Presentation
Q-NFSO: Exploring Quantum Applications, Noise Management, Fault Injection, Resource Scheduling and Optimization in the NISQ Era
DescriptionQuantum computing has achieved significant milestones in recent years, underscoring its potential benefits for NP-hard applications both currently and in the future. Despite these advancements, contemporary quantum computers are hindered by noise and a limited number of qubits. These limitations pose significant challenges for quantum applications, noise management, resource scheduling, and optimization. This research addresses the gap between quantum algorithms and hardware characteristics by examining quantum applications from high-level circuit definitions to noise model generation, fault injection, resource management, and optimization.
This study highlights the quantum advantage over classical computing, explores early quantum neural networks, evaluates quantum metrics, and constructs a noise model based on quantum hardware using fault injection techniques to investigate the vulnerabilities of quantum algorithms, operations, and qubits. Our research considers the uncertainty factors of qubits, including random faults and single and double fault injections with circuit cutting and its limitations. The final stage evaluates the performance of quantum jobs submitted to backends under controlled noise, uncontrolled errors, and established metrics.
The proposed Quantum NFSO (Noise, Fault, and Scheduling Optimization) model presents a comprehensive approach to scheduling and optimization, accounting for noise, random errors, resource management, job scheduling, and circuit optimization. While quantum computers hold immense potential, it is essential to calibrate expectations appropriately. In the NISQ (Noisy Intermediate-Scale Quantum) era, scientists and researchers must align their perspectives with the technology's limitations. Understanding these constraints is crucial for advancing future computational technologies, including quantum computing.
This study highlights the quantum advantage over classical computing, explores early quantum neural networks, evaluates quantum metrics, and constructs a noise model based on quantum hardware using fault injection techniques to investigate the vulnerabilities of quantum algorithms, operations, and qubits. Our research considers the uncertainty factors of qubits, including random faults and single and double fault injections with circuit cutting and its limitations. The final stage evaluates the performance of quantum jobs submitted to backends under controlled noise, uncontrolled errors, and established metrics.
The proposed Quantum NFSO (Noise, Fault, and Scheduling Optimization) model presents a comprehensive approach to scheduling and optimization, accounting for noise, random errors, resource management, job scheduling, and circuit optimization. While quantum computers hold immense potential, it is essential to calibrate expectations appropriately. In the NISQ (Noisy Intermediate-Scale Quantum) era, scientists and researchers must align their perspectives with the technology's limitations. Understanding these constraints is crucial for advancing future computational technologies, including quantum computing.