TRAPPED-ION CONTROL SYSTEM WORKFLOW

How I build trapped-ion control systems.

Start with the experiment. Make timing measurable. Keep the hardware replaceable. Stop claims where the evidence stops.

Pick a topic and follow the evidence →

01 · Define the contract

Timing Can the scope see what the sequence promised? pulse width · alignment · phase · repeatability Architecture Keep physics intent stable while hardware changes. requirements · contracts · registries · adapters

02 · Prove the path

Verification Test software and the electrical path separately. deterministic simulation · TTL/photon emulation Full stack Keep one run traceable from circuit to photon data. OpenQASM · backend · PMT · evidence

03 · Operate + scale

Calibration Update only when the measurement earns it. stale checks · bounded candidate · R² gate Scale up Grow by giving each controller a bounded job. active set · controller shard · evidence hand-off

EVIDENCE KEY Implemented: ARTIQ / Kasli-SoC Integration target: portable FPGA + AWG/DDS No physical-ion result is claimed here.

01 · Measurement-aware timing validation

A timing claim is not complete until the scope agrees.

The interactive trace is a literature-informed model of the intended sequence. It defines the signals, timing relationships and acceptance questions that a real capture would need to verify.

Published parameter MW Qubit 1 · Gate operation Ref [2] · PRX 15, 021079 (2025)

A / ΩAmplitude: Ω₁ / 2π = 94.8 kHz
f / ωFrequency: f_μw ≈ 12.6 GHz
τDuration: 313 µs
φPhase: φ(t) = 0.749 sin(2π · 94.8 kHz · t)

Notation: A amplitude · Ω/2π Rabi rate · f frequency · ω/2π angular-frequency form · τ duration · φ phase · Δ detuning

Each channel has a fixed color. Pulse height is an ON/OFF envelope unless a paper reports intensity or Rabi frequency; stage widths are visually compressed.

Evidence boundary: this is a composite literature model, not one experimental shot and not eleQtron proprietary calibration. Ref [1] reports the cooling sequence at an axial frequency of 117.48(11) kHz; Ref [2] reports the MAGIC gate in a different setup at 98.08 kHz. Unpublished hardware settings are shown as N/R (not reported).

References: [1] Sriarunothai et al., J. Mod. Opt. 65, 560–567 (2018) · [2] Nünnerich et al., Phys. Rev. X 15, 021079 (2025) · [3] eleQtron · MAGIC uses radio-frequency control

Public-evidence system study

The scope is an engineering sidecar—not the shot-loop data plane.

University of Siegen apparatus descriptions place deterministic sequencing, detector acquisition and state discrimination inside the experiment loop. Oscilloscopes observe selected electrical nodes through probes or splitters for commissioning, validation and debugging.

Main shot loop · published Siegen lineage

01 · SchedulePython or host software compiles the experiment; a real-time sequencer owns TTL triggers and deterministic I/O.

02 · DriveAOM/EOM channels prepare and detect; AWG/DDS plus microwave conversion deliver coherent control.

03 · Fluoresce369 nm emission passes collection optics, spatial filtering and a narrow-band optical filter.

04 · AcquireEMCCD, PMT or SPAD data enters a frame grabber, counter, TDC, ADC or FPGA for classification and feedback.

Engineering sidecar · diagnostic taps

Laser lockPhotodiode, cavity transmission and error signal.
RF / microwaveIF envelope, trigger, mixer or crystal-detector output.
Clock / triggerTTL fan-out, camera-ready and AWG trigger alignment, skew and jitter.
Detector front endPulse amplitude, noise, threshold, saturation and signal integrity.

Software boundary

Python/SCPI can configure the scope, acquire waveforms and emit commissioning, calibration or monitoring artifacts. The real-time controller still owns shot scheduling, while detector interfaces and analysis software own structured photon counts, frames, timestamps and bright/dark decisions.

Not claimed: eleQtron's current oscilloscope vendor, bandwidth, sample rate, channel count or installed topology are not public.

System-study references: [4] Piltz, 2016 · EMCCD/PMT triggering and ADwin sequencing · [5] Huber, 2024 · AWG hand-off and image feedback · [6] eleQtron · Python control software and programmatic device control · [7] DLR QSea II · SPAD and control-electronics integration direction

Capture integrity

Requested window, sample depth and scope readback must agree. A plausible trace from a shortened pre-trigger record is rejected.

Shot alignment

All channels must share one record length, and the expected dark window must prove that the capture contains the intended shot phase.

Reproducible consensus

Repeated captures must agree numerically. A conflicting capture stays a disagreement; it is never averaged into a green result.

02 · Extensibility and maintainability

Scale qubits by adding contracts—not branches.

The orchestration core should not care whether control stays in one zone, crosses a shuttling boundary or reaches another module through a photonic link. Hardware-specific behavior belongs behind registries and adapters.

Core mechanisms implemented Shuttling & photonic links: architecture targets

CONTRACT SURFACE

One source of truth

Process, dataset and backend registries define what exists. A new lane cannot silently omit its timing probe, readback contract or safety gate.

ADAPTER BOUNDARY

Hardware owns its I/O

The implemented control contract keeps orchestration separate from device I/O. Spectrum M4i.66xx/DDS is modelled as an eleQtron-facing adapter target; shuttling and photonic links enter through the same seam—not an if qubits > N rewrite.

MAINTENANCE SURFACE

Agents can change it safely

Code maps identify ownership; contract tests detect registry drift; fail-closed admission blocks incomplete capability or stale runtime evidence before execution.

CURRENT CONTROL MODELSingle control regionRegistry-driven qubit, pair, PMT and calibration contracts.

EXTENSION TARGETMulti-zone shuttlingTransport plan becomes a scheduled adapter with explicit timing and hand-off evidence.

EXTENSION TARGETPhotonic module linkRemote entanglement becomes another typed execution/readout lane with heralding provenance.

Agent maintenance loop

1 · Read contractLocate registry, dataset owner and evidence boundary.
2 · Add adapterKeep device-specific I/O outside orchestration.
3 · Run guardsCatch drift, stale mappings and unsupported paths.
4 · Emit evidenceRetain provenance, limitation and terminal outcome.

03 · Timing verification system

Test the same contract through two independent realities.

A fast deterministic simulator checks logic and data contracts. An electrically live lane runs the production hardware-timed control core with real TTL/DDS envelopes and a controlled photon source—still without ions.

Verification lanes implemented

LANE A · SOFTWARE

Deterministic software simulation

Compile the exact primitive OpenQASM, execute synthetic counts and verify repeatable evidence without lab network access.

Same primitive circuit and execution contract
Deterministic replay and evidence hash
Freshness, all-dark and capability failures are injectable
Proves software behavior—not RTIO or wiring

LANE B · TTL / PHOTON EMULATION

Electrically live control path

The production control core runs on the test master; a scope observes physical 369/935 envelopes while controlled TTL photon pulses exercise the counter/readout chain.

Real FPGA-backed scheduling and deterministic timeline
Scope-visible DDS/TTL timing envelopes
Controlled photon counts through the acquisition boundary
Proves electrical/dataflow integration—not ion physics

Fail-closed evidence ladder: software-sim results cannot enter calibration; electrically live results cannot claim state preparation or gate fidelity; physical-ion verified capability remains explicitly separate.

04 · Circuit-to-photon full stack

Own every boundary from algorithm to evidence.

The system does not stop at compilation or pulse generation. It carries circuit identity through execution, photon acquisition, analysis and a claim that includes its own limitation.

OpenQASM → control contract → counts implemented Spectrum M4i.66xx AWG/DDS: integration target

The diagram distinguishes implemented software contracts from the publicly documented eleQtron microwave backend, which is presented as an integration target rather than personal hands-on evidence. Horizontal scrolling is available on narrow screens.

PUBLIC ELEQTRON QUBIT-CONTROL PATH · REF [4]

Python contract to microwave carrier.

Python / control services
↓
Spectrum M4i.66xx AWG + M4i.66xx-DDS
↓
SSB mixer + microwave LO
↓
f_μw ≈ 12.64 GHz → ¹⁷¹Yb⁺ MAGIC processor

Spectrum’s official eleQtron case study identifies the M4i.66xx series as the microwave qubit-control AWG family. The control-software and FPGA boundaries are consistent with eleQtron’s public engineering roles: Python APIs/data interfaces above hardware-specific I/O, and FPGA signal generation plus real-time feedback below.

f_kcarrier frequency φ_kcarrier phase A_kcarrier amplitude df_k/dtfrequency slope dA_k/dtamplitude slope

16 bit · up to 1.25 GS/sPCIe AWG family; one, two or four synchronous channels.

k = 1…20 carriers/outputEach carrier exposes f_k, φ_k, A_k, df_k/dt and dA_k/dt.

Δt_cmd ≈ 6.4 nsf_μw ≈ 12.64 GHz after SSB mixing; Δf_ion ≈ 3–5 MHz.

References: [4] Spectrum Instrumentation · DDS technology enables microwave ion control for quantum computing · [5] eleQtron · Senior Software Engineer—Python control software · [6] eleQtron · FPGA Engineer—Quantum Computing

Claim boundary: the public source confirms the M4i.66xx series, not an exact installed submodel. M4i.6631 appears in the article image caption only. No claim is made about eleQtron’s laser, camera, trap-DAC or laboratory-wide sequencer stack.

Selected research · application bridge

Translate application questions into executable quantum workflows.

Collaborations across quantum machine learning, generative chemistry and quantum chemistry gave me a working vocabulary on both sides of the interface: domain objectives and benchmarks on one side; circuits, simulators, hardware constraints and defensible evidence on the other. Google Scholar profile ↗

01 · ASKApplication objective & success metric

02 · MODELPhysics assumptions & algorithm choice

03 · EXECUTECircuit, backend & resource constraints

04 · EXPLAINEvidence, benchmark & limitations

QUANTUM MACHINE LEARNING · PREPRINT

Unentangled quantum reinforcement learning agents in the OpenAI Gym

First author · arXiv:2203.14348 · 2022

Application → experimentTurned standard reinforcement-learning tasks and metrics into a single-qubit variational workflow, classical post-processing and execution on real IBM quantum machines.

Read the arXiv preprint

GENERATIVE CHEMISTRY · JOURNAL

Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry

Co-author · J. Chem. Inf. Model. 63, 3307–3318 · 2023

Domain objective → hybrid modelConnected small-molecule generation goals to hybrid quantum-classical GAN components, then compared physicochemical, goal-directed and validity trade-offs.

Read the ACS paper

QUANTUM CHEMISTRY · JOURNAL

Quantum simulation of preferred tautomeric state prediction

Co-author · npj Quantum Information 9, 102 · 2023

Scientific workflow → resource constraintsMapped a drug-discovery question through active-space selection, qubit-efficient encoding and VQE while retaining benchmark and hardware-resource limits.

Publisher-stated contribution: performed noiseless and noisy quantum simulations with Yu Shee.

Read the npj paper

These publications evidence cross-domain collaboration in quantum applications. They complement—but do not replace—the separately labelled software and electrical evidence for the trapped-ion control system above.