Algorithm and hardware-aware compilation co-design is essential for the efficient deployment of near-term quantum programs. We present a compilation case-study implementing QCrank - an efficient encoding protocol for storing sequenced real-valued classical data in a quantum state targeting neutral atom-based Dynamically Programmable Qubit Arrays (DPQAs). We show how key features of neutral-atom arrays such as high qubits count, operation parallelism, multizone architecture, and natively reconfigurable connectivity can be used to inform effective algorithm deployment. We identify algorithmic and circuit features that signal opportunities to implement them in a hardware-efficient manner. To evaluate projected hardware performance, we define a realistic noise model for DPQAs using parameterized Pauli channels, implement it in Qiskit circuit simulators, and assess QCrank's accuracy for writing and reading back$24-320$real numbers into$6-20$qubits. We compare DPQA results with simulated performances of Quantinuum's H1-1E and with experimental results from IBM Fez, highlighting promising accuracy scaling for DPQAs.
Algorithm and hardware-aware compilation co-design is essential for the efficient deployment of near-term quantum programs. We present a compilation case-study implementing QCrank - an efficient encoding protocol for storing sequenced real-valued classical data in a quantum state targeting neutral atom-based Dynamically Programmable Qubit Arrays (DPQAs). We show how key features of neutral-atom arrays such as high qubits count, operation parallelism, multizone architecture, and natively reconfigurable connectivity can be used to inform effective algorithm deployment. We identify algorithmic and circuit features that signal opportunities to implement them in a hardware-efficient manner. To evaluate projected hardware performance, we define a realistic noise model for DPQAs using parameterized Pauli channels, implement it in Qiskit circuit simulators, and assess QCrank's accuracy for writing and reading back24−320real numbers into6−20qubits. We compare DPQA results with simulated performances of Quantinuum's H1-1E and with experimental results from IBM Fez, highlighting promising accuracy scaling for DPQAs.
Neutral-atom quantum computing (NAQC) offers distinct advantages such as dynamic qubit reconfigurability, long coherence times, and high gate fidelities, making it a promising platform for scalable quantum computing. Among existing implementations, the Dynamically Field-Programmable Qubit Array (DPQA) architecture has emerged as the most prominent NAQC platform, enabling large-scale, high-fidelity operations through dynamic atom rearrangement and global Rydberg excitation. Despite these strengths, efficiently implementing quantum circuits like the Quantum Fourier Transform (QFT) remains a significant challenge due to atom-movement overheads and connectivity constraints. This paper introduces optimal compilation strategies tailored to QFT circuits on the DPQA architecture, addressing these challenges for both linear and grid-like configurations. By minimizing atom movements, the proposed methods achieve theoretical lower bounds in movement counts while preserving high circuit fidelity. Comparative evaluations against state-of-the-art DPQA compilers demonstrate the superior performance of the proposed methods, which could serve as benchmarks for evaluating the performance of future DPQA compilers.
Dynamically field-programmable qubit arrays (DPQA) have recently emerged as a promising platform for quantum information processing. In DPQA, atomic qubits are selectively loaded into arrays of optical traps that can be reconfigured during the computation itself. Leveraging qubit transport and parallel, entangling quantum operations, different pairs of qubits, even those initially far away, can be entangled at different stages of the quantum program execution. Such reconfigurability and non-local connectivity present new challenges for compilation, especially in the layout synthesis step which places and routes the qubits and schedules the gates. In this paper, we consider a DPQA architecture that contains multiple arrays and supports 2D array movements, representing cutting-edge experimental platforms. Within this architecture, we discretize the state space and formulate layout synthesis as a satisfiability modulo theories problem, which can be solved by existing solvers optimally in terms of circuit depth. For a set of benchmark circuits generated by random graphs with complex connectivities, our compiler OLSQ-DPQA reduces the number of two-qubit entangling gates on small problem instances by 1.7x compared to optimal compilation results on a fixed planar architecture. To further improve scalability and practicality of the method, we introduce a greedy heuristic inspired by the iterative peeling approach in classical integrated circuit routing. Using a hybrid approach that combined the greedy and optimal methods, we demonstrate that our DPQA-based compiled circuits feature reduced scaling overhead compared to a grid fixed architecture, resulting in 5.1X less two-qubit gates for 90 qubit quantum circuits. These methods enable programmable, complex quantum circuits with neutral atom quantum computers, as well as informing both future compilers and future hardware choices.
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.
Dynamically field-programmable qubit arrays based on neutral atoms feature high fidelity and highly parallel gates for quantum computing. However, it is challenging for compilers to fully leverage the novel flexibility offered by such hardware while respecting its various constraints. In this study, we break down the compilation for this architecture into three tasks: scheduling, placement, and routing. We formulate these three problems and present efficient solutions to them. Notably, our scheduling based on graph edge-coloring is provably near-optimal in terms of the number of two-qubit gate stages (at most one more than the optimum). As a result, our compiler, Enola, reduces this number of stages by 3.7x and improves the fidelity by 5.9x compared to OLSQ-DPQA, the current state of the art. Additionally, Enola is highly scalable, e.g., within 30 minutes, it can compile circuits with 10,000 qubits, a scale sufficient for the current era of quantum computing. Enola is open source at https://github.com/UCLA-VAST/Enola
Quantum computers are noisy; without quantum error correction (QEC), deep programs fail as qubits lose information due to decoherence. Among QEC approaches, bivariate bicycle (BB) codes offer low overhead and constant-depth syndrome extraction, while neutral-atom arrays provide scalable, reconfigurable qubit layouts. However, executing BB-code primitives on neutral-atom systems requires a hardware-aware mapping that respects movement, zoning, and interaction constraints. We present Park-n-Ride, a system for compiling and executing the BB code on neutral-atom processors. Park-n-Ride introduces a module layout and movement model aligned with neutral-atom constraints, exposes a compact BB-native logical interface for compilation, and integrates scheduling mechanisms that enable efficient execution on zoned architectures. By co-designing BB-code abstractions with hardware execution, Park-n-Ride provides a practical path from qLDPC primitives to resource-efficient, high-throughput execution on reconfigurable neutral-atom arrays.
Resource estimation is a significant challenge in evaluating fault tolerant quantum computers. Existing approaches often rely on either fixed architectural assumptions or coarse analytical models that fail to capture the interaction between hardware constraints and circuit compilation. This challenge is particularly acute for neutral atom quantum computers, where architectural features such as atom movement, measurement zones, and multi-species arrays introduce a broad design space for implementing fault tolerant computation. Addressing the need for a tighter feedback loop between hardware design and practical application development, we present a compilation-driven framework for quantum resource estimation that translates arbitrary quantum circuits into logical primitive operations with known physical resource costs. This framework allows for easily configurable hardware assumptions that enable rapid comparison of different architectural design choices. We apply our approach to two early fault tolerant quantum simulation and optimization workloads, assuming the use of the surface code, revealing several architectural trends. While the production of magic states continues to be the dominant source of overhead for these benchmarks, access to movement can save time on cultivation and important transversal gates. As problem size grows, routing and qubit movement become dominant bottlenecks, highlighting the need for movement-aware compiler optimizations and frugal routing strategies. Finally, our results suggest that neutral atom architectures combining dual-species arrays with controlled qubit movement offer a promising path toward near-term advantage on fault tolerant devices.