Quantum Control
for Neutral Atoms

Quantum Control for Neutral Atoms 

Schematic of a neutral atom’s quantum computer operation cycle. Starting with loading atoms into traps, sorting them, performing a set of gates and finally performing the readout. Decisions can be made based on the readout about the next sequence of steps which can include atom transport, reloading, and compensation gate. Image adapted from [1].

Neutral atom quantum computers have emerged as one of the most compelling platforms in quantum computing. Unlike many competing approaches, neutral atoms are naturally identical, optically controlled, and can be reconfigured in mid-experiments using optical tweezers. This makes it possible to realize programmable qubit connectivity and dynamically adapt the array layout to suit the algorithm at hand, including all-to-all entanglement, zone-based architectures, and transversal gate protocols. 

At a high level, neutral atom quantum computers operate through a repeating cycle of loading atoms into a trap array, performing gate operations, and reading out the resulting quantum states. In practice, however, each of these steps carries latency rooted in both the underlying physics and the capabilities of the control stack. 

Meeting these demands requires a control platform that is simultaneously fast, precise and can adapt to programmable connectivity. Quantum Machines’ OPX1000 is designed for this. It supports multiple simultaneous RF output channels, flexible frequency-chirps for atom transport, gate drives, readout solution integrated with off-the shelf camera, and real-time feedback based on the readout. All these operations are referenced to a single global timing framework. QUA, the OPX1000’s native pulse-level language, enables mid-circuit decision branching and control, triggered directly from mid-circuit measurement outcomes, without ever leaving the control system.  

For systems that require additional classical compute power for operations like quantum error correction decoding, fast readout, and heavy-compute transport algorithms, the Quantum Machines’ Open Acceleration Stack provides a low-latency interconnect between the OPX1000 and external CPU or GPU servers, closing the feedback loop in microseconds and enabling classically accelerated workflows at scale. 

Whether your lab is controlling hundreds of atoms today or scaling toward thousands, QM’s solutions can orchestrate the entire workflow, from sorting to gate operations to readout, minimizing latency at every step so researchers can focus on the physics.

Typical Atom Array Setup. The static trap beam is reflected from an SLM (Spatial Light Modulator) which imprints the static trap array on the beam. The static array beam then goes through a polarizing beam splitter (PBS) where it is combined with the dynamical tweezers beams used to perform the atom arrangement. A camera is used for readout.

Atom Transport and Sorting 

A major advantage of neutral atom platforms is the ability to transport atoms dynamically during an experiment. This makes it possible to change qubit configurations on the fly, realizing all-to-all connectivity and enabling continuous reloading of atoms into the array without interrupting ongoing operations.  It also enables unique architectural capabilities like zone-based layouts that separate storage from computation, transversal gate execution across logical qubit boundaries, and active defect correction when atoms are lost mid-sequence.

The standard hardware approach uses a spatial light modulator (SLM) to generate a static array of optical tweezers defining the base trap configuration. Superimposed on this is a dynamic tweezer layer driven by a pair of crossed acousto-optic deflectors (AODs). By injecting multi-tone RF signals into the AODs, multiple tweezers can be independently steered in parallel. Chirping the signal frequency linearly in time sweeps the corresponding tweezer beam across the trapping plane, carrying its captured atom to a new site. 

Time Sequence Of A Single Tweezer Picking Up A Single Atom. At T=0 An Atom Is Trapped In The Static Trap (The One Made Using The SLM In This Example).

Rearrangement requires computing, in real-time, the detuning of each tweezer tone to move its atom to the target site. Chirp rates are limited by trap depth and atomic temperature, and all tweezers share a pulse duration set by the longest move, so each tone is assigned its own chirp rate to ensure simultaneous arrival. 

On the OPX1000 this computation runs directly on the pulse processor in microseconds, a negligible overhead relative to transport pulses that follow. Each tone is shaped using polynomial splines, with AOD nonlinearity corrected in real time. Power ramps use Blackman envelopes to minimize heating during adiabatic transfer between dynamic and static traps.  

Each OPX1000 module generates up to 16 tones, and multiple modules can be combined to produce higher number of tones. Waveforms are synthesized on the fly, with the pulse processor calculating the next segment while playing the current one, keeping memory requirements low and allowing the sequence to adapt dynamically to each new camera image. For more complex rearrangements, precomputed arbitrary waveforms can extend the tone count further. Throughout, QUA allows users to move seamlessly from physical lab parameters to sophisticated rearrangement algorithms, without dropping to low-level FPGA programming. 

Spectrogram of row-by-row sorting with 30 tones of a 40×40 grid. The shown spectrogram is for a 1-millisecond long sorting segment for one of the rows.

As qubit arrays scale toward thousands of atoms, the computational demands of rearrangement grow substantially. Routing algorithms must handle larger occupation matrices, more complex target configurations, and tighter timing budgets for arrays of 10,000 or more sites. For these workloads, Quantum Machines’ Open Acceleration Stack connects the OPX1000 directly to classical CPU and GPU servers via the OPNIC interface. The OPNIC can stream the occupation matrix data to the OPX1000 for simple sorting and transport algorithms and the resulting frequency and chirp parameters for the move. General transport algorithms that require more compute power can also run on the server itself such that the moves are streamed back to the OPX1000 with a latency of few microseconds. This keeps the full rearrangement loop within the quantum real-time budget, even as array sizes and algorithmic complexity grow. 

Fast, Intelligent Readout 

Schematic of the neutral atom readout solution offered by Quantum Machines. The OPX1000 and OPNIC coordinate the readout process with CPU/GPU server, enabling fast processing of measurement data and supporting real-time updates to the experiment.

Readout is currently a dominant source of latency in the experimental cycle — and because stochastic loading, iterative rearrangement, mid-circuit measurement, and calibration routines all depend on it, reducing readout latency has an impact on total cycle time. State discrimination in neutral atom systems is performed by fluorescence imaging where atoms in the bright state scatter photons that are collected by a high-NA objective and detected on an EMCCD or sCMOS camera, while atoms in the dark state produce no signal. Achieving reliable separation between bright and dark states requires a photon budget that is challenging to satisfy at short exposure times. Thus, reducing camera readout latency requires advances in both camera technology and image processing, enabling significant state discrimination even at short exposure times. 

Quantum Machines’ readout solution is designed to reduce latency across the full path from measurement to response. Built on the OPX1000 and the Open Acceleration Stack, it connects imaging hardware, accelerated data processing, and real-time control so that readout results can be made available quickly for use during the experiment. This enables the system to react to measurement outcomes without interrupting the quantum runtime, while also supporting operating modes that keep other tasks running in the non-blocking mode during the readout process. 

QM’s OPX1000 can then branch on this data, trigger rearrangement, selectively apply correction pulses, update gate parameters, or initiate a second imaging cycle, all without leaving the quantum runtime.  

Gate Fidelity and Qubit Control 

A train of Gaussian pulses simulating a concatenated gate sequence (bottom left). Pulse intensity is measured in real time by a power detector; (bottom right) the error signal is computed, and feedback applied within each iteration to compensate for intensity fluctuations.

Single-qubit gates on neutral atom platforms are implemented via direct driving of hyperfine transitions or via stimulated Raman transitions driven by a pair of optical beams. Two-qubit gates are applied using the Rydberg blockade mechanism where one atom is excited to a high-lying Rydberg state, and the strong dipole-dipole interaction prevents simultaneous excitation of neighboring atoms, enabling conditional entanglement. The standard entangling gate is a controlled-Z realized through a sequence of laser pulses driving atoms in and out of the Rydberg manifold. Laser pulses are shuttered and controlled through AOMs or electro-optic modulators, which set the amplitude, phase, and frequency of each optical pulse. 

Gate quality depends on phase coherence across the full control sequence. Any discontinuity in the drive introduces rotation errors that accumulate across a circuit. The OPX1000 generates all control signals from a single phase-coherent timing reference, ensuring that relative phases between gate pulses are preserved regardless of sequence length or complexity. For simple gates, built-in oscillators and pulse envelopes are sufficient.  

As systems scale toward thousands of individually addressed qubits, delivering a separately controlled laser beam to each atom becomes a fundamental bottleneck. Individual beam routing through free-space optics does not scale beyond a few hundred channels. Addressing this requires a photonic integrated circuit approach, where large arrays of waveguide-coupled modulators replace the bulk optical path. Quantum Machines has partnered with QuEra Computing to develop a Photonic Control Unit designed for this regime, capable of addressing gate operations across quantum processing units containing tens of thousands of atoms. 

Maintaining high gate fidelity across a large array also requires continuous calibration. Qubit frequencies drift due to magnetic field fluctuations, laser intensity noise, and thermal effects in the trapping environment. Static calibration routines performed once before a run are insufficient to track these drifts. The Open Acceleration Stack enables online calibration by running optimization routines on a GPU or CPU server in a closed loop with the OPX1000. A reinforcement-learning agent on the classical accelerator side can optimize pulse parameters in real time, correcting drifts that static routines miss. 

Scale-ready with QUA and the Open Acceleration Stack 

Schematic showing the OPX1000 and the Open Acceleration Stack (via OPNIC interconnect) for fast, mid circuit calibration algorithms and QEC.

Neutral atom quantum computers place unusual demands on control software. Each experimental cycle spans transport, gate control, and readout, with distinct timing requirements and dependencies on earlier outcomes. This calls for a unified programming model that integrates quantum and classical operations in a single program that can evolve without rebuilding the stack. 

QUA, the native pulse-level language of the OPX1000, provides exactly this. With a Python-like syntax, it expresses quantum pulse operations like frequency updates, amplitude ramps, phase rotations, chirps, alongside classical control flow including conditionals, loops, and arithmetic. Programs written in QUA are compiled and executed directly on the pulse processor in the OPX1000, running in quantum coherence timescales with deterministic latency and no operating system overhead. A measurement result can be evaluated, a branch taken, and the next pulse dispatched within microseconds of the measurement completing.  

For the transport problem specifically, the full sequence from reading the atom occupation matrix into pulse processor registers, computing tweezer detuning and chirp rates, setting frequencies and phases, and playing the arrangement pulse is executed as a single continuous program. QUA’s concurrency model allows additional operations to run in parallel during this time, maximizing utilization across the experimental cycle. 

Beyond a few atoms, QUA scales with the system roadmap. A program written for a few qubits extends to thousands of qubits by adding multiple OPX1000s and updating the system configuration file. The QUA compiler orchestrates all OPXs as one, handling synchronization and data sharing transparently. This means that the same code used to develop and debug a protocol on a small testbed runs unchanged on a full-scale system. 

For workloads that exceed what the pulse processor can handle alone, for example, large-scale routing algorithms, deep reinforcement-learning inference, syndrome decoding for quantum error correction, or the computationally intensive image processing pipeline described in the readout section, the Open Acceleration Stack extends the QUA runtime to external classical accelerators. The OPNIC connects the OPX1000 to standard CPU and GPU servers over a high-bandwidth, low-latency link, with roundtrip communication times of about 4 microseconds. Quantum error correction decoding can be written on the server that receives syndrome data streamed from the OPX1000, computes corrections, and returns them in time for feed-forward operations before the next stabilizer round. Calibration routines run as background processes, continuously updating qubit parameters without interrupting computation. 

Together, the OPX1000 and the Open Acceleration Stack provide a complete control and orchestration layer for neutral atom quantum computers, scaling from today’s research systems to future fault-tolerant architectures and allowing researchers to focus on physics rather than control infrastructure. 

References  

[1] Adams, C. S. et al. “Rydberg atom quantum technologies.” J. Phys. B: At. Mol. Opt. Phys. 53, 012002 (2020). 

[2] Wintersperger, K., Dommert, F., Ehmer, T. et al. “Neutral atom quantum computing hardware: performance and end-user perspective.” EPJ Quantum Technol. 10, 32 (2023). 

[3] Saffman, M. “Quantum computing with atomic qubits and Rydberg interactions: progress and challenges.” J. Phys. B: At. Mol. Opt. Phys. 49, 202001 (2016). 

[4] Henriet, L., Béguin, L., Signoles, A., Lahaye, T., Browaeys, A., Reymond, G.-O. & Jurczak, C. “Quantum computing with neutral atoms.” Quantum 4, 327 (2020). 

[5] Bluvstein, D., Geim, A. A., Li, S. H. et al. “A fault-tolerant neutral-atom architecture for universal quantum computation.” Nature 649, 39–46 (2026).