Best Quantum Computing Software for Python

Open source platform for machine learning. TensorFlow is a machine learning platform that is open-source and available to all. It offers a flexible, comprehensive ecosystem of tools, libraries, and community resources that allows researchers to push the boundaries of machine learning. Developers can easily create and deploy ML-powered applications using its tools. Easy ML model training and development using high-level APIs such as Keras. This allows for quick model iteration and debugging. No matter what language you choose, you can easily train and deploy models in cloud, browser, on-prem, or on-device. It is a simple and flexible architecture that allows you to quickly take new ideas from concept to code to state-of the-art models and publication. TensorFlow makes it easy to build, deploy, and test.

It is based upon a complete representation for the qubit state, but avoids the explicit representation in terms of matrices of gates and other quantum operation. Intel-QS uses the MPI (message-passing-interface) protocol to handle communication between the distributed resources used to store and manipulate quantum states. Intel-QS is built as a library that, when linked to an application program, allows the user to benefit from the high-performance implementations of circuit simulations. The library can run on a wide range of systems, including laptops and HPC server systems.

Cirq is a Python library that allows you to write, manipulate, and optimize quantum circuits. Then, you can run them on quantum simulators and quantum computers. Cirq is a Python software library that provides useful abstractions to deal with noisy intermediate-scale quantum computer systems, where the details of hardware are crucial for achieving state-ofthe-art results. Cirq has built-in simulations for both wave functions and density matrices. These can handle noisy quanta channels using monte-carlo or full matrix simulations. Cirq also works with the state-of-the art wavefunction simulator qsim. These simulators can also be used to simulate quantum hardware using the quantum virtual machines. Cirq is used by Google to run experiments on its quantum processors. You can learn more about the latest experiments, and download the code so you can run them yourself.

Qiskit comes with a complete set of quantum gates, as well as a variety pre-built circuits. This allows users of all levels to use Qiskit in research and application development. The transpiler converts Qiskit code to an optimized circuit using the native gate set of a backend, allowing users program for any quantum processor. Users can transpile using Qiskit's standard optimization, a custom configuration, or their own plugin. Qiskit allows users to schedule and run quantum programs using a variety local simulators or cloud-based quantum processors. It supports a variety of quantum hardware designs such as superconducting ions and trapped qubits. Are you ready to discover Qiskit for yourself? Learn how to run Qiskit on your local Python environment or in the cloud.

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.