The rapid expansion of quantum cloud services has led to long job queues due to single-tenant execution models that underutilize hardware resources. Quantum multi-programming (QMP) mitigates this by executing multiple circuits in parallel on a single device, but existing methods target superconducting systems with limited connectivity, high crosstalk, and lower gate fidelity. Trapped-ion architectures, with all-to-all connectivity, long coherence times, and high-fidelity mid-circuit measurement properties, presents itself as a more suitable platform for scalable QMP. We present CircPack, a hardware-aware circuit packing framework designed for modular trapped-ion devices based on the Quantum Charge-Coupled Device (QCCD) architecture. CircPack formulates static circuit scheduling as a two-dimensional packing problem with hardware-specific shuttling constraints. Compared to superconducting-based QMP approaches, CircPack achieves up to 70.72% better fidelity, 62.67% higher utilization, and 32.80% improved layer reduction. This framework is also capable of scalable, balanced scheduling across a cluster of independent QCCD modules, highlighting trapped-ion systems' potential in improving the throughput of quantum cloud computing in the near future.
The rapid expansion of quantum cloud services has led to long job queues due to single-tenant execution models that underutilize hardware resources. Quantum multi-programming (QMP) mitigates this by executing multiple circuits in parallel on a single device, but existing methods target superconducting systems with limited connectivity, high crosstalk, and lower gate fidelity. Trapped-ion architectures, with all-to-all connectivity, long coherence times, and high-fidelity mid-circuit measurement properties, presents itself as a more suitable platform for scalable QMP. We present CircPack, a hardware-aware circuit packing framework designed for modular trapped-ion devices based on the Quantum Charge-Coupled Device (QCCD) architecture. CircPack formulates static circuit scheduling as a two-dimensional packing problem with hardware-specific shuttling constraints. Compared to superconducting-based QMP approaches, CircPack achieves up to 70.72% better fidelity, 62.67% higher utilization, and 32.80% improved layer reduction. This framework is also capable of scalable, balanced scheduling across a cluster of independent QCCD modules, highlighting trapped-ion systems' potential in improving the throughput of quantum cloud computing in the near future.
As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple QPUs. Different platforms realize this modularity through different communication models: superconducting systems rely on real-time classical links and dynamic-circuit coordination, trapped-ion systems use photonic interconnects for remote entanglement, and neutral-atom systems provide strong intra-core connectivity with proposed optical links for inter-core communication. This heterogeneity makes communication-aware scheduling essential for shared modular quantum cloud environments.
We present InterQ, a communication-aware scheduler for modular QPU architectures with heterogeneous communication models. InterQ jointly considers qubit capacity, placement, parallel execution, and communication-driven dependencies across distributed subcircuits, while enabling adaptive circuit cutting to reduce makespan while balancing fidelity and communication overhead. The framework distinguishes classical-link execution, where measurement and feedforward impose synchronization constraints, from quantum-link execution, where entanglement distribution and state transfer determine coordination cost.
Using a unified simulation framework to compare superconducting, trapped-ion, and neutral-atom modular systems, InterQ shows how communication models and scheduler-driven cutting decisions affect throughput, latency, and fidelity. Across evaluated workloads, InterQ exposes an architecture-dependent tradeoff: neutral-atom modular QPUs achieve the highest fidelity, superconducting systems minimize runtime, and trapped-ion systems provide a balanced intermediate profile across fidelity and makespan.
Trapped-ion quantum computers are a promising platform, offering high-quality qubits with long coherence times and high-fidelity gate operations. The Quantum Charge Coupled Device (QCCD) architecture provides a scalable blueprint by leveraging the ability to shuttle ions between distinct zones. However, realizing such architectures in practice requires software support to manage ion movement across multi-zone layouts. In this work, we propose a compilation strategy for QCCD architectures with multiple processing zones located outside a grid-type memory zone. Unlike previous approaches that treat processing zones as black-boxes, our method explicitly models their structural constraints, enabling optimized ion movement to and through them. It combines qubit partitioning with dependency-aware gate selection to reduce inter-zone shuttling while enabling simultaneous gate execution. We implemented the method in an open-source tool and empirically demonstrated its effectiveness across several QCCD layouts, laying a foundation for the compilation of multi-zone trapped-ion systems.
Trapped-ion Quantum Charge-Coupled Device (QCCD) architectures promise scalability through interconnected trap zones and dynamic ion transport; however, this transport capability creates a complex compilation challenge: how to move qubits efficiently without degrading fidelity. We introduce a routing strategy that turns this challenge into an advantage by exploiting operational parallelism across traps while adapting to both algorithmic structure and device topology through a configurable multi-parameter scoring mechanism. Across a broad suite of benchmarks and QCCD layouts, the method consistently reduces ion-transport overhead and improves execution fidelity, outperforming state-of-the-art routing techniques. These results highlight that explicitly balancing movement overhead and execution parallelism under architectural constraints is key to unlocking the full potential of modular trapped-ion quantum processors.
Trapped-ion quantum computers offer high-fidelity operations, long coherence times, and all-to-all connectivity within a single trap. However, scaling to large circuits is limited by the trap's ion capacity. The QCCD architecture addresses this by enabling ion shuttling across multiple traps, but excessive shuttling increases execution time and degrades fidelity. This paper presents an improved ion-shuttling approach targeting linear QCCD architectures. Building on a state-of-the-art approach [27] for linear trapped-ion devices, our approach introduces a front-layer-based scheduling method that dynamically prioritizes executable gates, along with a novel destination trap selection method for ion-shuttling, effectively reducing unnecessary ion movements. We also present a look-ahead gate selection strategy based on the gate dependency graph, avoiding the high runtime of the gate proximity approach used in the state-of-the-art approach, especially in circuits with all-to-all communication. Our approach achieves up to a 37.67% reduction in shuttle count and up to a 74.91% reduction in compilation time compared to the state-of-the-art approach for linear trapped-ion devices on actual NISQ benchmarks and synthetic circuits. On average, our approach achieves a 16.44% reduction in shuttle count and a 30.77% reduction in compilation time across all test cases. These improvements highlight the effectiveness of our approach, particularly for actual circuits.
Quantum Processing Units are rapidly scaling, with current systems exceeding 100 qubits and targeting one billion operations in the near future. However, Quantum Computing as a Service platforms are facing growing pressure due to high user demand and long execution queues. This is primarily due to resource underutilization and the current execution model, which typically allows only one job per queue slot. Additionally, high costs are associated with the execution of each job. To address these limitations, this paper proposes a multi-batch quantum circuit scheduling approach aimed at increasing hardware utilization and reducing execution queues. To achieve this, different circuits are packed into various batches to be executed within a single job, where the results obtained in each batch are measured in distinct classical registers. These features allow for the reuse of previously utilized qubits in previous batches, enabling them to be used again for execution without the need to submit a new job. The approach has been validated on the 133-qubit IBM Torino processor through large-scale experiments with multiple circuits and controlled noise isolation tests. By reducing repeated queue interactions and job submission overhead, the proposed method increases execution throughput, allowing for numerous circuit executions within a single job. Under the evaluated conditions, this approach achieves significant execution cost reductions compared to traditional circuit submissions. Although the method incurs an overall absolute noise penalty, it allows packing numerous circuits into a single iteration, resulting in substantial financial savings. Furthermore, the noise level remains stable from the first to the last batch, showing no progressive degradation over time.
Trapped ions (TI) are at the forefront of quantum computing implementation, offering unparalleled coherence, fidelity, and connectivity. However, the scalability of TI systems is hampered by the limited capacity of individual ion traps, necessitating intricate ion shuttling for advanced computational tasks. The Quantum Charge-Coupled Device (QCCD) framework has emerged as a promising solution, facilitating ion mobility for universal quantum computation. Current QCCD architectures predominantly feature a linear topology, which is increasingly recognized as inefficient for complex quantum operations. Anticipating the shift towards more efficacious designs, this paper introduces an innovative quantum scheduling strategy optimized for parallel QCCD topologies. Our strategy proposes a probabilistic formula for ion movement, alongside ingenious methods for local layer generation and layer compression, yielding a significant reduction in ion shuttle times. Through simulations, we demonstrate that our strategy not only substantially outstrips the linear model but also exhibits better performance over other parallel strategies that employ greedy algorithms. This is achieved through our nuanced resolution of complexities such as traffic blocks and trap capacity limitations. The consequent reduction in shuttle operations leads to lower energy consumption and an enhancement in the quantum computer's fidelity, ultimately accelerating program execution times.