Build & Run

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Solution · Build & Run

From defined use case to running solver

Bring your code, run it on the quantum, GPU or classical hardware that fits, and switch backends without rewriting it.

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  1. Discovery
  2. Prioritisation & roadmap
  3. Experimentation
  4. Results & decision
  5. Structured program
  6. Scaling & Quantum Ops

Build & Run is the stage of the QCentroid QuantumOps platform where defined use cases become working solvers.

Developers prototype in cloud notebooks with GPUs, build solvers from their own Git repositories and run them on quantum hardware, simulators, NVIDIA GPUs or classical compute through one integration. The same code runs on any backend, so teams avoid vendor lock-in.

  • Telefónica
  • Mahou San Miguel
  • MasOrange
  • GSMA
  • Moeve
  • Moody's
  • Sabadell BStartup
  • BiQain
  • BackFund
  • QAI Ventures
  • CDL
  • Telefónica
  • Mahou San Miguel
  • MasOrange
  • GSMA
  • Moeve
  • Moody's
  • Sabadell BStartup
  • BiQain
  • BackFund
  • QAI Ventures
  • CDL

Products

Which tools are used in this stage?

Local prototypesSolvers the whole team can run

Solver Workbench

Turn your code into a validated solver that the platform builds and runs.

  • Solver Templates & Runtime: start from a provider template
  • Repository-Connected Solvers: build straight from Git
  • Build-Time Code Validation: errors caught before the run
Explore Solver Workbench
Repository page of a connected solver with its builds history and the entrypoint check (qcentroid.py, run() and signature) passed

One vendor, one SDKAny backend, same code

Compute Gateway

Run on quantum hardware, simulators, GPUs or classical compute through one integration.

  • Multi-Provider Quantum Hardware Access: leading QPUs in one place
  • GPU-Accelerated Execution: NVIDIA GPUs with CUDA-Q
  • Provider Credential Management: secure access per provider
Explore Compute Gateway
Enabled hardware providers: Fujitsu Bizkaia, Amazon Braket, Xanadu, Pennylane, classical CPU, Alice & Bob and more

Setup and driversCoding in minutes

Launchpad

Prototype and test in a cloud notebook with CPU and GPU ready to go.

  • Cloud Jupyter Workspace: nothing to install
  • On-Demand CPU & NVIDIA GPU: right-sized compute
  • Hybrid Development with CUDA-Q: simulate, then run on QPUs
Product page coming soon
Screenshot pending: cloud notebook

How does it work?

  1. 01

    Prototype

    Launchpad

    Test ideas in a notebook on CPU or GPU.

  2. 02

    Build

    Solver Workbench

    Package your code as a solver from Git; the platform validates and builds it.

  3. 03

    Run

    Compute Gateway

    Execute on the backend that fits, from simulators to real QPUs.

What you walk away with

  • Solvers built from your own code
  • Runs on quantum, GPU and classical backends
  • Provider credentials managed in one place
  • Versioned, reproducible runtimes

AI layer in this stage

AI assistants help turn the problem formulation into a solver approach. Your developers write and own the code.

Build & Run

Your code, any backend, no plumbing

Build, validate and run solvers from one platform.

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QCentroid platform home: your quantum journey with the tools of each phase