Quantum-Clarity
Technology Overview
Three integrated technology layers: the ELSD platform for electronic regime classification, QuantaCore™ for world-record modular quantum computing, and QllMe™ for quantum-enhanced AI on consumer hardware.
- 116 qubits validated (29 OrthoTiles™)
- 85.7% average fidelity — no error mitigation
- 97% peak Y⊗Z correlation
- Linear scaling — only 0.9% degradation over 10×
- Patent pending (January 2026)
- 96.61% domain-specific accuracy
- 96% parameter reduction vs classical
- Six validated application domains
- Runs on standard RTX GPUs (6GB+ VRAM)
- Patent & trademark pending
Electronic Landscape Stability Diagnostics — HELIOS & Prometheus
Quantum-Clarity's HELIOS/Prometheus platform evaluates atoms, molecules, and materials — not only estimating ground-state solutions, but determining whether the underlying electronic model is stable, ambiguous, or too unreliable to support downstream decisions. Using a penalized variational quantum eigensolver (VQE), multi-seed ensemble sweeps, sector enforcement, and per-run energy decomposition, the platform measures whether independent optimizations converge to one coherent electronic family or disperse across competing basins. The same engine and quality standard have been applied across battery cathodes, solid-state electrolytes, cuprate superconductors, nitrogen-fixation catalysts, and biological metalloenzyme targets.
Most computational chemistry tools return an energy. ELSD returns a classification — whether the underlying electronic model is stable enough to trust, sensitive to perturbation, open-shell coherent, or too truncated to support decision-making. The commercial value is not just a number, but an audited verdict on whether the model itself is trustworthy enough to guide materials or drug-discovery decisions.
How it works — four diagnostic layers
A variational quantum eigensolver augmented with sector penalties that enforce physical electron-number and spin constraints throughout optimisation — preventing convergence to unphysical solutions.
15–35 independent random initialisations per condition, all sharing the same Hamiltonian. The statistical distribution of converged solutions — not any single run — is the diagnostic signal.
Per-run confirmation of ⟨N⟩ (particle number) and ⟨Sz⟩ (spin) eigenvalues. Results that violate sector constraints are flagged automatically and excluded from basin analysis.
Full decomposition of each run's energy into orbital contributions, dominant determinant probability, and correlation components — enabling mechanistic interpretation beyond a single total energy value.
When all independent seeds converge to the same energy basin and sector-clean electronic family, the model is reproducible and reliable. Downstream ranking, screening, and mechanism-building can proceed with confidence.
When seeds disperse across competing basins, split between sector families, or fail to converge consistently, the model is ambiguous, multi-basin, or structurally underconstrained. Optimising on top of such a model produces unreliable conclusions.
Four classification regimes — applicable across all domains
Perturbation finds nothing to split. Single basin retained across all ensemble seeds. ⟨N⟩ and ⟨Sz⟩ confirmed throughout. The model is reliable enough to support downstream decisions — ligand screening, dopant selection, or synthesis.
Multi-reference character present but well-structured. The ensemble converges within a single sector-clean electronic family. Wider dispersion than Rigid Stability, but internally consistent. Usable with appropriate care.
Two or more distinct electronic basins coexist under the same scaffold. Seeds disperse across competing configurations. Results depend on starting conditions and should not be used for ranking or mechanism-building without explicit landscape diagnosis.
The active space or scaffold is too truncated or underconstrained to produce reliable results. Ensemble seeds diverge in ways that reflect model failure rather than genuine physical electronic structure. Not safe to optimise against.
Energy scatter across all seeds — the primary ruggedness signal
Hartree–Fock weight in the ground state — multi-reference indicator
Basin count, inter-basin gap (kcal/mol), and trapped seed fraction
Per-run particle number verification — sector integrity check
Per-run spin eigenvalue verification — prevents unphysical solutions
Rigid Stability / Coherent Open-Shell / Multi-Basin / Model Pathology
Domains where ELSD has been applied
The ELSD platform sits upstream of ranking, screening, and mechanism-building workflows. Before teams spend time and money optimising compounds or materials, it determines whether the electronic model they are using is actually trustworthy enough to support those decisions. Most computational tools rank candidates as if the target picture were already settled. ELSD works one layer earlier — on whether the target-state model itself is decision-grade. That removes a category of failure that no existing tool addresses: false mechanistic commitment built on the wrong electronic basin.
Modular Quantum Computing — World Record
Operator-aligned basis migration creates independent quantum modules that scale linearly instead of degrading exponentially. Validated on IBM Quantum hardware at a scale 9.7× larger than any previous modular MBQC demonstration.
Measurement-Based Quantum Advantage
Validated
No Mitigation
Correlation
Modules
Validated January 2, 2026 on IBM ibm_fez (156-qubit Heron R2) | 9.7× larger than previous MBQC demonstrations | Patent Pending (USPTO)
Linear Scaling Demonstrated
Same architecture, 10× the scale — with negligible fidelity loss
increase
Only 0.9% fidelity degradation over 10× scale increase — demonstrating robust modular independence and a clear pathway to 1,000+ qubits.
Core technology stack
Patent-pending deterministic circuit that relocates quantum information from the computational (Z) basis into symmetry-protected Y⊗Z orthogonal manifolds, creating independent error channels.
Independent 4-qubit building blocks with isolated error channels. Each module operates in its own error space, preventing cascading failures across the system.
Real-time verification framework producing operator-level manifold integrity metrics. Enables instant accept/reject decisions without exponentially costly quantum state tomography.
Information encoded in the Y⊗Z manifold is orthogonal to Z-basis noise — the dominant error channel on NISQ hardware. Mathematical orthogonality Y·Z = 0 creates independent error channels: Z-dephasing does not corrupt Y⊗Z correlations.
Measured 95.3% Z-orthogonality success (⟨Z⟩ ≈ 0) confirms information successfully migrated out of the computational basis.
Traditional quantum computing encodes information along the Z-axis (north–south pole of the Bloch sphere). Z-noise directly corrupts this encoding.
Y⊗Z approach: Information resides perpendicular to the Z-axis, in the equatorial Y⊗Z plane. Z-noise rotates around the Z-axis but does not project onto the orthogonal Y⊗Z subspace — first-order immunity to the dominant error channel.
🔮 Breakthrough Discovery: [[4,0,d]] Stabilizer State
Our research discovered that 4-qubit modules prepared via basis migration occupy a unique eigenspace characterised by 16 independent stabilisers with perfect Y⊗Z correlations and Z-orthogonality.
This creates a [[4,0,d]] resource state (4 physical qubits, 0 logical qubits encoded, distance d protection) optimised specifically for measurement-based quantum computing rather than direct information storage.
Measurement-based quantum computing applications
FeMoco nitrogen fixation simulations using modular MBQC protocols. Target: 400–1,000 qubits via 100–250 OrthoTiles™
Variational algorithms via measurement-based execution, bypassing cumulative gate errors through modular resource consumption
High-fidelity quantum state transfer using OrthoTiles™ as entanglement channels with Z-orthogonal protection
Distributed Y⊗Z entanglement for multi-party quantum computation and quantum key distribution protocols
Multi-platform hardware compatibility
(156-qubit processor)
qubit systems
processors
systems
GPU-accelerated simulation platform
| Hardware platform | Scale achieved | Performance | Status |
|---|---|---|---|
| IBM Quantum (ibm_fez)Heron R2 — 156 qubits | 116 qubits 29 OrthoTiles™ |
85.7% avg fidelity 97% peak · 96.6% success rate |
✓ Validated World Record — Jan 2026 |
| GPU-Accelerated Simulation & Development Platform | |||
| Consumer GPU (RTX 3060)Development & validation | 12q exact 16+ sampling |
~2 min protocol validation Full statevector |
Production |
| RTX 4090High-performance development | 16q exact · 20+ sampling | ~20–30 sec 4–6× speedup |
Projected |
| RTX 5090Next-gen platform | 20q exact · 24+ sampling | ~12–18 sec 6–10× speedup |
Projected |
| NVIDIA A100Enterprise development | 24q exact · 28+ sampling | ~8–12 sec 10–15× speedup |
Projected |
| NVIDIA H100Advanced R&D platform | 28q+ exact · 32+ sampling | ~4–6 sec 25–30× speedup |
Projected |
- 116 qubits on IBM Quantum (world record)
- 85.7% average manifold integrity
- 97% peak Y⊗Z correlation
- 95.3% Z-orthogonality success
- 89.1% average Y⊗Z correlation
- First-order noise immunity confirmed
- 12-qubit exact statevector (validated)
- 16+ qubit sampling methods
- ~2 min protocol validation time
- 10–100× speedup vs CPU-only
- GPU simulation → IBM QPU validation
- Module pre-screening before deployment
- IBM Quantum — Heron R2, 156 qubits
- Qiskit 1.0+ with EstimatorV2
- Topology-optimised qubit layouts
- Qiskit + CuPy GPU acceleration
- Python 3.11 · CUDA-enabled
- 4-qubit Y⊗Z modules → N×4 scale
What makes QuantaCore™ unique
Independent OrthoTiles™ prevent cascading failures. When one module underperforms, others remain unaffected — a fundamental departure from monolithic quantum circuits.
Patent-pending technology relocates quantum information into orthogonal manifolds, providing passive first-order noise immunity without active error correction overhead.
EigenSpectrum™ Analyzer provides O(n) verification vs O(2n) tomography, enabling instant quality assessment at scale without exponential resource cost.
Only 0.9% fidelity degradation over a 10× scale increase proves the architecture maintains quality independently of system size — a pathway to 1,000+ qubits.
Quantum-Enhanced AI — Consumer Hardware
Revolutionary architecture where quantum circuits replace classical weight matrices, delivering genuine quantum advantages on accessible GPU hardware. Validated across six application domains with 96.61% accuracy using 96% fewer parameters.
vs 70–85% traditional
vs 100M+ traditional
superior results maintained
vs classical equivalents
Six-domain quantum intelligence
Portfolio optimisation & risk analysis using quantum algorithms for superior correlation modelling
20-qubit molecular simulations enabling breakthrough protein structure prediction
Quantum chemistry calculations accelerating drug-target interaction modelling
Quantum pattern recognition for advanced anomaly detection and financial security
Genomic analysis acceleration through quantum algorithms for genetic pattern recognition
Quantum simulations for novel material discovery and property prediction
Shared Quantum Technologies
Both platforms leverage Quantum-Clarity's comprehensive quantum computing research foundation — proprietary techniques developed across hardware validation, AI deployment, and electronic-regime classification.
Enables rapid customisation for specialised applications while maintaining quantum advantages across different domains. Reduces fine-tuning overhead without sacrificing quantum processing capability.
Advanced adaptation techniques allowing domain-specific optimisation without losing core quantum processing capabilities. Adapts the quantum layer efficiently for new target domains.
Proprietary optimisation combining quantum circuit training with classical machine learning for superior convergence. Underpins QuantaCore™ protocol development, QllMe™ training, and ELSD VQE campaigns.
10–100× speedup vs CPU-only simulation, enabling rapid protocol validation and development iteration. Core infrastructure shared across QuantaCore™, QllMe™, and ELSD ensemble campaigns.
Technical Specifications
Side-by-side comparison of hardware foundations, software stacks, and validation status across both platforms.
| Component | QuantaCore™ Platform | QllMe™ Engine |
|---|---|---|
| Hardware foundation | IBM Quantum (Heron R2, 156 qubits)Rigetti, IonQ (roadmap) | NVIDIA RTX Series GPU6GB+ VRAM, CUDA enabled |
| Software stack | Qiskit 1.0+, EstimatorV2Custom topology optimisation | TensorFlow Quantum 0.7.3TensorFlow 2.13.0 + CUDA 11.8 |
| Quantum scale | 116 qubits validatedPathway to 1,000+ qubits | 6–31 qubit simulationsScalable with GPU memory |
| Processing speed | ~2.5 min for 116 qubits~1.5 sec per module | Sub-second inferenceReal-time AI processing |
| Key innovation | Basis migration (patent pending)Y⊗Z orthogonal manifolds | Quantum weight matricesVariational quantum circuits |
| Validation status | World record (Jan 2026)Peer review in progress | Production-readySix domains validated |
A new paradigm for scalable quantum computing
From sequential gate operations to parallel modular preparation.
From exponential error accumulation to isolated error channels.
From fragile global states to robust independent modules.
Patent Pending (U.S. Provisional Filed January 2026) | QuantaCore™ (USPTO Serial No. 99575735), OrthoTiles™, EigenSpectrum™, QllMe™, PyTran™, Cyber Circuit™ are trademarks of Quantum-Clarity LLC