Making data accessible for quantum computing

Q-Alchemy solves one of the fundamental bottlenecks in quantum computing: getting classical data into the quantum processor. Known as quantum state preparation (or the loading problem), this step is essential for quantum machine learning, quantum chemistry, solving PDEs, and Monte Carlo simulations — yet it remains one of the hardest parts of building practical quantum algorithms.

Our platform automates and optimizes this process. By exploiting the entanglement structure of your data through singular value decompositions, Q-Alchemy finds the most efficient way to encode classical vectors as quantum states — reducing circuit depth and improving end-to-end algorithm performance.

Why Q-Alchemy?

Automated state preparation — No need to manually design encoding circuits. Provide your data vector, and Q-Alchemy returns an optimized quantum circuit ready to run.
Platform-agnostic — Works across quantum hardware: superconducting qubits, ion traps, photonic systems, neutral atoms, and more.
NISQ-ready — Supports approximate state preparation with configurable fidelity loss, giving you control over the depth-accuracy tradeoff on today’s noisy devices.
Simple integration — Use the Python SDK with Qiskit, PennyLane, or directly via QASM. A REST API is also available for any language.

Get started

Introduction Sign up, get your API key, and run your first state preparation.

Python SDK Install the SDK and integrate Q-Alchemy into your quantum workflow.

Algorithm Learn how Q-Alchemy exploits entanglement structure to reduce circuit complexity.

API Reference Explore the full REST API specification.