CTO Pitch • Architecture Vision 300 TB Scale

Quantum-Augmented Analytics Platform
From Big Data to Deep Patterns

A production-grade architecture that combines classical big data infrastructure with quantum hardware to unlock non-trivial patterns, optimization, and anomaly detection across ~300 TB of enterprise data.

Classical systems retain responsibility for volume and throughput. Quantum is introduced as a controlled, high-impact augmentation for complexity, not as a replacement.

View Solution Overview Jump to Architecture
Executive Snapshot
Strategic Objective
Establish a hybrid analytics foundation that:
  • Operates at 300 TB scale using proven data infrastructure.
  • Uses quantum hardware selectively for high-complexity pattern extraction.
  • Preserves vendor optionality and minimizes lock-in.
Data Footprint
≈ 300 TB
Pattern Focus
Clusters • Anomalies • Optimization
// Design stance
classical("scale") + quantum("complexity") →
    enterprise_pattern_superpowers();
Context
The Problem We Are Actually Solving

The organization is already capable of storing and querying large volumes of data. The bottleneck has shifted from raw storage and basic analytics to identifying non-obvious, high-value patterns in massive, multi-dimensional datasets.

  • Multiple data domains (telemetry, transactions, journeys, logs).
  • High event velocity and historical depth (300 TB+).
  • Standard BI and ML capture only surface-level relationships.

Key challenges:

  • Combinatorial explosion in search and optimization tasks.
  • Subtle anomalies hidden within billions of “normal” events.
  • Non-linear, high-dimensional patterns across entities and time.
Why Quantum Now
Strategic Motivation (CTO Lens)

Quantum hardware is not yet a general-purpose compute replacement. However, it is increasingly effective at specific classes of high-complexity problems:

Optimization on large graphs Quantum kernels for complex manifolds Variational models for anomalies Many-body-style structure discovery

The goal is to integrate these capabilities in a way that:

  • Does not disrupt existing big data investments.
  • Provides a clear path from PoC to production.
  • Maintains optionality across quantum vendors and hardware roadmaps.
Solution Overview
Quantum-Augmented Analytics Platform

A layered platform where:

  • Classical infrastructure ingests, stores, and aggregates ~300 TB of data.
  • Feature and embedding layers compress raw events into dense semantic vectors.
  • A “Quantum Pattern Lab” consumes representative subsets of these vectors.
  • Quantum algorithms run in tightly scoped workflows to enhance clustering, anomaly detection, and combinatorial optimization.

Quantum outputs are not exposed directly. They are translated into:

  • Model parameters and decision boundaries.
  • Segments, scores, and optimization configurations.
  • Incremental uplift over strong classical baselines.
Key Design Principles
What Makes This Executable, Not Experimental
1. Classical-first for volume

All heavy IO, transformations, and broad analytics stay on proven big data stacks (Spark/Flink/Trino/BigQuery on Parquet/ORC).

2. Quantum as a controlled sidecar

Quantum is introduced only in targeted workflows via a dedicated sidecar lab, behind a gateway abstraction.

3. Vendor-agnostic integration

The Quantum Gateway isolates the rest of the architecture from specific hardware vendors or modalities (neutral-atom today, others later).

4. Measurable uplift vs baselines

Every quantum-assisted workflow has a classical baseline, and uplift is measured in precision/recall, revenue impact, cost savings, or risk reduction.

// Platform invariant
if (!quantum_available) {
  run_classical_baseline();
} else {
  baseline = run_classical_baseline();
  uplift = run_quantum_augmented(baseline);
  ship_if(uplift > threshold);
}
Target Architecture
Component View: End-to-End Flow

The following view illustrates the flow from raw events to deployed quantum-augmented patterns, with clear boundaries between classical and quantum responsibilities.

Architecture Diagram
flowchart TD %% -------------------- %% INGEST → FEATURES %% -------------------- A[Ingestion & Raw Storage] --> B[Feature Engineering Layer] subgraph Ingestion A1[Kafka / Pulsar / Kinesis] A2[Parquet / ORC Storage] A3[Iceberg / Delta Catalog] A1 --> A2 --> A3 --> A end subgraph Feature_Engineering B1[Spark / Flink / Trino] B2[Feature Store] B3[Embeddings Layer] B1 --> B2 --> B3 --> B end %% -------------------- %% CLASSICAL ANALYTICS %% -------------------- B --> C[Classical Pattern Analytics] subgraph Classical_Analytics C1[Clustering] C2[PCA / UMAP] C3[Isolation Forest] C4[One-Class SVM] C1 --> C2 --> C3 --> C4 --> C end %% -------------------- %% QUANTUM LAB %% -------------------- B --> D[Quantum Pattern Lab] subgraph Quantum_Lab D1[Sampler Service] D2[Quantum Gateway] D3[Hybrid Engine] D4[Result Reducer] D1 --> D2 --> D3 --> D4 --> D end %% -------------------- %% DEPLOYMENT %% -------------------- C --> E[Pattern Deployment] D --> E subgraph Deployment E1[MLflow Registry] E2[REST / gRPC APIs] E3[Batch & Streaming] E1 --> E2 --> E3 --> E end %% -------------------- %% CONSUMPTION %% -------------------- E --> F[Consumption & Feedback] subgraph Consumption F1[Dashboards] F2[Fraud / Routing / CX / Ops] F3[Feedback Loop] F1 --> F2 --> F3 --> F end %% -------------------- %% GOVERNANCE %% -------------------- G[Governance Layer] G --- A G --- B G --- D G --- E subgraph Governance G1[IAM / RBAC] G2[Cost Controls] G3[Audit & Lineage] G1 --> G2 --> G3 --> G end
Layers 1–3: Data, Features & Baselines
1. Ingestion & Raw Storage
  • Streaming via Kafka/Pulsar/Kinesis.
  • Batch via scheduled ETL/ELT jobs.
  • Storage: Parquet/ORC on object store (S3/GCS/Blob/HDFS).
  • Catalog: Iceberg/Delta/Hive for schema and partitions.
2. Classical Analytics & Feature Engineering
  • Transformation engines: Spark/Flink/Trino/BigQuery.
  • Feature Store to manage and serve entity-level features.
  • Embedding pipelines using Autoencoders, GNNs, and sequence models.
3. Classical Pattern Analytics
  • Clustering, dimensionality reduction, anomaly detection.
  • Establishes a strong baseline for pattern discovery.
  • Every quantum experiment compares against this baseline numerically.
Layers 4–6: Quantum Lab, Serving & Feedback
4. Quantum Pattern Lab
  • Sampler Service selects representative embedding subsets.
  • Quantum Gateway abstracts hardware vendors and modalities.
  • Hybrid Engine runs QAOA, VQE, QSVM, and quantum kernels.
  • Result Reducer converts bitstrings into segments, scores, and optimization recommendations.
5. Pattern Deployment & Serving
  • Model Registry tracks classical and quantum-augmented models.
  • REST/gRPC services score entities, routes, transactions in real time.
  • Batch and streaming pipelines apply patterns to the full 300 TB footprint.
6. Consumption & Feedback
  • Dashboards and notebooks for technical and business stakeholders.
  • Feedback loops that continuously refine embeddings and models.
  • Platform evolves as data, workloads, and hardware improve.
Value Proposition
Where Quantum Adds Material Value

Quantum is not used for generic SQL-style analytics. It is focused on areas where classical approaches hit complexity walls: combinatorics, high-dimensional structure, and subtle boundary definitions in feature space.

1. Optimization at Realistic Scale

Use QAOA-style workflows to address:

  • Routing and scheduling across large graphs.
  • Feature subset selection for ML and risk models.
  • Configuration optimization (capacity, allocation, portfolios).
2. Clusters & Anomalies in Complex Manifolds

Quantum kernels and variational models can:

  • Capture non-linear relationships not easily modeled classically.
  • Enhance anomaly detection in dense embedding spaces.
  • Refine segments beyond traditional clustering approaches.
3. Strategic Capability & Optionality

Beyond immediate uplift, the platform:

  • Builds internal capability for quantum-era analytics.
  • Allows safe, governed experimentation with emerging hardware.
  • Positions the organization ahead of competitive adoption curves.
Execution Plan
Roadmap: From PoC to Production

The roadmap is intentionally incremental: each phase creates tangible value on classical infrastructure while adding quantum components in a controlled, measurable manner.

Phase 1 – Foundation (0–3 months)
  • Confirm data lake structure, catalog, and feature store strategy.
  • Implement initial embedding pipeline for one or two domains.
  • Establish baseline classical clustering and anomaly models.
Phase 2 – Quantum Lab Pilot (3–6 months)
  • Stand up Quantum Gateway with a selected hardware vendor.
  • Implement Sampler Service and a simple Hybrid Engine workflow.
  • Run side-by-side experiments on a narrow, high-value use case (e.g., fraud, routing, or segmentation).
Phase 3 – Productionization (6–12 months)
  • Promote successful quantum-augmented models into the model registry.
  • Integrate with real-time scoring services and batch pipelines.
  • Expand to two additional domains with proven uplift.
Phase 4 – Scale & Governance (12+ months)
  • Formalize quantum capability as part of data/ML platform strategy.
  • Support multiple quantum vendors for redundancy and leverage.
  • Standardize quantum experimentation playbooks for internal teams.
CTO-Level Decision Inputs

To move forward, the following decisions are typically required:

  • Selection of a primary quantum hardware / cloud partner for Phase 2.
  • Identification of 1–2 “hero” use cases with high signal-to-noise in outcomes (fraud, routing, risk, or personalization are common candidates).
  • Agreement on KPIs used to define “quantum uplift worth deploying”.
  • Assignment of an accountable owner (Head of Data/ML or equivalent) for the Quantum Pattern Lab as a product, not a one-off research project.
Risk, Governance & Economics
Managing Risk While Capturing Strategic Upside

The architecture is explicitly designed to contain technology risk, cost risk, and organizational risk by isolating quantum workloads and ensuring graceful fallback to classical-only execution.

Technical & Delivery Risk
  • Quantum remains sidecar, not core dependency.
  • All pipelines run in classical-only mode if required.
  • Vendor abstraction allows hardware changes without full redesign.
Governance & Access Control
  • IAM/RBAC restricts who can launch quantum workloads.
  • Cost guardrails and budgets on quantum job submission.
  • Full audit trail for models derived from quantum experiments.
Economics & ROI Framing
  • Target specific business metrics (fraud loss, route efficiency, conversion uplift) per use case.
  • Track incremental gains against classical-only baselines.
  • Treat Phase 2 as a controlled options bet on strategic capability, not as capex-heavy infrastructure replacement.