From prototype to fault-tolerant scale

What we'd propose

• Enterprise AI

  Data platform for vacuum chambers, optical arrays and gate logs

  An ontology-based data backbone that ingests vacuum-chamber sensor output, optical-tweezer state and quantum-gate logs against one shared model, so that engineering, calibration and manufacturing teams query the same data instead of reconciling separate extracts.

  • Shared data model for quantum experiments

    One agreed vocabulary

    Define vacuum-chamber state, optical-tweezer configuration, gate operation and run as explicit entities with agreed relationships, so a query written once returns the same answer whether it comes from an engineer, a calibration scientist or a manufacturing operator.

  • Ingestion from sensors and control systems

    Data captured at the source

    Build pipelines from FPGA control boards, optical sensors and chamber diagnostics so each measurement reaches the platform with its instrument identity, calibration version and timestamp attached, instead of being read off a screen and re-typed downstream.

  • Analytics and retrieval on top of the model

    Answers without new extracts

    Expose the model through dashboards and a retrieval layer so scientific, engineering and customer-facing teams can ask questions of the combined experimental record without commissioning a new extract for every report.

  • Calibration, engineering and manufacturing work from the same underlying record instead of three reconciled copies.
  • Digital-twin simulation of vacuum-chamber fluid dynamics draws on real instrument data rather than on assumed values.
  • New instrument types attach to the model instead of triggering another migration.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Digital Transformation of QC Labs: From Paper-Based Legacy to Integrated LES

  See how we do Enterprise AI
• Digital CDMO

  Autonomous calibration agent for optical tweezers and gates

  A control agent that automates the routine calibration of optical tweezers and two-qubit gate phases across the system, with AI-driven diagnosis when a calibration step fails, so the hardware stays at peak without manual intervention.

  • Calibration workflow automation

    Push-button calibration

    Replace the manual step-by-step calibration routine with an automated sequence that runs the same checks every time and records each result against the instrument it came from, so a new operator can run a full calibration without holding the underlying physics in their head.

  • AI-driven failure diagnosis

    What failed and why

    When an experiment fails, the agent reads the calibration history and the current run log, identifies which parameter drifted, and retries with optimised values, so the engineering team gets a diagnosis rather than a stack of unsuccessful runs.

  • Continuous performance baseline

    Hardware stays at peak

    Hold the system against a documented performance envelope so drift shows up as a calibration alert rather than as a sudden drop in experiment success, which keeps the share of system time spent actually running experiments on a known upward trajectory.

  • Researcher time moves from routine calibration to science.
  • Calibration records are generated by the system and are reproducible across shifts and operators.
  • Performance drift is caught early instead of after the next set of failed runs.

  We've done this before

  • Fully Automated Autosampling Control System
  • Scaling Biotech Manufacturing via Universal IT/OT Architecture

  See how we do Digital CDMO
• Digital CDMO

  Hybrid quantum-classical workflow orchestration

  An orchestration layer that connects the QPU and the classical HPC cluster in one workflow, with sub-millisecond feedback loops for error correction and conditional branching, so quantum jobs no longer wait on a scheduler built for batch processing.

  • Persistent job identifiers across QPU and HPC

    One job, one identity

    Extend the scheduler so a hybrid quantum-classical job keeps a single identity across thousands of small conditional branches, with the QPU and HPC resources booked against that identity rather than against separate batch slots.

  • Low-latency feedback path

    Sub-millisecond return path

    Build a feedback path between the QPU and the HPC control plane that returns mid-circuit results inside the latency budget error correction needs, so the QPU is not left waiting on the classical side.

  • Workload patterns for chemistry and energy applications

    Templates for known algorithms

    Document workload patterns for nuclear-dynamics, materials-simulation and power-grid-optimisation use cases, with the orchestration steps named so a new algorithm can adopt a known pattern rather than re-designing the scheduling each time.

  • Hybrid quantum-classical algorithms run end-to-end instead of stalling at the orchestration boundary.
  • The expensive QPU spends more of its time on quantum work rather than waiting on the classical scheduler.
  • Workload templates turn a one-off integration into a reusable pattern for the next use case.

  We've done this before

  • Scaling Biotech Manufacturing via Universal IT/OT Architecture
  • Strategic Integration Guides for Pharmaceutical Control Systems

  See how we do Digital CDMO
• Digital CDMO

  Digital twin of vacuum chambers and optical elevator geometry

  A digital twin of the vacuum chamber and the optical-elevator geometry that simulates fluid dynamics and laser-matter interaction in-silico, so design changes are validated before they reach physical hardware.

  • Geometry and physics model

    Chamber geometry as a model

    Build a physics model of the vacuum chamber, the optical elevator and the tweezer-array geometry from the engineering drawings, so design proposals can be tested against the same model that production will eventually use.

  • What-if simulation

    Design choices tested in-silico

    Run what-if simulations for new optical layouts, atom-loading sequences and laser-power budgets so a design candidate can be compared on a documented basis before a physical build is committed.

  • Comparison against measured chamber behaviour

    Twin validated against reality

    Feed measured chamber behaviour back into the twin so the simulation tracks what the actual hardware is doing, which makes the twin a planning tool for the next generation rather than a one-off design exercise.

  • Design iterations happen in simulation, which shortens the path from concept to manufacturable hardware.
  • Fluid-dynamics and laser-matter effects become visible before physical builds, which reduces expensive late-stage rework.
  • The same twin supports engineering, manufacturing and customer conversations about system behaviour.

  We've done this before

  • Creating a Digital Twin to Simulate Biopharma Processes
  • Transforming Biotech Manufacturing with VR/AR

  See how we do Digital CDMO
• Agents

  AI agents for quantum-computing documentation work

  Narrow, reviewable agents that take the repetitive part of quantum-computing documentation: drafting the first version of a peer-reviewed paper, customer proof-of-concept report or NIST post-quantum cryptography migration package from source experiment records, checking each draft against its template before human review, and searching the controlled document set to find every artifact a given standards change touches. A named person approves every output.

  • Drafting from experimental records

    First drafts from run data

    Generate the first draft of a peer-reviewed paper, customer proof-of-concept summary or error-analysis report directly from the run log and the calibration record, so the author edits and judges rather than assembles the document from scratch.

  • Template and completeness checking

    Gaps found before review

    Check each draft against its target template and the project's own checklist, returning missing or inconsistent sections before the document enters the human review queue, which keeps review time on judgement rather than on completeness checking.

  • Change-impact search across the document set

    Which documents a change touches

    When a NIST post-quantum standard, an ITAR/EAR rule or a customer security requirement changes, retrieve every controlled document and report that references it and rank them by how directly they are affected, so the update scope is known on day one.

  • Review queues move faster because documents arrive complete and already cite their underlying runs.
  • The scope of a standards change is established by search rather than by recollection across the team.
  • Every output is traceable to the source records it came from and signed off by a named reviewer.

  We've done this before

  • Process Transfer Reporting System

  See how we do Agents