Hybrid quantum computing” is the preferred industry term for a simple idea: a quantum computer and a classical computer working together to solve a problem. In a sense, this describes all of quantum computing: we rely on classical computers for almost every aspect of running a quantum computer.


Imagine mixing Scikit-learn, TensorFlow,OpenCV, Numypy,Keras PyTorch with Q# , cirq and Qiskit . It is possible to use both solutions to solve both classical and quantum computing problems.

TensorFlow Quantum (TFQ) is a Python framework for hybrid quantum-classical machine learning that is primarily focused on modeling quantum data. TFQ is an application framework developed to allow quantum algorithms researchers and machine learning applications developed by Google
By default, every quantum computer is going to be a hybrid that combines quantum and classical compute.

Microsoft estimates that a quantum computer that will be able to help solve some of the world’s most pressing questions will require at least a million stable qubits. It’ll take massive classical compute power — which is really only available in the cloud — to control a machine like this and handle the error correction algorithms needed to keep it stable. Indeed, Microsoft estimates that to achieve the necessary fault tolerance, a quantum computer will need to be integrated with a peta-scale compute platform that can manage between 10 to 100 terabits per second of data moving between the quantum and classical machine.


Hybrid computing challenges could lead to the introduction of quantum in real-world uses like solving problems in chemistry, biology, physics ,mathematics and computer science . The future of quantum computing is hybrid by recent computer scientist and programmers .

Jax is a Python library developed by Google that provides a way to execute numerical computations on CPUs, GPUs and TPUs efficiently it should be used with QPU (Quantum processing unit). It is primarily used for machine learning tasks but can also be utilized for quantum computing simulations.

Jax can be used also to simulation well-known quantum algorithms like Grover's algorithm or Shor's algorithm and quantum circuit .

A quantum agent refers to an intelligent agent that operates using principles of quantum mechanics. Quantum mechanics is a branch of physics that deals with the behavior of particles at the microscopic level, such as atoms and subatomic particles. These agents exhibit wave-particle duality and can exist in superposition states, where they can simultaneously be in multiple states or locations until measured.

A quantum agent leverages the unique properties of quantum mechanics, such as superposition and entanglement, to perform tasks that classical agents cannot efficiently accomplish. These tasks may include optimization, machine learning, data analysis, and solving complex problems. This will bring new laws and theories in quantum mechanics and computer engineering.

Quantum agents are thought to have the potential for significant advancements in various fields, including quantum Internet, cryptography, finance, Material science,drug discovery and artificial intelligence. However, building practical quantum agents is still an area of active research, as the technology and algorithms in this field are still being developed and improved.