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.

Quantum algorithms need to be compiled to respect the constraints imposed by quantum processors, which is known as the mapping problem. The mapping procedure will result in an increase of the number of gates and of the circuit latency, decreasing the algorithm's success rate. It is crucial to minimize mapping overhead, especially for noisy intermediate-scale quantum (NISQ) processors that have relatively short qubit coherence times and high gate error rates. Most of prior mapping algorithms have only considered constraints, such as the primitive gate set and qubit connectivity, but the actual gate duration and the restrictions imposed by the use of shared classical control electronics have not been taken into account. In this article, we present a mapper called Qmap to make quantum circuits executable on scalable processors with the objective of achieving the shortest circuit latency. In particular, we propose an approach to formulate the classical control restrictions as resource constraints in a conventional list scheduler with polynomial complexity. Furthermore, we implement a routing heuristic to cope with the connectivity limitation. This router finds a set of movement operations that minimally extends circuit latency. To analyze the mapping overhead and evaluate the performance of different mappers, we map 56 quantum benchmarks onto a superconducting processor named Surface-17. Compared to a prior mapping strategy that minimizes the number of operations, Qmap can reduce the latency overhead (LtyOH) up to 47.3% and operation overhead up to 28.6%, respectively.

Quantum computing is currently moving from an academic idea to a practical reality. Quantum computing in the cloud is already available and allows users from all over the world to develop and execute real quantum algorithms. However, companies which are heavily investing in this new technology such as Google, IBM, Rigetti, Intel, IonQ, and Xanadu follow diverse technological approaches. This led to a situation where we have substantially different quantum computing devices available thus far. They mostly differ in the number and kind of qubits and the connectivity between them. Because of that, various methods for realizing the intended quantum functionality on a given quantum computing device are available. This paper provides an introduction and overview into this domain and describes corresponding methods, also referred to as compilers, mappers, synthesizers, transpilers, or routers.

Fault-tolerant (FT) computation by using quantum error correction (QEC) is essential for realizing large-scale quantum algorithms. Devices are expected to have enough qubits to demonstrate aspects of fault tolerance in the near future. However, these near-term quantum processors will only contain a small amount of noisy qubits and allow limited qubit connectivity. Fault-tolerant schemes that not only have low qubit overhead but also comply with geometrical interaction constraints are therefore necessary. In this work, we combine flag fault tolerance with quantum circuit mapping, to enable an efficient flag-bridge approach to implement FT QEC on near-term devices. We further show an example of performing the Steane code error correction on two current superconducting processors and numerically analyze their performance with circuit level noise. The simulation results show that the QEC circuits that measure more stabilizers in parallel have lower logical error rates. We also observe that the Steane code can outperform the distance-3 surface code using flag-bridge error correction. In addition, we foresee potential applications of the flag-bridge approach such as FT computation using lattice surgery and code deformation techniques.

Quantum computers can solve problems that are inefficiently solved by classical computers, such as integer factorization. A fully programmable quantum computer requires a quantum control microarchitecture that connects the quantum software and hardware. Previous research has proposed a Quantum Instruction Set Architecture (QISA) and a quantum control microarchitecture, which targets Noisy Intermediate-Scale Quantum (NISQ) devices without fault-tolerance. However, fault-tolerant (FT) quantum computing requires FT implementation of logical operations, and repeated quantum error correction, possibly at runtime. Though highly patterned, the amount of required (physical) operations to perform logical operations is ample, which cannot be well executed by existing quantum control microarchitectures. In this paper, we propose a control microarchitecture that can efficiently support fault-tolerant quantum computing based on the rotated planar surface code with logical operations implemented by lattice surgery. It highlights a two-level address mechanism which enables a clean compilation model for a large number of qubits, and microarchitectural support for quantum error correction at runtime, which can significantly reduce the quantum program codesize and present better scalability.

Modern computer applications usually consist of a variety of components that often require quite different computational co-processors. Some examples of such co-processors are TPUs, GPUs or FPGAs. A more recent and promising technology that is being investigated is quantum co-processors. In this paper, we present a modern computer architecture where a quantum co-processor is included as an additional accelerator. In such an environment, the idea is to execute the application on a heterogeneous architecture where the classic processor will execute the host part, but certain components will be mapped, in our case, on the quantum accelerator. To this purpose, we define the distinct layers for the quantum computer architecture where there is a clear boundary between the host program and quantum kernel(s). We also discuss the opportunities and challenges of mapping hybrid algorithms to such a heterogeneous quantum computer architecture.