Neutral Atom Quantum Computing

Updated 25 October 2025

Neutral atom quantum computing is a paradigm that uses optically trapped atoms as qubits, offering long coherence times and tunable interactions.
It employs techniques like Rydberg blockade and controlled collisions to achieve fast, high-fidelity single- and multi-qubit gate operations.
Scalable architectures with dynamic atom rearrangement and integrated error correction enable robust, application-driven quantum computation.

Neutral atom quantum computing is a technological paradigm wherein individual atoms—held and manipulated in optical traps or tweezers—function as qubits for the implementation of quantum logic and algorithms. These platforms exploit atomic hyperfine or nuclear spin states for computational encoding, enable dynamic and high-fidelity entangling operations via Rydberg blockade or controlled collisions, and accommodate scalable, reconfigurable architectures that combine long coherence times with strong, tunable interactions. The convergence of advances in optical control, microfabrication, error correction, and quantum compilation has positioned the field at the forefront of efforts toward universal, fault-tolerant, and application-driven quantum computation.

1. Fundamental Physical Principles and Device Architectures

Neutral atom processors utilize optically trapped atoms—alkali (e.g., Rb, Cs) or alkaline-earth(-like) (e.g., Yb, Sr)—as qubits, with ground-state hyperfine manifolds or nuclear spin sublevels serving as the computational basis. Arrays are formed using tightly focused laser beams via optical tweezers, which can be arranged in configurable one-, two-, or three-dimensional geometries using spatial light modulators (SLMs) or acousto-optic deflectors (AODs) (Henriet et al., 2020, Wintersperger et al., 2023).

Qubit readout and register initialization leverage high-resolution fluorescence imaging to identify atomic occupancies and state populations. Reconfigurable, defect-free arrangements are achieved by deterministic rearrangement of atoms via mobile tweezers, often with feedback control implemented on FPGA hardware for millisecond-level speedup (Wintersperger et al., 2023, Guo et al., 19 Nov 2024).

Long-range entangling operations are mediated primarily by the Rydberg blockade effect, wherein excitation to high-lying Rydberg states induces strong van der Waals interactions ( $V(r) \propto 1/r^6$ ) that inhibit simultaneous excitation of nearby atoms within the blockade radius (Wintersperger et al., 2023). This enables native implementation of two-qubit gates (CZ, CNOT) and multi-qubit gates (e.g., CCZ) with high spatial connectivity (Henriet et al., 2020, Schmid et al., 2023, Wang et al., 2023). Alternatively, state-dependent controlled collisions in an optical lattice—with dynamical phase accumulation—provide a complementary two-qubit gate mechanism (Nakahara et al., 2010).

Trap lifetimes range from 10–60 s at room temperature and extend to thousands of seconds under cryogenic operation. Coherence times for qubit superpositions exceed seconds for nuclear spin states and $O(1)$ s for electronic spins, while Rydberg state lifetimes are on the order of $100$-- $500\,\mu$ s (Wintersperger et al., 2023). The gating times for single- and two-qubit operations are typically in the $0.2$-- $2\,\mu$ s range (Wintersperger et al., 2023, Radnaev et al., 15 Aug 2024).

2. Control, Readout, and Quantum Gate Implementation

Single-qubit gates are generally realized through Raman transitions between hyperfine or spin states, using tightly focused, frequency-controlled laser beams. The typical Hamiltonian under far-detuned Raman coupling is: $H_1 = \frac{1}{2} \epsilon \sigma_z - \frac{\Omega_0 \Omega_1}{4\Delta} \sigma_x, \quad \epsilon = (E_1 - E_0) + \frac{\Omega_1^2-\Omega_0^2}{4\Delta},$ where $\Omega_{0,1}$ are Rabi frequencies and $\Delta$ is detuning from an excited state (Nakahara et al., 2010). Arbitrary single-qubit rotations are achieved by tuning pulse duration, intensity, and phase (Henriet et al., 2020, Wintersperger et al., 2023, Radnaev et al., 15 Aug 2024).

Entangling gates employ two main physical mechanisms:

Rydberg blockade gates: Pairs (or larger sets) of atoms are excited to Rydberg levels, with dipole-dipole interactions effecting a conditional phase when both atoms are within blockade range. The canonical gate protocol for a controlled-Z operation consists of a resonant $\pi$ -pulse targeting the control atom, a $2\pi$ -pulse on the target, and a final $\pi$ -pulse on the control. Multi-qubit gates (e.g., CCZ, Toffoli) are also native and can be implemented with a minimal pulse sequence (Henriet et al., 2020, Schmid et al., 2023, Wintersperger et al., 2023, Radnaev et al., 15 Aug 2024).
State-dependent controlled collisions: Atoms are adiabatically transferred into a shared optical lattice, where selective collisions impart a controllable, state-dependent dynamical phase. The acquired phase is $\exp(-i U t_{\text{hold}})$ , with $U$ the on-site interaction energy (Nakahara et al., 2010).

Readout relies on high-fidelity fluorescence detection, with the latest advances enabling non-destructive state-selective readout (NDSSR). By using cycling transitions, loss during readout is minimized to below 1%, which increases the shot rate and enables mid-circuit measurement crucial for error correction (Radnaev et al., 15 Aug 2024, Muniz et al., 11 Jun 2025).

Scalability derives from (i) the ability to rearrange and dynamically assemble registers with hundreds of atoms in arbitrary geometries, (ii) high-fidelity gate operations ( $>$ 99.9% for single-qubit and up to $\sim$ 99.5% for two-qubit gates), and (iii) flexible connectivity ranging from local neighbor to full all-to-all interaction via atom movement or long-range blockade (Wintersperger et al., 2023, Radnaev et al., 15 Aug 2024, Muniz et al., 11 Jun 2025).

3. Error Correction, Fault Tolerance, and Atom Loss Management

Neutral atom processors are subject to error channels including decoherence, imperfect laser control, ambient field fluctuations, and atom loss due to residual vacuum or photon scattering. Additional error subclasses arise from Rydberg physics: blackbody radiation-induced transitions, radiative decay from the Rydberg state, and intermediate-state scattering (Cong et al., 2021).

Recent work demonstrates hardware-efficient, fault-tolerant schemes tailored to neutral atoms:

By converting dominant error channels (leakage, decay) into detectable atom loss (erasure errors), e.g., through erasure conversion protocols, codes such as [4,2,2] and [9,1,3] Bacon-Shor have been implemented to achieve lower logical error rates than the physical error rate (Reichardt et al., 18 Nov 2024).
Ancilla recycling, midcircuit measurement, and real-time atom replacement allow for logical circuits (including up to 41 rounds of syndrome extraction) to proceed despite stochastic atom loss (Muniz et al., 11 Jun 2025, Li et al., 18 Jun 2025).
Error correction protocols are designed to reduce error propagation by mapping leakage to a single-basis (Pauli-Z) error; bias-preserving gate sequences and optical pumping are used for leakage conversion and correction (Cong et al., 2021).

Fast, continuous atom replacement techniques leverage metastable qubit encoding (e.g., $^{171}$ Yb in $^3P_0$ ), spatial separation of reloading and computation zones, and nondestructive readout, enabling sustained computation with minimal disturbance to active qubits. Reload rates up to 500 times per second and new array preparation at 30 Hz have been demonstrated (Li et al., 18 Jun 2025).

4. Quantum Compilation, Circuit Mapping, and Algorithmic Optimization

The unique capabilities of neutral atom architectures—shuttling, native multi-qubit gates, dynamic connectivity—necessitate hardware-aware compilation strategies:

Hybrid circuit mapping combines gate-based and shuttling-based routing, optimizing for overall circuit fidelity and execution time by dynamically partitioning gates to either mapping modality (Schmid et al., 2023).
Atomique and similar frameworks support field-programmable qubit arrays, efficiently scheduling qubit mapping, atom movement, and gate operations using MAX k-Cut algorithms and parallelism-aware routers to reduce SWAP overhead and depth (Wang et al., 2023).
The DasAtom framework partitions circuits into subcircuits, solves each with optimal local mapping, and shuttles atoms between mappings, yielding exponential fidelity improvements for QFT and other dense circuits (Huang et al., 5 Sep 2024, Gao et al., 18 Jun 2025).
Qompose applies machine-learning models to select optimal 2D topologies (e.g., Square, S-Triangle, T-Triangle) for individual quantum circuits, using statistical features (entanglement variance, critical depth, PageRank), mapping frequently interacting qubits close together to minimize pulse count and error accumulation (Silver et al., 29 Sep 2024).
Compilation algorithms based on matrix decompositions (QSD, QR) and quaternion-based single-qubit gate optimization translate arbitrary unitaries to the native C( $\theta$ , $\phi$ ) and entangling gate set, reducing depth and facilitating direct hardware calibration (Xu, 9 Jan 2025).

5. Applications: Quantum Simulation, Optimization, and Algorithmic Domains

Neutral atom platforms are leveraged across analog-digital and hybrid computational regimes:

Quantum Simulation: Native implementation of Hamiltonians such as the Ising, Heisenberg, and XY models (e.g., $\mathcal{H} = (\hbar/2)\Omega(t)\sum_j\sigma_j^x - \hbar\delta(t)\sum_j n_j + \sum_{i\neq j}(C_6/r_{ij}^6)n_i n_j$ ) enables exploration of quantum magnetism, quantum phase transitions, lattice gauge theories, and topological phenomena (Henriet et al., 2020, Wang et al., 2023, Grotti et al., 21 Oct 2025).
Combinatorial Optimization: The Rydberg blockade naturally enforces constraints for Maximum (Weighted) Independent Set (MIS/MWIS) problems, directly translating to efficient analog Hamiltonian simulation of QUBO and portfolio optimization tasks. Unit disk graph embeddings and spatial partitioning techniques further enhance scalability, allowing the solution of large QUBO instances with quantum-classical hybrid approaches (Das et al., 21 Oct 2025, Grotti et al., 21 Oct 2025).
Quantum Chemistry and Molecular Simulation: Gate-based digital approaches (e.g., VQE) map fermionic Hamiltonians onto qubit arrays for approximating ground-state energies and molecular structure. Neutral atom platforms have demonstrated the simulation of molecules such as H $_2$ , LiH, and BeH $_2$ (Wintersperger et al., 2023, Grotti et al., 21 Oct 2025).
Quantum Machine Learning: Quantum evolution kernels and graph-based tasks are implemented by encoding graphs in atomic registers and extracting quantum-feature maps via measurement probabilities, enhancing classical algorithms’ expressiveness, especially for machine learning and pattern recognition (Grotti et al., 21 Oct 2025).

6. Architectural Innovations and Future Prospects

Recent advances point to several promising directions:

Dual-type dual-element arrays: Combining individually trapped qubits with small atomic ensembles (ancilla) enables fast, high-fidelity, reconfigurable gates and rapid, non-demolition midcircuit readout, supporting advanced error-correction and syndrome extraction techniques (Zhang et al., 21 Mar 2025).
Hardware acceleration: FPGA-based architectures for real-time feedback, register assembly, and parallel rearrangement have dramatically reduced initialization overhead, enabling efficient scaling to large arrays (Guo et al., 19 Nov 2024).
Algorithmic benchmarks: Optimal compilation strategies for QFT circuits, in both 1D (Linear Path) and 2D (Zigzag Path) grid architectures, minimize atom movement and achieve distances saturating theoretical lower bounds (Gao et al., 18 Jun 2025).

Future directions include further integration of midcircuit measurement, erasure-based error correction, fusion-based quantum computation, atom–light hybrid interfaces, and the extension of current compilation and scheduling algorithms to arbitrary circuit classes. As register sizes approach and surpass 1,000 qubits and gate fidelities improve, neutral atom processors are poised for both near-term scientific advantage and robust, fault-tolerant, large-scale quantum computation.

Table 1. Comparison of Key Hardware Metrics for Neutral Atom Quantum Computing Systems

Metric	Typical Value/Method	Source
Qubit encoding	Hyperfine/nuclear spin of alkali/alkaline-earth atom	(Wintersperger et al., 2023)
Single-qubit gate fidelity	0.996–0.999, $\sim 250\,\text{ns}$ gate times	(Radnaev et al., 15 Aug 2024)
Two-qubit gate fidelity	0.955–0.995 (CZ, CCZ), $\sim 400\,\text{ns}$	(Radnaev et al., 15 Aug 2024)
Array size	100–1,000+ qubits (scalable to 10,000+)	(Henriet et al., 2020)
Trap lifetime	10–60 s (room temp); $10^3$ s (cryogenic)	(Wintersperger et al., 2023)
Rydberg blockade radius	5–15 $\mu$ m, $V(r) \sim 1/r^6$	(Wintersperger et al., 2023)
Preparation time	$\sim$ 400 ms (inc. rearrangement/readout)	(Wintersperger et al., 2023)
Readout: nondestructive	Atom loss $<$ 1%, NDSSR, shot rate 8.2 Hz	(Radnaev et al., 15 Aug 2024)

Neutral atom quantum computing leverages the tunable, scalable, and high-fidelity control of atomic ensembles to deliver a versatile, architecture-adaptive platform capable of supporting a diverse range of quantum algorithms and tasks. Continued innovation in physical device engineering, error mitigation, quantum compilation, and application mapping is rapidly advancing the field toward universal, fault-tolerant quantum computation.