Abstract We describe staq , a full-stack quantum processing toolkit written in standard C++. staq is a quantum compiler toolkit, comprising of tools that range from quantum optimizers and translators to physical mappers for quantum devices with restricted connectives. The design of staq is inspired from the UNIX philosophy of ‘less is more’, i.e. staq achieves complex functionality via combining (piping) small tools, each of which performs a single task using the most advanced current state-of-the-art methods. We also provide a set of illustrative benchmarks.
Abstract We describe staq , a full-stack quantum processing toolkit written in standard C++. staq is a quantum compiler toolkit, comprising of tools that range from quantum optimizers and translators to physical mappers for quantum devices with restricted connectives. The design of staq is inspired from the UNIX philosophy of ‘less is more’, i.e. staq achieves complex functionality via combining (piping) small tools, each of which performs a single task using the most advanced current state-of-the-art methods. We also provide a set of illustrative benchmarks.
This paper introduces HamilToniQ, an open-source benchmarking toolkit for Quantum Processing Units (QPUs). It addresses the complexities of quantum computations by providing a methodological framework to assess QPU types, topologies, and systems. HamilToniQ facilitates performance evaluations through steps like circuit compilation and quantum error mitigation, with strategies tailored for each stage. The toolkit's H-Score measures QPU fidelity and reliability, offering a comprehensive view of performance. Focused on the Quantum Approximate Optimization Algorithm (QAOA), HamilToniQ enables consistent QPU comparisons, enhancing benchmarking transparency. Validated on various IBM QPUs, the toolkit proves effective and robust, advancing quantum computing with precise benchmarking metrics.
The advances in quantum hardware with functional quantum processors integrating tens of noisy qubits, together with the availability of near-term quantum algorithms have allowed the development of the so-called full-stacks that bridge quantum applications with quantum devices. In this talk, we will provide an overview of the different layers of the quantum computing full-stack, with emphasis on the software ones that include the compilation of quantum algorithms. We will then discuss the need for optimal physical-aware and algorithm-driven compilation techniques in the midst of the NISQ (Noisy Intermediate-scale quantum) era. We will also focus on key principles for architecting quantum computers such as codesign, optimization and benchmarking and illustrate how the use of structured design space exploration methodologies could help towards the development of a cross-layer codesign framework for full-stack quantum computing systems. Finally, we will talk about the scalability of quantum computing systems, which is one of the main challenges the quantum computing community is currently facing.
Quantum computing, once just a theoretical field, is quickly advancing as physical quantum technology increases in size, capability, and reliability. In order to fully harness the power of a general quantum computer or an application-specific device, compilers and tools must be developed that optimize specifications and map them to a realization on a specific architecture. In this work, a technique and prototype tool for synthesizing algorithms into a quantum computer is described and evaluated. Most recently reported methods produce technologically-independent reversible cascades comprised of a functionally complete set of operators with no regard to actual technologically-dependent cell libraries or constraints due to a device's pre-configured interconnectivity. In contrast, our prototype tool synthesizes algorithms into technologically-dependent specifications that consist of a set of primitives and connectivity constraints present in the computer architecture. The tool performs optimizations based on actual architectural constraints, and a high-quality technology-dependent synthesized result is achieved through the use of optimizing cost functions derived from real hardware and architecture parameters. Additionally, another important aspect of our tool is the incorporation of internal formal equivalence checking that ensures the initially specified algorithm is functionally equivalent to the optimized, technologically-mapped output. Experimental results are provided that target the IBM Q family of quantum computers.
Realizing large-scale quantum advantage is expected to require quantum error correction (QEC), making the compilation and optimization of logical operations a critical area of research. Logical computation imposes distinct constraints and operational paradigms that differ from those of the Noisy Intermediate-Scale Quantum (NISQ) regime, motivating the continued evolution of compilation tools. Given the complexity of this emerging stack, where factors such as gate decomposition precision and computational models must be co-designed, standardized benchmarks and toolkits are valuable for evaluating progress. To support this need, we introduce FTCircuitBench, which serves as: (1) a benchmark suite of impactful quantum algorithms, featuring pre-compiled instances in both Clifford+T and Pauli Based Computation models; (2) a modular end-to-end pipeline allowing users to compile and decompose algorithms for various fault-tolerant architectures, supporting both prebuilt and custom optimization passes; and (3) a toolkit for evaluating the impact of algorithms and optimization across the full compilation stack, providing detailed numerical analysis at each stage. FTCircuitBench is fully open-sourced and maintained on Github.
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.
Quantum computers promise to efficiently solve important problems classical computers never will. However, in order to capitalize on these prospects, a fully automated quantum software stack needs to be developed. This involves a multitude of complex tasks from the classical simulation of quantum circuits, over their compilation to specific devices, to the verification of the circuits to be executed as well as the obtained results. All of these tasks are highly non-trivial and necessitate efficient data structures to tackle the inherent complexity. Starting from rather straight-forward arrays over decision diagrams (inspired by the design automation community) to tensor networks and the ZX-calculus, various complementary approaches have been proposed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.