Platform
Automatski's platform is designed for teams that want to run real workloads rather than study quantum computing from a distance. It combines quantum computers, annealers, qudit research, free SDKs, framework integrations, and hybrid orchestration for AI, HPC, and enterprise software.
Systems
Entry-level backends are preconfigured in the SDKs so developers can run examples quickly, while larger engagements can focus on production optimization, algorithm validation, and domain-specific workload design.
For quantum circuits, chemistry algorithms, cryptography experiments, Quantum AI models, option pricing, risk methods, and research workloads requiring logical qubits.
For optimization problems that can be formulated as QUBO, Ising, graph, routing, allocation, scheduling, packing, coloring, and resource planning workloads.
Qudit systems go beyond two-level qubits by encoding information across N energy levels, opening routes to more compact algorithms and more efficient representations.
SDKs
Komenco targets gate-based quantum circuit programming. Initium targets optimization with quantum annealers. Both include 25 language bindings and hundreds of examples, giving software teams a practical adoption path across web, mobile, backend, AI, HPC, and research environments.
| SDK | Purpose | What customers can build |
|---|---|---|
| Komenco | Quantum circuit programming for gate-based quantum computers. | Run circuits written natively or through PyQuil, Pytket, Qiskit, QIBO, PennyLane, Qrisp, Qlasskit, Blueqat, Cirq, QPanda3, Qoro-Divi, Braket, Classiq, and MyQLM. Use it for algorithms, chemistry, Quantum AI, finance, cryptography, and education. |
| Initium | Optimization SDK for quantum annealers and QUBO-style problems. | Submit optimization workloads written natively or through D-Wave, PyQUBO, Qubovert, AutoQUBO, MyQLM, and Qlasskit. Use it for routing, scheduling, graph coloring, portfolio optimization, logistics, telecom planning, and resource allocation. |
Hybrid workflows
Automatski integrates quantum computers and annealers into heterogeneous workflows where quantum workloads run alongside AI/ML models, GPUs, HPC clusters, cloud resources, and on-prem infrastructure.
Build Quantum Neural Networks and Quantum Machine Learning models using familiar tools such as PennyLane, Qiskit QML, and sQULearn, then execute suitable workloads on Automatski backends.
Explore molecular ground-state energy, VQE-style workflows, and large electron-count chemistry targets using specialized quantum hardware and software.
Prepare for architectures where CPUs, GPUs, HPC systems, AI models, quantum computers, and annealers cooperate as one workflow rather than isolated silos.
Evaluation
Prospective customers can bring one workload and leave with a clear technical path: formulation, backend selection, SDK integration, benchmark plan, and deployment options.
Translate business constraints into circuits, QUBO models, Pauli exponentials, graph structures, molecular Hamiltonians, or hybrid workflows.
Decide whether the workload belongs on an annealer, gate-based system, qudit method, simulator-backed development path, AI workflow, or classical-quantum hybrid pipeline.
Map the SDK, language bindings, framework choices, data movement, security needs, reporting, and pilot success metrics before committing engineering time.