilities.
As quantum computers of non-trivial size become available in the near future, it is imperative to develop tools to emulate small quantum computers. This allows for validation and debugging of algorithms as well as exploring hardware-software co-design to guide the development of quantum hardware and architectures. The simulation of quantum computers entails multiplications of sparse matrices with very large dense vectors of dimension 2n, where n denotes the number of qubits, making this a memory-bound and network bandwidth-limited application. We introduce the concept of a quantum computer emulator as a component of a software framework for quantum computing, enabling a significant performance advantage over simulators by emulating quantum algorithms at a high level rather than simulating individual gate operations. We describe various optimization approaches and present benchmarking results, establishing the superiority of quantum computer emulators in terms of performance.