Home
Create with Pasqal Documentation
Your pathway for building quantum solutions
Learn how to leverage Python SDKs, industry-grade solvers, and scalable emulators to prototype, optimize, and apply quantum technology to real-world problems.
From problem class to pulses to hardware
Quantum Applications
Ready-to-use libraries built around quantum-hybrid algorithms tailored to problem classes as combinatorial optimization, dynamical system simulation, or quantum-inspired machine learning, bridging customer needs to quantum solutions.Quantum SDKs
Python libraries to design and run quantum algorithms on neutral-atom hardware. Build and control pulse sequences with Pulser, or use high-level abstractions with QoolQit to focus on your application.
Execution
Run your algorithms on Pasqal’s quantum cloud (QPU) or test them on remote or local emulators before deploying to hardware.An idea is enough to start
Describe the experiment you want to run.
The coding agent you already use picks up the machine-facing work: device limits, register geometry, pulse code, emulation, submission. You keep every decision.
“I want to know whether a square array of about 25 atoms orders antiferromagnetically when I ramp the detuning through the transition. Set it up so I can emulate it locally first.”
Comes back with the questions your description does not settle — which observable decides it, which part of the ramp matters, whether a register small enough to emulate still answers anything. Then it writes the files.