Quantum Charge-Coupled Device Architecture

• Quantum Charge-Coupled Device (QCCD) Architecture is a scalable trapped-ion quantum computing framework using modular electrode arrays for precise ion confinement and transport.
• The framework integrates dedicated memory, gate, readout, and transport zones to enable high-fidelity operations and deterministic quantum matter-link transitions.
• Advanced compiler scheduling and multiplexed control electronics optimize shuttling and gate operations, paving the way for fault-tolerant, large-scale quantum computation.

Quantum Charge-Coupled Device (QCCD) Architecture is a trapped-ion quantum computing framework in which modular electrode arrays enable scalable confinement, shuttling, and gate operations on large networks of atomic qubits. Each QCCD module comprises planar or 3D arrangements of RF and DC electrodes supporting memory, gate, readout, and transport zones, with adiabatic ion movement between and across modules. The architecture achieves high-fidelity quantum information transport, enables modular scalability, and provides a pathway to fault-tolerant, utility-scale quantum computation.

1. Principles and Design of QCCD Architectures

The QCCD paradigm is rooted in structuring ion-trap processors as networks of zones—memory (storage), gate (processing), readout, and transport—interconnected via precisely shaped electrical potentials on segmented electrodes. Each module is essentially a surface- or 3D-printed array of RF rails and segmented DC electrodes, with zones allocated for specific functions:

• Memory zones: Hold ion chains, shielded from laser and electrical noise.
• Gate zones: Allow for fast, high-fidelity single- and two-qubit gates, integrating tightly focused lasers or microwaves.
• Readout zones: Dedicated to state-preparation and measurement (SPAM).
• Transport zones: Engineered for shuttling ions (split, move, merge) and often include T-, X-, or Y-junctions for directional routing.

Modules are fabricated as planar chips (RF rails ~270 µm wide, DC electrodes ~220 µm wide, 1 µm thick, spaced by 10 µm gaps (Akhtar et al., 2022)) or with integrated 3D junction arrays (junction units: 600 µm × 600 µm footprint, 3D RF rails 247 µm high (Taniguchi et al., 21 Sep 2025)). QCCD modules can be arrayed in 1D chains, 2D grids, or more complex topologies, with modular interconnectivity via "matter-link" shuttling or photonic entanglement nodes.

2. Deterministic Ion Transport and Quantum Matter-Links

A key advance for modular QCCD systems is the realization of deterministic, high-fidelity quantum "matter-link" transport between neighboring modules (Akhtar et al., 2022). In Akhtar et al., two adjacent microchip modules ("Alice" and "Bob") are aligned to sub-10 µm precision so their electrode arrays form a continuous trapping well across a ~10 µm gap. Time-dependent DC potentials $V(x,t)$ are computed via finite-element simulation and least-squares optimization, enforcing:

• Vanishing stray fields at the ion position,
• Fixed axial curvature (secular frequency ~141 kHz),
• Minimal total electrode voltage,
• Pre-distortion for RC filter compensation and Savitzky-Golay smoothing.

Transport proceeds over 684 µm in 412.5 µs (rate ~2424 s⁻¹) with infidelity associated with ion loss below $7\times10^{-8}$ after $1.5\times10^{7}$ shuttles. Ramsey-type qubit phase-coherence experiments demonstrate no measurable impact from shuttling, yielding $T_2^* = 560(40)$ ms for stationary ions and a per-link dephasing infidelity below $5\times10^{-4}$.

Such quantum matter-links constitute a deterministic, ultralow-loss "quantum wire" for modular QCCD integration, supporting thousands of ions per module and scalable reticulation of multi-chip arrays without photonic overheads or probabilistic gates.

3. Compiler and Scheduling for QCCD

Control and orchestration of gate execution and ion shuttling in QCCD architectures require advanced compiler phases and scheduling heuristics, given the non-trivial assignment of ions to zones and costs associated with movements (Zhu et al., 2 May 2025, Schmale et al., 2022).

Compilers (e.g., S-SYNC (Zhu et al., 2 May 2025)) co-optimize the number of shuttling and SWAP operations using static-topology graph abstraction. Physical sites (ions or free space) become graph vertices; SWAPs (local reorders in-trap) and shuttles (inter-trap moves) are weighted by time and motional heating cost:

• Inner weights ($w_1 \sim 0.001$) for intra-trap swaps,
• Outer weights ($w_2 = 1$ per segment plus junction crossings) for inter-trap shuttling.

A frontier-based scheduler minimizes $\alpha S + \beta W$, selecting swaps and shuttles that bring operands for two-qubit gates into adjacency. Scheduling algorithms employ heuristic scores $H(e)$ that combine shortest path length, penalties for vacant traps, and decay factors to avoid redundant zig-zag moves. Empirical results show $3.69\times$ reduction in shuttling and up to $1.73\times$ improvement in application success rate for circuits mapped to various QCCD topologies.

Complementary strategies (QVLS-Q1 backend (Schmale et al., 2022)) interleave gate and move instructions, using heuristics (junction-distance, partner-sorting) to minimize shuttling and optimize gate ordering. These reduce shuttle counts per gate by up to $>50\%$ compared to random ordering, with compile times scaling polynomially.

4. Topology, Scalability, and Optimization

Trap zoning, capacity, and interconnectivity directly impact QCCD performance and scalability (Murali et al., 2020, Ovide et al., 2024, Zhu et al., 2 May 2025). Design choices include:

• Trap capacity: $10$–$25$ ions per zone balance local gate speed and shuttle overhead; excess ions increase gate time and heating, while fewer ions inflate the number of shuttles.
• Module connectivity: Linear chains, 2D grids, and rings. Grids (e.g., $G_{2\times3}$ or $G_{3\times3}$) yield shortest shuttle distances, best performance for highly-connected circuits.
• Electrode segmentation: Fine segmentation increases flexibility, lowering generic-swap costs.
• Initial placement: Spatio-temporal aware (STA) allocation (Ovide et al., 2024) achieves up to $50\%$ speedup over greedy allocation. Ring topologies reduce average shuttle distance for many workloads.

SAT-based scheduling models (Schoenberger et al., 2023, Schoenberger et al., 2024) formalize movement as Boolean decision problems, yielding minimal time steps for small instances and guiding heuristic approaches for large devices.

5. Control Electronics and Resource Requirements

Scaling QCCD requires advanced analog control: each electrode must be driven by programmable, low-noise, high-bandwidth voltage ramps. Traditional one-to-one DAC-to-electrode mapping is not scalable—$10,000$ electrodes per $1,000$–qubit device necessitate extensive resources (Ohira et al., 2 Apr 2025). Multiplexed control systems use time-division multiplexing (TDM) to drive $M$ electrodes per DAC, reducing DAC count by orders of magnitude (e.g., $104$ DACs and $13$ FPGAs for $10,000$ electrodes).

FPGA-based ±50 V, 16-channel DAC systems (Oshio et al., 2024) deliver:

• ±50 V output range,
• 16 MUPS per channel,
• 20 V/µs slew rate,
• >200 kHz bandwidth,
• Sub-µV noise.

Voltage scaling (from ±10 V to ±50 V) increases secular frequency by $\sqrt{5}$, enabling faster adiabatic transports with lower heating. Quadratic programming optimizes waveform steps for shuttling, aligning motional excitation and speed constraints.

6. Quantum Error Correction, Parallelism, and Large-Scale Integration

QCCD architectures support implementation of quantum error correction (QEC) codes, notably the surface code (Jones et al., 27 Oct 2025), by modularizing traps and optimizing for logical clock speed and hardware efficiency. Notably, cells with capacity $k=2$ ions are optimal for surface-code QEC, minimizing logical cycle time $\tau_L$ and error rate $\epsilon_L$ for fixed code distance $d$:

$\tau_L \approx 4d(t_g + t_s) + \tfrac{1}{2}d^2 t_m$

where $t_g$ is two-qubit gate time, $t_s$ average shuttling time, and $t_m$ measurement time.

Design recommendations include:

• $k=2$ capacity cells on a 2D grid,
• Direct wiring for up to $10$ logical qubits; multiplexed DACs (WISE) for larger systems,
• Aggressive cooling and platform co-design for power scalability.

Analyses of parallelism vs. ion movement (Ovide et al., 6 Feb 2025) show that optimal fidelity arises when parallelization is sufficient to reduce logical depth without incurring excess motional heating from shuttling. Algorithms with high two-qubit density (e.g., QAOA, QFT) benefit most from QCCD parallelism, with fidelity gains up to $48\%$.

7. Future Directions and Impact

The QCCD architecture is advancing toward utility-scale quantum computers capable of fault-tolerant, modular operation. Recent demonstrations of quantum matter-links (Akhtar et al., 2022) establish the physical mechanism for connecting modules with deterministic, high-fidelity ion transport at rates $2,424$ s$^{-1}$ and transport infidelity below $7\times10^{-8}$. Compiler-driven co-optimization of shuttle/SWAP counts (Zhu et al., 2 May 2025), resource-efficient control stacks (Ohira et al., 2 Apr 2025, Oshio et al., 2024), and surface-code-optimized layouts (Jones et al., 27 Oct 2025) indicate practical pathways to error budgets compatible with utility quantum computation.

Continued innovation is expected in:

• 3D-printed junction arrays with ultra-deep, low-anharmonicity traps (Taniguchi et al., 21 Sep 2025),
• SAT-based and dependency-aware multi-zone compilation tools (Schoenberger et al., 2023, Schoenberger et al., 12 May 2025, Schoenberger et al., 2024),
• Precision synchronization of multiplexed electronics,
• Integration with photonic links for remote entanglement and large-scale quantum interconnects,
• Robust error-mitigation protocols and dynamically reconfigurable ion assignments.

The QCCD framework is central to scaling trapped-ion quantum processors beyond current device limitations, establishing a modular, high-fidelity substrate for algorithms demanding thousands to millions of qubits.