Quantum Machine Learning Backend Selection Process for Medical Applications

Explore the intricacies of selecting appropriate quantum backends for machine learning applications in the medical field during this hour-long open discussion. Delve into various quantum simulators and real quantum computers offered by leading platforms such as IBM Qiskit, Pennylane, TensorFlow Quantum, and Amazon AWS Braket. Learn about state-vector and mixed-state qubit simulators, their capabilities, and how to evaluate qubit gates for minimal error. Discover the differences between simulators like default.qubit, default.mixed, and lightning.qubit in Pennylane, as well as DM1, SV1, and TN1 in AWS Braket. Gain insights into the execution backend agnostic nature of TensorFlow Quantum and the diverse quantum hardware types available through third-party providers. Understand how to assess and choose the most suitable backend based on quantum circuit size and hybrid classical-quantum applications, with a focus on medical use cases.

Quantum Machine Learning Backend Selection Process; Medical