Improving Quantum Computing Task Scheduling

Stop waiting a full day for a 3-second quantum circuit to execute. Quantum computing is finally moving from isolated experiments into large-scale cloud workflows, but there is a massive bottleneck: the 60x overhead caused by provider queues. In today’s landscape, a job that takes seconds to run can sit in a first-come-first-serve line for hours, often on a device that doesn't even offer the best fidelity for your specific task. Enter Qurator, a new architecture-agnostic scheduler designed to bridge the gap between classical HPC and heterogeneous quantum providers. Unlike traditional schedulers that treat queue time and circuit fidelity as separate issues, Qurator optimizes for both simultaneously. By reconciling incompatible calibration data from IBM (IBM Quantum), IonQ , Rigetti Computing , and others into a unified success score, Qurator makes intelligent mapping decisions. QuEra Computing Inc. It uses advanced techniques like circuit cutting and merging to fit tasks onto the best available hardware while respecting quantum-specific constraints like the No-Cloning Theorem and entanglement synchronization. The results speak for themselves. Under high-load conditions, Qurator reduces queue wait times by 30% to 75% while keeping execution fidelity within a user-defined target. It proves that we don't have to sacrifice accuracy for speed if we treat quantum constraints as first-class scheduling concerns. If you are building the future of hybrid quantum-classical systems, this is how we scale. Read the full paper: Qurator: Scheduling Hybrid Quantum-Classical Workflows Across Heterogeneous Cloud Providers. #QuantumComputing #CloudComputing #HPC #QuantumSoftware #TechInnovation

I am happy to share our new results on "Distributed scheduling of quantum circuits with noise and time optimization". In this study, we first use circuit knitting as a method for reducing noise. Next we propose a scheduler that finds the optimum schedule for the subcircuits obtained via circuit knitting on the available set of hardware to (i) maximize the overall fidelity, and (ii) ensure that the predefined maximum execution time for each hardware is not exceeded. The average increase in the fidelity obtained by our method are respectively ~12.3% and ~21% for 10-qubit benchmark circuits without and with measurement error mitigation, even when each hardware was allowed the minimum possible execution time. Kudos to Debasmita Bhoumik, who is the lead author of this paper, and Dr. Amit Saha, and my PhD supervisor Prof. Susmita Sur Kolay for her guidance in this. https://lnkd.in/gEiTsnwM #quantum #quantumcomputing #ibmquantum

In our new paper, "Efficient Compilation for Shuttling Trapped-Ion Machines via the Position Graph Architectural Abstraction," we introduce the position graph, a novel architectural abstraction that unifies the representation of superconducting, trapped-ion, and other quantum architectures. This enables the adaptation of powerful compilation techniques from superconducting systems to different types of platforms such as trapped-ion. Built on this foundation, we developed shuttling-aware scheduling algorithms that reduce quantum circuit execution time and successfully compiles circuits where existing methods fail. This work is the result of a wonderful collaboration with Ed Younis from Berkeley Lab , and big congratulations to PhD student Bao Bach for leading this exciting contribution to scalable quantum compilation. Check our paper at https://lnkd.in/ecYuj2jj #QuantumComputing #QuantumCompilation #IonTrap #QAOA #QuantumOptimization #MachineLearning #VariationalAlgorithms #QuantumAlgorithms #Preprint Computer & Info. Sciences at the Univ. of Delaware University of Delaware UD College of Engineering