The world's first Quantum AI solution engine.
Skip the training. Upload your problem and Kepler Solve fuses IBM's 156-qubit Heron r2 processor with neural intelligence to return classifications and optimization plans — in seconds.
33 million dimensions of quantum reasoning.
Every Kepler Q-Max inference operates inside a 2²⁵ Hilbert space — an exponentially richer feature manifold than any classical model of comparable size.
Qubits
25
logical
Hilbert dim
33,554,432
2²⁵ states
Superposition
Full
amplitude encoded
Exponential capacity
Each added qubit doubles the representable state space.
Entangled features
Correlations no classical kernel can express natively.
Amplitude richness
Continuous complex amplitudes — not just bits.
Six pre-trained quantum solutions, ready to call.
No models to train. No quantum expertise required. Just upload your data and get back a verified, actionable result.
- Drug discovery
- Compound triage
- Bioassay prediction
- Payments
- Insurance claims
- AML screening
- Credit scoring
- Lending
- Underwriting
- Last-mile
- Fleet ops
- Field service
- Manufacturing
- Cloud workloads
- Workforce shifts
- Asset allocation
- Index rebalancing
- Wealth advisory
Powered by IBM Heron R2, refined by Kepler Q-Max.
Real superconducting qubits — not simulators. Every solution runs on enterprise-grade quantum infrastructure with cryogenic precision.
Quantum Processor
IBM Heron R2
156 physical qubits
Flagship Model
Kepler Q-Max
25 logical qubits
Hilbert Space
33,554,432
2²⁵ dimensional states
Gate Fidelity
99.7%
2-qubit median CZ
Quantum vs Classical. Verified results.
Evaluated on industry-standard datasets with QAOA + variational quantum classifiers. Median across 10 runs.
Average accuracy ≥ 93% across pre-trained Kepler Q-Max models
Classical AI hit a scaling wall. Quantum didn't.
The next decade of AI breakthroughs will come from leveraging superposition, entanglement, and interference — not from stacking more transformer layers.
Exponential speedups
Quadratic-to-exponential gains on optimization, sampling, and kernel tasks.
Sample efficiency
Quantum priors learn from far less labeled data than deep nets.
Native correlations
Entanglement encodes joint distributions classical models approximate poorly.
Post-classical security
Inference paths invisible to classical reverse engineering.
From problem to solved in three steps.
- 1
Pick a problem
Choose from six quantum-ready solution templates.
kepler.solve('fraud_detection') - 2
Upload your data
Drop a CSV / JSON file. We handle parsing and validation.
client.upload('./transactions.csv') - 3
Get a result
Quantum models return predictions or optimal plans in seconds.
→ result.json
A side-by-side look at the paradigm shift.
Where classical compute hits asymptotic walls, quantum scales naturally. Below is how the two architectures compare on the dimensions that decide AI outcomes.
Console, API, SDK — three doors, one engine.
Upload data through the console, hit a REST endpoint, or call our SDK. Every path lands on the same Kepler Q-Max QPU pipeline.
Accuracy
93.6%
Speedup
11×
Cost / 1k
$0.12
curl https://api.keplerq.ai/v1/predict \
-H "x-api-key: $KEPLER_KEY" \
-d '{
"model": "kepler-q-max",
"task": "fraud_detection",
"features": [0.12, -0.84, ... ]
}'
# → { "label": "fraud", "confidence": 0.974,
# "qpu_ms": 1420, "qubits": 25 }QAOA on a 25-qubit ansatz.
Kepler Q-Max combines Quantum Approximate Optimization with variational classifiers — pre-trained on industry corpora, ready for inference.
QAOA
Quantum Approximate Optimization
VQC
Variational Quantum Classifier
93%+
Median accuracy across tasks
33M
Hilbert space dimensions
Enterprise hybrid quantum infrastructure.
Production-grade tooling around the QPU — security, governance, and observability built in.
Hybrid Quantum-Classical
GPU pre-processing handles feature engineering; quantum kernels do the heavy optimization. Best of both runtimes, one API.
Enterprise Quantum Compute
Dedicated queue access on IBM Heron R2 with priority scheduling, private endpoints, and reserved qubit capacity.
Global Edge Inference
Pre-trained Kepler Q-Max model artifacts replicate to edge regions for sub-200ms classical fallback when QPU is busy.
SOC 2 + ISO 27001
Audit logs, customer-managed keys, regional data residency, and post-quantum TLS on every API call.
Zero-Knowledge Inputs
Encrypted data envelopes — your training distributions never leave your VPC. Only encoded amplitudes hit the QPU.
Versioned Model Releases
Pin to a specific Q-Max snapshot. Reproducible inference, semantic versioning, and a 12-month deprecation window.
Real teams, real quantum advantage.
Three production deployments running on Kepler Q-Max + IBM Heron R2.
Fintech
NorthBank Payments
fraud false positives
Replaced a 14-feature gradient-boosted model with Kepler Q-Max fraud detection. Catch rate jumped from 81% to 94% on the same transaction stream.
Pharma
Helix Therapeutics
compound triage
Screened 240k molecules for kinase inhibition in under a week. The quantum classifier surfaced 17 actives our team had previously missed.
Logistics
Meridian Freight
annual fuel saved
Daily route plans for 480 vehicles now solve in 90 seconds with 27% lower distance vs our previous OR-Tools pipeline.
One endpoint. Quantum on tap.
REST + JSON. SDKs in Python, TypeScript, and Go. No quantum framework, no qubits to manage — just call predict.
from kepler import Client
q = Client(api_key="sk_live_...")
result = q.predict(
model="kepler-q-max",
task="risk_assessment",
features=[...],
)
print(result.label, result.confidence)
# → "high_risk" 0.912API keys
Scoped, revocable, audit-logged. Per-environment rotation.
SDKs
Python, TypeScript, Go — typed responses, retries built-in.
Webhooks
Stream long-running QPU jobs back to your stack.
A network spanning hardware, research & enterprise.
Quantum AI doesn't ship in isolation. We've built an ecosystem of QPU vendors, academic labs, and production customers driving the standard forward.
Hardware partners
IBM Quantum (Heron R2), in-house Kepler Q-Max QPU.
Enterprise
Fintech, pharma, logistics, and energy deployments worldwide.
Research
Joint papers with quantum-ML labs across 4 continents.
Edge regions
us-east, eu-west, ap-south — quantum-classical hybrid inference.
12+
Enterprise deployments
4
Continents served
99.95%
API uptime (90d)
Built for researchers, engineers, and enterprises.
Quantum-ready infrastructure with the reliability of a modern cloud platform.
From 25 qubits to a quantum reasoning substrate.
Our roadmap isn't bigger transformers — it's a deeper integration of quantum mechanics into the fabric of AI itself.
Kepler Q-Max
25 logical qubits in production. QAOA + VQC on real workloads.
Q-Max 64
Scale to 64 logical qubits. Multi-modal quantum-classical pipelines.
Fault-tolerant era
Surface-code error correction. Quantum advantage on language modelling.
Quantum AGI substrate
Hybrid architectures where quantum is the default reasoning fabric.
Solve your hardest problem in under a minute.
Free during launch. No credit card. Get your first quantum solution in three clicks.