Bridging quantum mechanics, artificial intelligence, and the cosmos.
Sonicium Quantum Lab builds Kepler Q-Max — a 25-qubit quantum-trained AI platform deployed via hybrid quantum-classical inference. We translate the laws of nature into production-grade intelligence.
25Q
Logical qubits — Kepler Q-Max
33M+
Hilbert space dimensions
IBM Heron R2
Quantum-trained on ibm_fez
12+
Enterprise deployments
To make quantum intelligence a daily utility — not a laboratory privilege.
Classical deep learning has hit asymptotic walls in sample efficiency, energy cost, and reasoning depth. We believe the next decade of AI will be unlocked by superposition, entanglement, and interference — running on hybrid pipelines that any developer can call with a single API request.
Three forces that shape everything we ship.
Quantum-native science
QAOA + VQC ansätze designed for noisy intermediate-scale hardware, validated on IBM Heron R2.
Hybrid AI architecture
Quantum-trained parameters, classically deployed — sub-second inference at production SLAs.
Cosmic ambition
Inspired by Kepler's laws of orbital motion: simple equations, vast emergent behavior.
A short history of a long idea.
The science is over a century old. The hardware just caught up. The rest is what we've been building.
First entanglement
Sonicium Quantum Lab founded by a team of physicists, ML researchers, and systems engineers united by one question — can quantum mechanics make AI think differently?
QAOA breakthrough
Built our first 12-qubit Variational Quantum Classifier ansatz; proved sample-efficiency wins over classical baselines on optimization benchmarks.
Kepler Q-Max launch
25-qubit hybrid model quantum-trained on IBM Heron R2 (ibm_fez). Shipped REST API, console, and six pre-trained solution templates.
Production at scale
12+ enterprise deployments across fintech, pharma, logistics, and energy. 99.95% API uptime over the last 90 days.
Four quantum primitives. One AI substrate.
Superposition
A 25-qubit register simultaneously explores 2²⁵ ≈ 33M basis states — feature spaces classical nets can only approximate.
Interference
QAOA amplitude amplification steers probability mass toward optimal solutions — gradient descent's quantum cousin.
Entanglement
Native joint distributions encode correlations a transformer would need millions of parameters to memorize.
Hybrid inference
Quantum-trained parameters serve from a classical runtime — production latency without a QPU in the request path.
Named after a man who turned the night sky into equations.
Johannes Kepler discovered that planets do not move in perfect circles — they trace ellipses, governed by three deceptively simple laws. From those laws emerged Newton's gravity, Einstein's relativity, and ultimately the language we use to describe the universe.
Kepler Q-Max is built on the same conviction: a small set of quantum primitives, applied with rigor, can describe — and decide — at cosmic scale. From a 25-qubit ansatz, an entire decision substrate emerges.
1609
Astronomia nova
3 laws
Of planetary motion
∞
Downstream physics
The principles that keep us calibrated.
Honesty over hype
We say 'quantum-trained + hybrid inference' — never 'real-time quantum hardware in the request path' if it isn't.
Reproducible science
Every benchmark we publish includes the ansatz, shot count, hardware backend, and a runnable notebook.
Ship the future weekly
Quantum AI shouldn't take a decade to reach you. We ship measurable improvements every release cycle.
Open ecosystem
Joint papers with academic labs, public APIs for developers, and transparent pricing. No black boxes.
Physicists, ML researchers, and systems engineers.
A small team that has shipped quantum software at IBM, ML infrastructure at hyperscalers, and peer-reviewed physics across four continents.
PhD
Quantum information & ML
4
Continents represented
20+
Peer-reviewed papers
100%
Remote-first, async
A roadmap pointed at the quantum reasoning era.
Kepler Q-Max
25-qubit QAOA + VQC quantum-trained on IBM Heron R2; hybrid inference in production.
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.
Build with the quantum substrate.
Spin up a free trial, hit the API, and ship a quantum-trained model to production today. No QPU required in the request path.