Development

Software we write, and your team runs.

Everything on this page ends with working code in your environment, documentation your engineers can follow, and a handover. If a problem is better solved without new software, we say so before the contract starts.

  • Quantum & hybrid
  • Cryptography
  • Enterprise systems
  • Data & AI
  • HPC

D—01

Quantum software engineering

Qiskit · PennyLane · CUDA-Q · Cirq. Backend-agnostic, so hardware choices stay open.

We build the algorithm, the encoding and the classical scaffolding around it, then benchmark the result against the solver you run today.

Algorithms

  • QAOA and VQE implementations
  • Application-specific ansatz design
  • Grover and amplitude estimation routines
  • Quantum simulation for chemistry and materials

Optimization

  • QUBO and Ising problem encoding
  • Constraint handling and penalty tuning
  • Annealing and digital-annealer integration
  • Solver benchmarking harnesses

Machine learning

  • Quantum kernels and QSVC
  • Variational classifiers
  • Feature map and encoding design
  • Barren-plateau and trainability analysis

Circuit engineering

  • Synthesis and depth reduction
  • Transpilation for device topology
  • Error mitigation and readout correction
  • Noise characterisation

D—02

Post-quantum cryptographic implementation

FIPS 203 / 204 / 205. Hybrid modes first, so nothing depends on a single algorithm surviving.

Migration work, written into your systems rather than described in a report. The assessment that precedes it lives under Services.

Primitives

  • ML-KEM key encapsulation
  • ML-DSA and SLH-DSA signing
  • Hybrid X25519MLKEM768 key exchange
  • HQC as a backup KEM family

Integration

  • TLS termination and protocol upgrades
  • PKI, certificate lifecycle and signing services
  • HSM and key management integration
  • Crypto-agility abstraction layers

Quantum-native

  • QKD link integration alongside PQC
  • QRNG entropy sources and health tests
  • SP 800-90B validation support
  • Key distribution architecture

Legacy

  • Embedded and OT constrained devices
  • Protocol shims where endpoints cannot change
  • Staged rollout with rollback paths
  • Interoperability testing

D—03

Enterprise application development

The systems that carry the rest of it. Built secure by default rather than hardened afterwards.

Web, desktop and mobile software, and the integration work that connects it to whatever you already run.

Applications

  • Web platforms and internal tooling
  • Desktop and cross-platform clients
  • iOS and Android applications
  • Dashboards and decision-support interfaces

Business systems

  • ERP implementation and customisation
  • CRM and workflow automation
  • Reporting and operational analytics
  • Document and records systems

Integration

  • Secure API and protocol design
  • Legacy modernisation and migration
  • Message queues and event pipelines
  • Identity, SSO and access control

Operations

  • CI/CD pipelines and release automation
  • Infrastructure as code
  • Observability and logging
  • Network management tooling

D—04

Data engineering and applied AI

Classical models where classical models win. Most of the time they do.

Pipelines, models and the governance around them. This is also where quantum machine learning gets its baseline, since a QML result means nothing without one.

Platform

  • Data pipeline and warehouse architecture
  • Streaming and batch ingestion
  • Feature stores and versioning
  • Data quality and lineage

Models

  • Forecasting and demand modelling
  • Anomaly and fraud detection
  • Classification and ranking systems
  • Optimisation-backed decision tooling

Delivery

  • Model deployment and serving
  • Monitoring and drift detection
  • Retraining pipelines
  • Model governance and audit trails

Language models

  • Retrieval systems over internal documents
  • Agent and workflow integration
  • Evaluation harnesses
  • Data residency and privacy controls

D—05

HPC and hybrid infrastructure

The classical half of a variational loop dominates wall-clock time. It deserves engineering.

Simulation, orchestration and scheduling for workloads that span quantum backends, GPU clusters and cloud.

Simulation

  • GPU state-vector simulation
  • Tensor-network methods for wide circuits
  • Noise-model simulation
  • cuQuantum and CUDA-Q integration

Scale

  • MPI and multi-node distribution
  • SLURM scheduling and job orchestration
  • Parallel variational loops
  • Cost, latency and throughput modelling

Orchestration

  • Backend-agnostic execution layers
  • Queue management across providers
  • Result caching and replay
  • Failure handling and retry logic

Classical acceleration

  • Solver profiling and tuning
  • GPU-accelerated heuristics
  • Physics-inspired and digital annealing
  • Honest comparison against the quantum route

§ How we deliver

01

Scoped in writing

Success criteria and a classical baseline agreed before anyone writes code. We state what we expect to fail.

02

Your repo

Code lands in your version control from the first commit. No black boxes, no handover cliff at the end.

03

Paired

Your engineers work alongside ours. The capability stays after we leave, which is the point.

04

Benchmarked

Every quantum claim carries a classical comparison. If the classical route wins, we tell you and you keep it.

Get in touch

Bring us the problem, not the technology choice.

Describe what your system has to do and where it currently falls short. We will tell you whether quantum, hybrid or plain classical engineering is the answer.

WhatsApp +974 3381 3027 hello@qryptonics.com

Doha, Qatar