Solution / Quantum & PQC

Quantum Chemistry & Materials Simulation

Applied quantum-classical workflows for molecular, material and chemistry research with reproducible scientific pipelines.

Business context

What this capability solves

Quantum Chemistry & Materials Simulation converts fragmented operational signals into an accountable digital workflow so teams can detect risk earlier, optimize resources and improve safety or productivity without losing human operational control.

EBP Integra delivery principle

Technology is implemented as an operating capability: architecture, integration, governance, assurance, people, procedures and measurable outcomes are designed together.

Deep-dive capabilities

Capability model

Modular building blocks allow the scope to start with a focused pilot and expand into an enterprise operating model.

Molecular Model

Prepare structures, basis/model and scientific assumptions.

Problem Reduction

Select active space/model appropriate to available resources.

Quantum Algorithms

Run supported chemistry algorithms and hybrid solvers.

Classical Integration

Use classical chemistry/HPC tools around the quantum step.

Result Analysis

Compare energy/properties and uncertainty.

Reproducibility

Track workflow versions, inputs and experimental conditions.

Reference architecture

How the capability fits together

Final topology, control placement and deployment model are validated during discovery and detailed design.

Field / Asset Layer

Vehicles, equipment, sensors, cameras, tags, infrastructure and operational systems.

Connectivity & Edge

GNSS, industrial wireless/wired networks, gateways and local processing.

Analytics Platform

Telemetry normalization, event fusion, AI/computer vision, scoring and rules.

Operations Layer

Dispatch, command center, workflow, alerts, enterprise integration and management reporting.

Controls & governance

Authorized and purpose-bound data collection
Safety-first operational boundaries and human override
Role-based access and asset ownership
Data-quality and calibration controls
Event/audit history and investigation evidence
Cybersecurity for devices, edge and enterprise integration

Priority use cases

Materials research
Catalyst screening
Pharma research
Energy chemistry

Key deliverables

Operational/process assessment
Reference architecture
Pilot design/configuration
Integration and data map
SOP / escalation model
KPI baseline and scale roadmap

Integration considerations

Chemistry software
HPC/cloud
Quantum backend
Data repository
Research governance
Notebook/dev

Implementation

Phased delivery

Each phase ends with evidence, acceptance criteria and a decision gate before broader scale-out.

1. BaselineMap assets, routes/process, data, risk and current KPIs.

2. InstrumentConnect selected devices/data and validate data quality.

3. OperationalizeConfigure analytics, alerts, workflows and dashboards.

4. ScaleExpand fleet/site coverage, tune models and institutionalize performance management.

Outcome and KPI framework

Experiment reproducibilityTime to resultScientific validationWorkflow reuse