Quantum Resource Management in the NISQ Era: Challenges, Vision, and a Runtime Framework

TL;DR

This paper discusses the challenges of managing quantum resources in NISQ devices, proposes a runtime-aware software framework, and introduces Qonscious for dynamic resource evaluation to improve quantum software scalability.

Contribution

It presents a vision for resource-aware quantum software development and introduces Qonscious, a prototype framework for conditional quantum program execution based on resource assessment.

Findings

Qonscious enables dynamic resource-based decision making in quantum programs.

Identifies key challenges like limited introspection and temporal constraints in current platforms.

Strengthens Quantum Resource Estimation (QRE) for scalable quantum software.

Abstract

Quantum computers represent a radical technological advancement in the way information is processed by using the principles of quantum mechanics to solve very complex problems that exceed the capabilities of classical systems. However, in the current NISQ era (Noisy Intermediate-Scale Quantum devices), the available hardware presents several limitations, such as a limited number of qubits, high error rates, and reduced coherence times. Efficient management of quantum resources, both physical (qubits, error rates, connectivity) and logical (quantum gates, algorithms, error correction), becomes particularly relevant in the design and deployment of quantum algorithms. In this work, we analyze the role of resources in the various uses of NISQ devices today, identifying their relevance and implications for software engineering focused on the use of quantum computers. We propose a vision for runtime-aware quantum software development, identifying key challenges to its realization, such as limited introspection capabilities and temporal constraints in current platforms. As a proof of concept, we introduce Qonscious, a prototype framework that enables conditional execution of quantum programs based on dynamic resource evaluation. With this contribution, we aim to strengthen the field of Quantum Resource Estimation (QRE) and move towards the development of scalable, reliable, and resource-aware quantum software.