IBM Quantum Integrations

Superstaq's device-specific, low-level optimizations allow users to get the best performance from today's hardware in multiple qubit modalities. Qiskit and Cirq are open-source frontends that allow users to submit their work to leading quantum hardware platforms such as IBM, Infleqtion OQC, Rigetti and more. Use our pre-built quantum applications library to benchmark performance on "impossible problems" with quantum hardware. Superstaq's library includes sophisticated noise mitigation and compilation techniques, including dynamical decoupling. These techniques optimize quantum programs automatically based on the hardware's native gates. Superstaq allows you to write quantum code that targets virtually any quantum computer, whether it's Cirq.

Infrastructure software to power a quantum future Quantum technology has the potential to transform the economy. Software allows us to expand the utility and capabilities of quantum computers. Quantum infrastructure software transforms bare metal quantum processors into useful computing tools. Our technology unlocks the hidden power of the most powerful computer so you can achieve even more. Add automation and performance monitoring to my QC platform. Professional toolkits for designing, automating, and scaling quantum hardware and controls. Integrate performance management into cloud quantum computing platforms to unleash latent hardware performance. Automatically reduce error and increase algorithmic success with cloud-accessible quantum computing. Professional-grade tools to automate and scale quantum hardware.

Bayesforge™ is a Linux image that curates all the best open source software available for data scientists who need advanced analytical tools as well as quantum computing and computational math practitioners who want to work with QC frameworks. The image combines open source software such as D-Wave and Rigetti, IBM Quantum Experience, Google's new quantum computer language Cirq and other advanced QC Frameworks. Qubiter, our quantum compiler and fog modeling framework can be cross-compiled to all major architectures. The Jupyter WebUI makes all software accessible. Its modular architecture allows users to code in Python R and Octave.