The rapid progress quantum devices have made in recent years has led to the need for systems that bridge the gap between quantum algorithms and quantum hardware. To this purpose different full-stack quantum programming platforms have been developed, providing high level languages for expressing quantum algorithms and providing compilers for making those quantum algorithms executable on a given quantum device. OpenQL is one such a platform, with support for compiling a variety of quantum program inputs into a quantum circuit, ready to be executed on the targeted quantum hardware. OpenQL is an evolving tool in which new features are constantly added to improve its performance and extend its functionality. In order to enhance OpenQL and provide some extra support to the researchers using OpenQL for their experiments, a visualization tool with three main functionalities has been developed in this thesis project. First, a circuit visualizer, with support for both visualizing the circuit output of the OpenQL compiler as an abstract gate representation and a pulse representation, which has been made specifically for quantum hardware running on superconducting qubits. Secondly, a mapping graph visualizer, which allows displaying the logical to physical qubit mapping per program cycle. And lastly a qubit interaction graph generator, which shows the required interactions between qubits of a given quantum algorithm.
The rapid progress quantum devices have made in recent years has led to the need for systems that bridge the gap between quantum algorithms and quantum hardware. To this purpose different full-stack quantum programming platforms have been developed, providing high level languages for expressing quantum algorithms and providing compilers for making those quantum algorithms executable on a given quantum device. OpenQL is one such a platform, with support for compiling a variety of quantum program inputs into a quantum circuit, ready to be executed on the targeted quantum hardware. OpenQL is an evolving tool in which new features are constantly added to improve its performance and extend its functionality. In order to enhance OpenQL and provide some extra support to the researchers using OpenQL for their experiments, a visualization tool with three main functionalities has been developed in this thesis project. First, a circuit visualizer, with support for both visualizing the circuit output of the OpenQL compiler as an abstract gate representation and a pulse representation, which has been made specifically for quantum hardware running on superconducting qubits. Secondly, a mapping graph visualizer, which allows displaying the logical to physical qubit mapping per program cycle. And lastly a qubit interaction graph generator, which shows the required interactions between qubits of a given quantum algorithm.
With the potential of quantum algorithms to solve intractable classical problems, quantum computing is rapidly evolving, and more algorithms are being developed and optimized. Expressing these quantum algorithms using a high-level language and making them executable on a quantum processor while abstracting away hardware details is a challenging task. First, a quantum programming language should provide an intuitive programming interface to describe those algorithms. Then a compiler has to transform the program into a quantum circuit, optimize it, and map it to the target quantum processor respecting the hardware constraints such as the supported quantum operations, the qubit connectivity, and the control electronics limitations. In this article, we propose a quantum programming framework named OpenQL, which includes a high-level quantum programming language and its associated quantum compiler. We present the programming interface of OpenQL, we describe the different layers of the compiler and how we can provide portability over different qubit technologies. Our experiments show that OpenQL allows the execution of the same high-level algorithm on two different qubit technologies, namely superconducting qubits and Si-Spin qubits. Besides the executable code, OpenQL also produces an intermediate quantum assembly code, which is technology independent and can be simulated using the QX simulator.
In recent years, Quantum Computing has gone from theory to a promising reality, leading to quantum chips that in a near future might be able to exceed the computational power of any current supercomputer. For this to happen, there are some problems that must be overcome. For example, in a quantum processor qubits are usually arranged in a 2D architecture with limited connectivity between them and in which only nearest-neighbour interactions are allowed. This restricts the execution of two-qubit gates and requires qubit to be moved to adjacent positions. Quantum algorithms, which are described as quantum circuits, neglect the quantum chip constraints and therefore cannot be directly executed. This is known as the mapping problem. This thesis focuses on the problem of mapping quantum algorithms into a quantum chip based on spin qubits, called the crossbar architecture. In this project we have developed the required compiler support (mapping) for making quantum circuits executable on the crossbar architecture based on the tools provided by OpenQL. Using this compiler, we have analyzed the mapping overhead of the crossbar architecture and studied how it relates to the characteristics of quantum algorithms. In addition, we have developed a verification program that checks the output of the compiler and provides a visualisation tool for debugging.
Practical realizations of quantum computers are poised to deliver outstanding computational capabilities far beyond the reach of any classical supercomputer. While classical systems are reliably implemented using CMOS technology, the fabrication of quantum hardware has not yet reached a commercially viable level of maturity. Nowadays, many different technologies are being developed as competing candidates to build the first error-corrected quantum computer. Among the software resources required to operate such a system, quantum compilers translate a high-level description of a quantum algorithm into low-level, technology-dependent quantum instructions. Many quantum algorithms, including Shor's and Grover's algorithms, require to compute some classical logic functions on the quantum computer. The first part of this thesis deals with the problem of compiling quantum circuits that perform such classical Boolean functions, called oracles, into a fault-tolerant set of instructions. Oracle circuits often demand many resources in terms of the number of qubits and gates. The focus is on compiling quantum circuits starting from multi-level logic networks representing large Boolean functions while minimizing the resource footprint of the obtained quantum circuits. At the same time, the trade-off between memory resources and the number of operations typical of such a compilation process is explored. As part of the effort in reducing the resources required to perform a given quantum program, I also present some quantum circuit optimization techniques. The researched algorithms leverage data structures and techniques borrowed and adapted from classical logic synthesis, e.g., SAT solvers, LUT mapping, and multi-level logic networks. I implemented all the compilation algorithms presented in this thesis as part of open-source projects. In particular, I develop and maintain caterpillar---a C++ header-only library dedicated to the quantum compilation of oracles and quantum memory management. The second part of this thesis describes how to equip quantum programming language compilers with automatic accuracy management. Despite the availability of many of such languages, resource estimation of quantum algorithms does not yet support taking approximation errors into account. This general methodology is applicable to any programming language and I demonstrate its integration into the Q# compiler. The technique consists of providing language support to ease the accuracy-aware programming task. The user can define accuracy parameters that will be automatically optimized according to a constraint and a cost function directly generated from the source code. In the presented practical evaluations, the constraint function is the overall allowed accuracy, while the cost function is application-dependent and related to the number of gates. During the optimization process, such functions are evaluated hundreds of times. For this reason, we extract them as (near-)symbolic expressions, whose evaluation time does not depend on the quantum algorithm size. The algorithms and the methodologies presented in this thesis are part of a widespread effort of the research community to build a complete and efficient software stack to program and control the first practical universal quantum computer.
Quantum computing has the potential to tackle complex problems that are intractable for classical computers, thanks to its unique properties, such as superposition and entanglement. Nevertheless, developing efficient quantum programs remains challenging because of the diversity and rapid evolution of hardware, and limited access to quantum devices. To tackle these issues, we propose an open-source, profile-guided development flow that allows programmers to refine and optimize quantum code using quantum circuit simulators to evaluate program performance without the need for physical quantum hardware. In the proposed flow, the performance data collected in previous simulation runs helps guide compiler optimizations to generate efficient quantum circuits. An integrated visualization tool further aids programmers by associating performance data with the quantum program code, making it easier to pinpoint code regions for potential improvement. This development flow offers a practical path toward the development of novel and efficient quantum algorithms for various quantum computing platforms.
[EN] The rapid scaling of quantum processors in the NISQ era presents a significant challenge for software development: the increasing opacity of the compilation process. As algorithms are mapped onto complex hardware with hundreds or thousands of qubits, the transformation from a high-level logical description to a sequence of physical operations becomes a black box, hindering intuition, debugging, and the development of optimized compilation strategies. This thesis introduces Quvis, a novel visualization tool designed to address this challenge. Quvis provides an interactive environment to dissect the compilation pipeline by mapping logical circuit interactions onto a dynamic representation of the target processor’s physical qubit connectivity. Developed using a hybrid architecture combining React and Three.js, the tool effectively synchronizes user-driven controls with a real-time visualisation of the quantum hardware. The hardware topology is rendered using a force-directed layout algorithm to produce clear and intuitive spatial representations. The utility of Quvis is demonstrated through the analysis of state-of-the-art quantum circuits, such as the Quantum Fourier Transform (QFT). Furthermore, the performance of the visualization tool itself is benchmarked, evaluating the scalability of its layout algorithms and rendering frame rates on modern hardware. By offering a transparent and intuitive view into the compilation process, Quvis serves as a valuable didactic and investigative tool. It bridges a critical gap in the quantum software stack, empowering researchers and developers to better understand and optimize the interaction between quantum algorithms and noisy, intermediate-scale hardware.
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.