HQC PLATFORM See and control every element of the signal chain—from pulse to readout—with full access to attenuators, amplifiers, and more.
Write complex quantum experiments in an intuitive Python SDK (QUA), designed for physicists by quantum hardware experts.
Leverage 2 µs hardware-to-classical round-trip latency to implement closed-loop control, adaptive measurements, and low-latency decoding within qubit coherence times.
Connect to both classical and quantum hardware in our managed quantum data center directly from your local IDE, and explore experiment data visually through an intuitive web-based GUI.
Access the complete signal chain — from high-level circuit design down to pulse-level control and hardware execution. Design experiments at the abstraction layer that suits your research: prototype at the circuit level, refine with custom pulse sequences, or work directly with calibrated control parameters. Integrate classical algorithms alongside your quantum processor and implement low-latency feedback within qubit coherence times — enabling advanced protocols, adaptive measurements, and closed-loop optimization. Built for experimentalists. Accessible from anywhere.
Start with just a few lines of code.
Leverage classical compute resources within qubit coherence times.
Work with high-level abstractions and a flexible middleware stack.
Access experiment data, calibrations, and expert guidance.




Fixed- and tunable-coupler transmon architectures supporting circuit-level and pulse-level control, fast readout, and real-time feedback for advanced experimental protocols.
An 8-mode boson-sampling optical processor for single-photon experiments, supporting detailed studies of multi-photon interference, mode correlations, and hybrid quantum-classical computational protocols.
Low-latency, high-bandwidth optical interconnects tightly couple quantum hardware with classical compute clusters. Nearly 1,000 vCPUs and terabytes of RAM support large-scale simulations, real-time feedback, and quantum IP development within a standard software ecosystem.
Access to GPU accelerators (A100, L40S, and others) and FPGA platforms (Alveo V80) enables machine learning workflows, fast decoding, custom control logic, and ultra-low-latency hybrid processing pipelines.
Focus on advancing your ideas — not managing infrastructure. We provide the hardware, control stack, and remote access environment, so you can concentrate on experiment design, algorithm development, and data analysis.
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