Even as quantum computers continue to increase in number of qubits, high error rates limit the size of quantum circuits that can be executed. This results in low throughput and poor resource utilization on quantum computers. To address this, recent research has focused on extending circuit optimization and compilation techniques to support simultaneous execution of quantum circuits (i.e. quantum parallelism). While much work has been spent developing parallel mapping and gate scheduling techniques, there are few approaches to the problem of parallel job scheduling on quantum computers. There are several constraints that make quantum job scheduling different from classical scheduling. We propose a scheduling algorithm, QGroup, for parallelizing circuits of different lengths and shot counts while mitigating parallel slowdowns due to measurement synchronization. QGroup achieves a better parallelism-runtime tradeoff, with significant speedup and fidelity increase over existing methods. QGroup achieves improvement in both fidelity and runtime for noise-sensitive, diverse workloads.
Even as quantum computers continue to increase in number of qubits, high error rates limit the size of quantum circuits that can be executed. This results in low throughput and poor resource utilization on quantum computers. To address this, recent research has focused on extending circuit optimization and compilation techniques to support simultaneous execution of quantum circuits (i.e. quantum parallelism). While much work has been spent developing parallel mapping and gate scheduling techniques, there are few approaches to the problem of parallel job scheduling on quantum computers. There are several constraints that make quantum job scheduling different from classical scheduling. We propose a scheduling algorithm, QGroup, for parallelizing circuits of different lengths and shot counts while mitigating parallel slowdowns due to measurement synchronization. QGroup achieves a better parallelism-runtime tradeoff, with significant speedup and fidelity increase over existing methods. QGroup achieves improvement in both fidelity and runtime for noise-sensitive, diverse workloads.
Today's quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware. The NISQ era allows for a limited number of qubits with several physical limitations. As NISQ devices have unavoidable noisy quantum operations, we can execute only small circuits on a quantum machine to get reliable results. But, this leads to the quantum hardware under-utilization issue. With the increase of hardware qubit number and the improvement of error rates, it becomes possible to execute multiple circuits on a quantum chip simultaneously, enhancing the throughput and the utilization of NISQ hardware. Thus, we address a timely problem on how to execute reliably multiple quantum programs on given quantum hardware. This is the first attempt to propose a complete multi-programming process flow for executing an optimal number of workloads in parallel, ensuring output fidelity by analyzing the hardware limitations.
The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies -- such as trapped ions, photonics, neutral atoms, and superconducting circuits -- are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pair-based teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.
Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made worldwide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.
The quantum computing community is increasingly positioning quantum processors as accelerators within classical HPC workflows, analogous to GPUs and TPUs. However, many real-world applications require scaling to hundreds or thousands of physical qubits to realize logical qubits via error correction. To reach these scales, hardware vendors employing diverse technologies-such as trapped ions, photonics, neutral atoms, and superconducting circuits-are moving beyond single, monolithic QPUs toward modular architectures connected via interconnects. For example, IonQ has proposed photonic links for scaling, while IBM has demonstrated a modular QPU architecture by classically linking two 127-qubit devices. Using dynamic circuits, Bell-pairbased teleportation, and circuit cutting, they have shown how to execute a large quantum circuit that cannot fit on a single QPU. As interest in quantum computing grows, cloud providers must ensure fair and efficient resource allocation for multiple users sharing such modular systems. Classical interconnection of QPUs introduces new scheduling challenges, particularly when multiple jobs execute in parallel. In this work, we develop a multi-programmable scheduler for modular quantum systems that jointly considers qubit mapping, parallel circuit execution, measurement synchronization across subcircuits, and teleportation operations between QPUs using dynamic circuits.
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors—all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accuracy. To optimize quantum workloads under these constraints, strategic approaches to task scheduling and resource coordination are essential. One of the persistent challenges in this domain is how to efficiently divide and execute large circuits across multiple quantum processors (QPUs), especially in error-prone environments. In response, we introduce a simulation-based tool that supports distributed scheduling and concurrent execution of quantum jobs on networked QPUs connected via real-time classical channels. The tool models circuit decomposition for workloads that surpass individual QPU limits, allowing for parallel execution through inter-processor communication. Using this simulation environment, we compare four distinct scheduling techniques—among them, a model informed by reinforcement learning. These strategies are evaluated across multiple metrics, including runtime efficiency, fidelity preservation, and communication costs. Our analysis underscores the trade-offs inherent in each approach and highlights how parallelized, noise-aware scheduling can meaningfully improve computational throughput in distributed quantum infrastructures.
Context: Quantum computing is rapidly evolving, offering new opportunities for solving problems in optimization, cryptography, and simulation. However, the limited availability of quantum resources makes efficient utilization of quantum hardware a current challenge. Today’s paradigms often lead to under-utilization of qubits, increased costs, and execution delays, especially in the NISQ era. Objective: This work aims to improve the utilization of quantum hardware by introducing an execution model that integrates multiprogramming at circuit level with quantum shot-wise distribution in a single policy-driven pipeline. Methods: An architecture has been implemented that combines circuit scheduling and shot distribution techniques to aggregate multiple circuits and distribute their shots across heterogeneous QPUs. The approach was empirically validated on actual IBM Quantum devices using a diverse set of reference circuits. Results: The proposal achieved a reduction in cost of 95% and a reduction in tasks 92%. Moreover, the fidelity analysis of the results showed an increase in noise, with an average increase of approximately 20% using different statistical distances. Conclusions: This research provides a usable and extensible solution to increase the efficiency, cost effectiveness, and resilience of quantum workload execution in heterogeneous and dynamic cloud environments. These results obtained suggest that users should weigh the implications of fidelity versus cost (and time) savings based on the application requirements and their goals.