QUANTUM COMPUTING
Quantum Advantage at Scale
From quantum-classical hybrid optimisation to post-quantum cryptographic readiness
Our quantum programme spans two interlocking lines: offensive advantage and defensive readiness. On the offensive side, HyQCOpt and QuantumMetaML target non-convex search problems where classical methods exhaust budgets before convergence. On the defensive side, Quantum Bridge provides crypto-agile migration tooling aligned to NIST FIPS 203/204/205 and CNSA 2.0 timelines. Both lines are senior-led, peer-reviewed, and production-oriented.
Our methodology
We benchmark against classical baselines before every quantum investment. If the advantage doesn't materialise on representative data, we document why and move on. Research is published; production code is deployed.
Research pillars
How we work in quantum.
Hybrid optimisation
HyQCOpt maps enterprise combinatorial objectives to QUBO/Ising formulations, benchmarked against genetic algorithms on real constraint sets before any production commitment.
Quantum meta-learning
QuantumMetaML extends architecture search into regimes where classical AutoML stalls — validated on Predicta pipelines with published convergence comparisons.
Post-quantum cryptography
Quantum Bridge delivers cryptographic inventory, HNDL risk mapping, and phased PQC migration aligned to CNSA 2.0 — assessment-led, not rip-and-replace.
Capabilities
What we deliver.
- Hybrid quantum-classical optimisation (QUBO/Ising)
- Quantum architecture search & meta-learning
- Cryptographic inventory & HNDL risk assessment
- NIST FIPS 203/204/205 migration roadmaps
- CNSA 2.0 compliance planning
- Quantum error mitigation research
- Quantum-classical scheduling & orchestration
Portfolio · Quantum
Projects in this sector.
Quantum Bridge
Post-quantum cryptography without waiting for a crisis
Crypto-agile migration tooling aligned to NIST FIPS 203/204/205 and CNSA 2.0. Cryptographic inventory, HNDL risk assessment, and phased migration roadmaps. Defends long-lived secrets against harvest-now-decrypt-later before adversaries catch up.
View projectHyQCOpt
Hybrid quantum-classical optimisation engine
Maps enterprise combinatorial objectives to QUBO and Ising formulations. 25–40% faster convergence vs genetic algorithms on representative benchmark sets. Benchmarked rigorously before any production commitment.
View projectQuantumMetaML
Quantum-accelerated meta-learning
Extends neural architecture search into regimes where classical AutoML exhausts search budgets. Integrated with Predicta pipelines. Published convergence comparisons against classical baselines.
View projectMolecular Dynamics Research
Quantum-accelerated conformational sampling
Applying hybrid quantum-classical methods to molecular dynamics at scales classical MD cannot reach efficiently. Early-stage research in partnership with computational biology collaborators. Methods are being prepared for publication.
View projectWorking on a problem in quantum?
We engage selectively. Tell us what you're solving and we'll respond with a clear assessment — not a generic nurture sequence.