Quantum Computing and Machine Learning Integration: Challenges and Opportunities

Abstract

On the other hand, despite the fantastic results already achieved, the manipulation and control of quantum systems are still a big challenge, and the concept of quantum error correction is still very far from a scalable implementation. The mixed environment in which those systems are manipulated during the computation and the difficulty in creating coherent interactions between distant qubits are two of the key challenges a quantum engineer needs to overcome. For this reason, searching for an alternative or a complementary way to exploit the power of quantum mechanics has become as important as developing fault-tolerant quantum computers.

The amazing progress of quantum technologies and the related increased number of qubits promised a wide range of practical developments, ranging from perfect error correction protocols to enhanced problem solvers when compared to their classical counterparts or even to unbreakable encryption schemes. The possibility of significant acceleration in important problems or solving them in alternative ways makes quantum computing a proposal deeply attractive not only for academics but for relevant industrial sectors with a high level of strategic planning (i.e. national security or financial market).