Quantum computing is theoretically efficient for many high-dimensional problems (like factoring, optimization, or quantum simulation), but practical efficiency is still limited by hardware constraints, error rates, and readout bottlenecks. While quantum states can represent exponentially large solution spaces compactly, extracting meaningful results — due to the collapse of the quantum state upon measurement — is non-trivial.

There are promising techniques like quantum amplitude amplification, variational quantum algorithms, and quantum kernel methods that aim to efficiently extract information, but they often require careful design of observables and classical post-processing to interpret the quantum output.

Currently, quantum computers are not yet practical for everyday problems; many tasks are still handled more efficiently by classical systems. However, this does not mean that quantum computing is inherently inefficient; it simply reflects the fact that the infrastructure is still in the process of maturing.

In my open-source QKD simulation platform, I demonstrated that quantum key distribution protocols can successfully generate secure keys even under various attack scenarios, and these keys can be effectively integrated with classical AES in a hybrid encryption scheme. This shows that extracting meaningful information from quantum systems is practically achievable, as long as the system is carefully designed.

The true advantage will emerge once qubits become stable against environmental noise and quantum communication reaches a point of seamless, uninterrupted operation. At that stage, quantum systems will no longer be just theoretical tools but real, scalable infrastructures for secure communication and information processing.