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Quantum Cloud Computing: A Review, Open Problems, and Future Directions
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Quantum cloud computing is an emerging paradigm of computing that empowers quantum applications and their deployment on quantum computing resources without the need for a specialized environment to host and operate physical quantum computers. This paper reviews recent advances, identifies open problems, and proposes future directions in quantum cloud computing. It discusses the state-of-the-art quantum cloud advances, including the various cloud-based models, platforms, and recently developed technologies and software use cases. Furthermore, it discusses different aspects of the quantum cloud, including resource management, quantum serverless, security, and privacy problems. Finally, the paper examines open problems and proposes the future directions of quantum cloud computing, including potential opportunities and ongoing research in this emerging field.
Forward citations
Cited by 13 Pith papers
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Co-located simulations on a neutral atom machine interfere more when placed closer, and a moving target defense that relocates the victim restores fidelity to about 0.995 in simulator tests.
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Systematic Experiment Tracking in Quantum Software: A Case Study of Reservoir Computing with Error Mitigation
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Architectural Patterns for Designing Quantum Artificial Intelligence Systems
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QCOEM: Quantum Cloud Orchestration with Evolutionary Multi-Objective Optimization
QCOEM applies NSGA-II/III with AASF to quantum task scheduling and reports roughly 30% higher modeled fidelity and zero rescheduling versus noise-agnostic heuristics in emulation.
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Networked Quantum Services
A survey of networked quantum services, from distributed quantum computers and cloud platforms to programming languages and standardization efforts.
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Localized Kernel Methods for Signal Processing
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Security Vulnerabilities in Quantum Cloud Systems: A Survey on Emerging Threats
A review of security vulnerabilities, especially side-channel and crosstalk attacks, in multi-tenant quantum cloud platforms.
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Quantum Computing for Energy Management: A Semi Non-Technical Guide for Practitioners
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Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
A survey that categorizes known adversarial threats to quantum machine learning systems and reviews existing defenses, from logic locking to hardware-aware watermarking.
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