Beyond the Buzz: Strategic Paths for Enabling Useful NISQ Applications

TL;DR

This paper discusses strategic approaches to advance NISQ quantum computing applications by focusing on identifying quantum-specific 'killer apps', integrating AI techniques, and promoting interdisciplinary co-design for practical utility.

Contribution

It proposes three key strategies—finding distinctive quantum applications, leveraging AI integration, and fostering co-design—to guide NISQ computing towards practical and impactful applications.

Findings

Identifying quantum-specific applications like quantum chemistry can catalyze development.

Integrating AI methods such as quantum neural networks enhances NISQ capabilities.

Interdisciplinary co-design is essential for effective synergy between classical and quantum computing.

Abstract

There is much debate on whether quantum computing on current NISQ devices, consisting of noisy hundred qubits and requiring a non-negligible usage of classical computing as part of the algorithms, has utility and will ever offer advantages for scientific and industrial applications with respect to traditional computing. In this position paper, we argue that while real-world NISQ quantum applications have yet to surpass their classical counterparts, strategic approaches can be used to facilitate advancements in both industrial and scientific applications. We have identified three key strategies to guide NISQ computing towards practical and useful implementations. Firstly, prioritizing the identification of a "killer app" is a key point. An application demonstrating the distinctive capabilities of NISQ devices can catalyze broader development. We suggest focusing on applications that are inherently quantum, e.g., pointing towards quantum chemistry and material science as promising domains. These fields hold the potential to exhibit benefits, setting benchmarks for other applications to follow. Secondly, integrating AI and deep-learning methods into NISQ computing is a promising approach. Examples such as quantum Physics-Informed Neural Networks and Differentiable Quantum Circuits (DQC) demonstrate the synergy between quantum computing and AI. Lastly, recognizing the interdisciplinary nature of NISQ computing, we advocate for a co-design approach. Achieving synergy between classical and quantum computing necessitates an effort in co-designing quantum applications, algorithms, and programming environments, and the integration of HPC with quantum hardware. The interoperability of these components is crucial for enabling the full potential of NISQ computing.