Access to cloud quantum hardware is expensive, queue-constrained, and subject to calibration variability -- making efficient utilisation of every quantum shot and circuit execution critical for research groups with finite quantum access budgets. This paper proposes the Quantum Hardware Resource Optimisation (QHRO) framework, a systematic methodology for maximising the scientific output per quantum hardware credit across five resource dimensions: shot budget allocation, circuit batching and job scheduling, hardware platform selection, calibration-aware execution timing, and quantum- classical co-processing balance. QHRO is evaluated on a six-month IBM Quantum Network access log covering 2,840 quantum job submissions from this journal's algorithm ecosystem. QHRO's shot allocation strategy (adaptive Bayesian shot budgeting) reduces total shots by 38.4% at equal result quality. Circuit batching reduces queue wait time by 48.4% through intelligent job packing. Calibration-aware scheduling reduces effective error rate by 18.4% by submitting jobs during low-noise calibration windows. QHRO provides a complete resource management system reducing total quantum hardware cost by 42.4% for the evaluated workload, with a practical implementation guide for research groups.
Access to cloud quantum hardware is expensive, queue-constrained, and subject to calibration variability -- making efficient utilisation of every quantum shot and circuit execution critical for research groups with finite quantum access budgets. This paper proposes the Quantum Hardware Resource Optimisation (QHRO) framework, a systematic methodology for maximising the scientific output per quantum hardware credit across five resource dimensions: shot budget allocation, circuit batching and job scheduling, hardware platform selection, calibration-aware execution timing, and quantum- classical co-processing balance. QHRO is evaluated on a six-month IBM Quantum Network access log covering 2,840 quantum job submissions from this journal's algorithm ecosystem. QHRO's shot allocation strategy (adaptive Bayesian shot budgeting) reduces total shots by 38.4% at equal result quality. Circuit batching reduces queue wait time by 48.4% through intelligent job packing. Calibration-aware scheduling reduces effective error rate by 18.4% by submitting jobs during low-noise calibration windows. QHRO provides a complete resource management system reducing total quantum hardware cost by 42.4% for the evaluated workload, with a practical implementation guide for research groups.
As the popularity of quantum computing continues to grow, efficient quantum machine access over the cloud is critical to both academic and industry researchers across the globe. And as cloud quantum computing demands increase exponentially, the analysis of resource consumption and execution characteristics are key to efficient management of jobs and resources at both the vendor-end as well as the client-end. While the analysis and optimization of job / resource consumption and management are popular in the classical HPC domain, it is severely lacking for more nascent technology like quantum computing.This paper proposes optimized adaptive job scheduling to the quantum cloud taking note of primary characteristics such as queuing times and fidelity trends across machines, as well as other characteristics such as quality of service guarantees and machine calibration constraints. Key components of the proposal include a) a prediction model which predicts fidelity trends across machine based on compiled circuit features such as circuit depth and different forms of errors, as well as b) queuing time prediction for each machine based on execution time estimations.Overall, this proposal is evaluated on simulated IBM machines across a diverse set of quantum applications and system loading scenarios, and is able to reduce wait times by over 3x and improve fidelity by over 40% on specific usecases, when compared to traditional job schedulers.
Quantum-classical computing pipelines -- systems integrating quantum hardware for specific computational subroutines within larger classical workflows -- require system-level optimisation across the full stack: quantum circuit compilation (QCDE), error mitigation (QEMD), resource scheduling (QHRO), classical co-processing (RSHPC), and workflow orchestration (CRBWA). Optimising each layer independently ignores inter- layer dependencies that, when exploited jointly, yield substantially better end-to-end performance than the sum of individual optimisations. This paper proposes the Quantum- Classical Pipeline Optimisation (QCPO) framework, integrating QCDE, QEMD, QHRO, RSHPC, and CRBWA into a unified co- optimisation system with three system-level strategies: cross-layer parameter sharing (CLPS) propagating noise model information across compilation, mitigation, and scheduling layers; pipeline-aware shot allocation (PASA) allocating shots based on downstream classical processing requirements; and adaptive layer switching (ALS) dynamically selecting the quantum-classical boundary based on current hardware state. QCPO is evaluated on end-to-end pipelines for RPSO protocol optimisation, PRDT digital twin personalisation, and TRMDSS translational decision support. QCPO reduces total pipeline cost by 58.4% and end-to-end latency by 48.4% vs. independently-optimised layers on identical hardware.
We experimentally study error mitigation and finite-shot sampling on superconducting quantum hardware under a constrained execution budget. The study combines calibration-aware qubit selection, circuit-depth scaling, zero-noise extrapolation, dynamical decoupling, readout-error mitigation, and repeated-shot estimation on an IBM Quantum processor. Experiments are organized across ideal simulation, noise-model simulation, and physical-device execution to separate sampling uncertainty from device-induced error. We investigate how mitigation performance changes with circuit depth, effective noise scale, qubit connectivity, and measurement budget, and quantify accuracy using expectation-value error, mean-squared error, statistical uncertainty, and mitigation gain. A fixed hardware-execution budget is used to evaluate shot allocation strategies and repeated measurements without relying on unlimited sampling. The resulting analysis provides a hardware-aware characterization of when mitigation improves expectation-value estimation and when finite-shot fluctuations offset the benefit of additional mitigation overhead. The implementation uses contemporary Qiskit and IBM Quantum Runtime workflows and is designed to provide reproducible experimental evidence for error-mitigation studies on current quantum processors.
This paper introduces a lightweight predictive resource allocation framework for Quantum Operating Systems in cloud quantum computing. Current platforms often run user-submitted circuits serially on Quantum Processing Units (QPUs), leaving many qubits idle even when smaller circuits could execute in parallel on disjoint partitions. The proposed approach performs static analysis on the transpiled circuit at submission time, extracting three key metrics: qubit count (n), circuit depth (d), and total gate count (g). These metrics enable accurate estimation of each job’s resource needs and predicted execution time without runtime profiling. The framework combines two scheduling strategies: Resource-Aware FCFS (RA-FCFS) with crosstalk-aware bin-packing, which packs compatible circuits into idle qubit fragments while enforcing a minimum separation constraint (δ_sep = 2 edges on heavy-hex topologies) to control ZZ crosstalk and preserve fidelity. Quantum Highest Response Ratio Next (Q-HRRN), a priority policy that prevents starvation of long/deep circuits while maintaining fast response for short jobs. Discrete-event simulations on 27-qubit to 127-qubit heavy-hex devices using VQE, QAOA, and QFT workloads show significant gains: qubit utilization rises from 31–38% (serial baseline) to 79–87%, and average wait times decrease by 1.8× to 3.4×. The crosstalk guard limits additional error-budget to only 3.1%. This low-overhead solution requires no hardware changes and offers a practical path to improve efficiency in NISQ-era quantum cloud services.
Quantum computers face challenges due to hardware constraints, noise errors, and heterogeneity, and face fundamental design tradeoffs between key performance metrics such as \textit{quantum fidelity} and system utilization. This substantially complicates managing quantum resources to scale the size and number of quantum algorithms that can be executed reliably in a given time. We introduce QOS, a cloud operating system for managing quantum resources while mitigating their inherent limitations and balancing the design tradeoffs of quantum computing. QOS exposes a hardware-agnostic API for transparent quantum job execution, mitigates hardware errors, and systematically multi-programs and schedules the jobs across space and time to achieve high quantum fidelity in a resource-efficient manner. To achieve this, it leverages two key insights: First, to maximize utilization and minimize fidelity loss, some jobs are more compatible than others for multi-programming on the same quantum computer. Second, sacrificing minimal fidelity can significantly reduce job waiting times. We evaluate QOS on real quantum devices hosted by IBM, using 7000 real quantum runs of more than 70.000 benchmark instances. We show that the QOS achieves 2.6--456.5× higher fidelity, increases resource utilization by up to 9.6×, and reduces waiting times by up to 5× while sacrificing only 1--3\% fidelity, on average, compared to the baselines.
Any algorithm execution on quantum computers requires several repeated and costly executions (known as shots) to obtain reliable results. In this work, we propose a closed-form accurate analytical expression to determine optimal number of shots required for reliable execution of any algorithm on a quantum computer. We also present a theoretically grounded technique to distribute fixed shot budget across different partitions in a quantum circuit minimizing the total error. Our proposed analytical model helps to reduce the shots associated with reliable execution of quantum algorithms by about 58\% compared to current practice, in turn reducing the energy consumption by upto 62\%. Furthermore, our proposed optimal shot allocation technique across different partitions reduces total error by up to 73\% compared to conventional approaches.