QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

arXiv:2508.04974 · quant-ph, cs.ET · Submitted 2025-08-07 · Read on arXiv

quant-ph, cs.ET

Submitted: 2025-08-07

Updated: 2025-08-07

Terminology

• Fidelity — 101× in this paper · explained in 2 episodes
• Reinforcement Learning — 41× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 33× in this paper · explained in 2 episodes
• Calibration — 15× in this paper · explained in 7 episodes
• Quantum Cloud Computing — 14× in this paper
• Quantum Circuits — 7× in this paper · explained in 2 episodes
• Reward Function — 5× in this paper · explained in 2 episodes
• Benchmarking — 4× in this paper · explained in 3 episodes
• Markov Decision Process — 4× in this paper
• Action Space — 3× in this paper · explained in 1 episode
• Robustness — 3× in this paper · explained in 5 episodes
• Abstraction — 2× in this paper · explained in 1 episode
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• First Available Node — 2× in this paper
• International Conference on Quantum Computing and Engineering — 2× in this paper
• Objective Function — 2× in this paper · explained in 2 episodes
• Round Robin — 2× in this paper
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Combinatorial Optimization — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Directed Acyclic Graph — 1× in this paper
• Fastest Duration First — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generalized Advantage Estimation — 1× in this paper
• Hybrid System — 1× in this paper · explained in 2 episodes
• Markov Decision Process (MDP) — 1× in this paper · explained in 11 episodes
• Modular Architecture — 1× in this paper · explained in 4 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Smallest Error First — 1× in this paper
• Stochastic Dynamics — 1× in this paper · explained in 2 episodes

Sources

• Benchmarking a trapped-ion quantum computer with 30 qubits
• Qonductor: A Cloud Orchestrator for Quantum Computing
• Rainbow: Combining Improvements in Deep Reinforcement Learning
• QuSplit: Achieving Both High Fidelity and Throughput via Job Splitting on Noisy Quantum Computers
• Tune: A Research Platform for Distributed Model Selection and Training
• Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
• Quantum Cloud Computing: A Review, Open Problems, and Future Directions
• MQT Bench: Benchmarking Software and Design Automation Tools for Quantum Computing
• Benchmarking Quantum Computers and the Impact of Quantum Noise
• High-Dimensional Continuous Control Using Generalized Advantage Estimation
• Proximal Policy Optimization Algorithms
• Quality, Speed, and Scale: three key attributes to measure the performance of near-term quantum computers

Related papers

• Reconquering Bell sampling on qudits: stabilizer learning and testing, quantum pseudorandomness bounds, and more
• Encrypted clones can leak: Classification of informative subsets in Quantum Encrypted Cloning
• Polynomial-time classical and quantum simulation of quantum impurity models
• Theory of quantum-enhanced interferometry with general Markovian light sources
• A convergent hierarchy of spectral gap certificates for qubit Hamiltonians
• Universal Bound and Phase Transition in Many-Body Fermionic Non-Gaussianity