Abstract Quantum computing has the potential to provide solutions to problems that are intractable on classical computers, but the accuracy of the current generation of quantum computers suffer from the impact of noise or errors such as leakage, crosstalk, dephasing, and amplitude damping among others. As the access to quantum computers is almost exclusively in a shared environment through cloud‐based services, it is possible that an adversary can exploit crosstalk noise to disrupt quantum computations on nearby qubits, even carefully designing quantum circuits to purposely lead to wrong answers. In this study, the extent and characteristics of crosstalk noise are studied through tomography conducted on IBM Quantum computers, leading to an enhanced crosstalk simulation model. These results indicate that crosstalk noise is a significant source of errors on IBM quantum hardware, making crosstalk based attack a viable threat to quantum computing in a shared environment. Based on the crosstalk simulator benchmarked against IBM hardware, the impact of crosstalk attacks and develop strategies are assessed for mitigating crosstalk effects. Through a systematic set of simulations, the effectiveness of three crosstalk attack mitigation strategies is assessed, namely circuit separation, qubit allocation optimization via reinforcement learning, and the use of spectator qubits, and show that they all overcome crosstalk attack risk with varying degrees of success and help to secure quantum computing in a shared platform.
The power of quantum computation derives from algorithmic methods that exploit the availability of quantum superposition and entanglement to perform computations that are intractable with classical devices. The race is on to develop hardware that will unleash the promise of quantum algorithms. A handful of different types of hardware are currently being developed with the greatest efforts directed at superconducting, quantum-dot, trapped-ion, photonic, and neutral-atom approaches [1]. While all approaches have strengths and weaknesses, and are at different stages of development, the challenge of creating a practical design that can be scaled to a million or more qubits has not yet been met with any of the existing platforms. One may wonder if we really need a million qubits for a useful quantum computer. The answer depends on our ambition level. On the one hand it is widely believed that a quantum computer with less than 100 reliable qubits will be able to demonstrate a quantum advantage for a well defined computational problem [2]. Demonstration of a quantum computational advantage will generate great excitement in the halls of academia, but will have little broader import. A problem of great practical interest such as ab initio design of chemicals for increasing the yield of fertilizers for crop production, to name but one example, may also require just a few hundred qubits, provided computational sequences with circuit depth of 1010 or more operations can be reliably performed [3]. However, given the current performance of qubit hardware, which has demonstrated error rates not less than ∼10−3, computations with depth 1010 will only produce random outcomes of negligible value. The solution lies in the construction of error-correction circuitry operating on logical qubits, each composed of many physical qubits, which will enable logical error rates many orders of magnitude smaller than physical error rates. Although error correction is remarkably efficient for classical computing devices—Hamming codes detect and correct errors while only requiring roughly 10% overhead in the number of physical bits—quantum error correction is notoriously expensive. New approaches to error correction are the subject of intensive research with one promising direction being the development of codes that are optimized to suppress the dominant error mechanism in a particular physical platform [4,5]. Nevertheless, with physical error rates of 10−4, a plausible target for current qubit systems, the overhead required to reach logical error rates of 10−10 may be measured in thousands of physical qubits for each logical qubit. Thus a quantum computer able to perform deep calculations on a few hundred logical qubits could require a million physical qubits. A consequence of the cost of quantum error correction is that any viable approach to large-scale quantum computing needs to combine high-fidelity quantum logic operations with a capability for integrating large numbers of physical qubits. From this perspective neutral-atom qubits appear particularly promising [6] as they have already demonstrated control of more qubits than any other platform with recent experiments in 1D [7], 2D [8,9], and 3D [10] geometries showing control of up to 50 atomic qubits. Large qubit arrays can potentially be prepared in 1D, 2D, or 3D geometries. The number of proximal qubits increases with the dimensionality, which is advantageous for implementation of error correction code words, and points towards a preference for 2D or 3D arrays. On the other hand, the added overhead of compensating crosstalk incurred when targeting atoms inside a 3D volume makes parallel operations on multiple qubits challenging. A 2D geometry, as shown schematically in Fig. 1, may be an optimal choice since qubit control and measurement can be performed with lasers propagating normal to the plane of the array so that individual qubit sites can be addressed independently. With demonstrated array periods of a few micrometers we require a footprint of less than 1 mm2 for 104 qubits. Conceptual picture of a 2D neutral-atom qubit array. a) High-numerical-aperture lenses project several different wavelengths of light for control and measurement of the qubits. Specific choices of optical wavelengths depend on the atomic species. Not shown are optics for initial laser cooling, transport to the array, beam scanning, and imaging for qubit measurement. b) Averaged fluorescence image of 49 atomic qubits on a 2D grid with |
3.8μm| spacing [8]. From a fundamental perspective the prospects for scaling neutral-atom systems are particularly promising due to the exceptionally large ratio between coherent and incoherent coupling rates. Neutral-atom qubits are encoded in hyperfine ground states for which coherence times of the order of 10 s have been demonstrated [10]. This implies a dephasing rate |
γ∼0.1s−1|. The coherent coupling that enables quantum logic operations is switched on by exciting atoms to high-lying Rydberg states with principal quantum number close to 100 [11,12]. Rydberg excited atoms at spacings of a few micrometers in an optical lattice interact coherently with rates |
g>109s−1|. The ratio between desired coherent coupling and residual incoherent dephasing of g/γ ∼ 1010 establishes a figure of merit that is enabling for scalability. To our knowledge only trapped ions can claim a similar g/γ ratio [13], but without a correspondingly direct path towards controlling thousands of qubits in a single processing unit. Although the g/γ ratio is exceptionally favorable for neutral-atom qubits a complete set of universal gate operations has not yet been demonstrated with high fidelity. Single-qubit gates in large 2D and 3D arrays have reached fidelity |
F∼0.999| [8,10]. However, the highest demonstrated fidelity for two-qubit entanglement mediated by Rydberg-state interactions is |
F<0.8| [6,14,15], a value that is well short of the theoretical limit of |
F>0.9999| [16,17]. There is healthy optimism within the research community working with neutral-atom qubits that combining improved laser sources with reduced noise, better cooling to reduce motional dephasing, and control of stray electric fields that perturb Rydberg atoms will lead to substantially improved gate fidelity in the near future. Recent results with better filtering of laser phase noise have already demonstrated remarkable improvement in ground–Rydberg entanglement [18]. The outlook for the next few years appears very promising. We anticipate that with advances in lasers, optical lattices for atom trapping, deterministic rearrangement and loading of atoms in multi-dimensional arrays [19,20], and optics for fast beam scanning it will be possible to increase the number of physical qubits in a single neutral-atom module to at least 104. A challenging yet plausible path to a million-qubit processor can then be contemplated by connecting 100 such modules with atom–photon entanglement channels [21]. Beyond the potential for advancing the state of the art in computation, development of techniques for single-atom control inside large multi-atom arrays will enable other applications of quantum science including metrology and time-keeping. Rydberg interactions that are at the heart of a neutral-atom quantum computer may lead to multi-atom entanglement and Heisenberg scaling of atomic-clock precision [22]. This work was supported by the NSF and ARL CDQI.
Neutral atom Quantum Processing Units (QPUs) are emerging as a popular quantum computing technology due to their large qubit counts and flexible connectivity. However, performance challenges arise as large circuits experience significant fidelity drops, while small circuits underutilize hardware and face initialization latency issues. To tackle these problems, we propose
multi-programming on neutral atom QPUs, allowing the co-execution of multiple circuits by logically partitioning the qubit array. This approach increases resource utilization and mitigates initialization latency while maintaining result fidelity. Currently, state-of-the-art compilers for neutral atom architectures do not support multi-programming.
To fill this gap, we introduce MultiQ, the first system designed for this purpose. MultiQ addresses three main challenges: (i) it compiles circuits into a
virtual zone layout to optimize spatio-temporal hardware utilization; (ii) it parallelizes the execution of co-located circuits, allowing single hardware instructions to operate on different circuits; and (iii) it includes an algorithm to verify the functional independence of the bundled circuits. MultiQ functions as a cross-layer system comprising a compiler, controller, and checker. Our compiler generates \emph{virtual zone layouts} to enhance performance, while the controller efficiently maps these layouts onto the hardware and resolves any conflicts. The checker ensures the correct bundling of circuits.
Experimental results show a throughput increase from 3.8
× to 12.3
× when multi-programming 4 to 14 circuits, with fidelity largely maintained, ranging from a 1.3% improvement for four circuits to only a 3.5% loss for fourteen circuits. Overall, MultiQ facilitates concurrent execution of multiple quantum circuits, boosting throughput and hardware utilization.
Neutral-atom quantum computers provide a scalable platform for large-scale quantum computation due to their all-optical control, room-temperature operation, and flexible lattice geometry. Although the favorable energy characteristics of these systems are well recognized, the relationship between system-level energy consumption, runtime, and computational fidelity remains poorly understood, limiting practical scheduling decisions. In this work, we develop a hardware-grounded analytical model that captures how energy and runtime scale with qubit utilization in neutral-atom systems.
We introduce PaQit, a fidelity-aware qubit packing framework that integrates device-level Rydberg interaction physics with system-level scheduling to jointly optimize energy, runtime, and fidelity. By translating fidelity targets into packing decisions, PaQit identifies operating regimes that maximize parallelism while respecting interaction-driven crosstalk constraints. We validate the analytical framework using simulations of QuEra's analog Aquila system and digital Gemini system, as well as real hardware executions, demonstrating close agreement between the predicted trends and observed system behavior.
Crosstalk remains a major source of correlated error in quantum systems, yet lacks a precise, community-wide definition - hindering systematic analysis and mitigation. This paper introduces a model-driven approach to crosstalk characterisation through three architecture-inspired noise models: (1) simultaneous two-qubit gate execution, (2) shared qubit interference, and (3) proximity-induced noise from shared control or readout hardware. These models act as both diagnostic tools and building blocks for crosstalk-aware quantum programming. We assess their impact across a broad benchmark suite - quantum simulation, Grover's algorithm, and fault-tolerant primitives like surface, Shor, and Steane codes - and evaluate two mitigation techniques: dynamical decoupling and Pauli twirling. While both are discussed in literature, only dynamical decoupling consistently enhances fidelity across noise types. Our work links low-level noise effects to high-level software engineering, underscoring the role of hardware-software co-design in scalable quantum computing. Model-based, hardware-aware design flows and composable noise abstractions improve error mitigation and program portability. Integrating such strategies into the toolchain is essential for building resilient quantum programs under realistic noise conditions.
Neutral Atom Quantum Computing (NAQC) emerges as a promising hardware platform primarily due to its long coherence times and scalability. Additionally, NAQC offers computational advantages encompassing potential long-range connectivity, native multi-qubit gate support, and the ability to physically rearrange qubits with high fidelity. However, for the successful operation of a NAQC processor, one additionally requires new software tools to translate high-level algorithmic descriptions into a hardware executable representation, taking maximal advantage of the hardware capabilities. Realizing new software tools requires a close connection between tool developers and hardware experts to ensure that the corresponding software tools obey the corresponding physical constraints. This work aims to provide a basis to establish this connection by investigating the broad spectrum of capabilities intrinsic to the NAQC platform and its implications on the compilation process. To this end, we first review the physical background of NAQC and derive how it affects the overall compilation process by formulating suitable constraints and figures of merit. We then provide a summary of the compilation process and discuss currently available software tools in this overview. Finally, we present selected case studies and employ the discussed figures of merit to evaluate the different capabilities of NAQC and compare them between two hardware setups.