![]() The third limitation in quantum computing is that the number of qubits one can have on a quantum circle is limited. Most known Quantum algorithms suffer from a proviso of specific simulations that limit their practical applicability and it becomes difficult to develop models that can have a significant impact on machine learning. Most of the development is therefore intuitive. Very little information is available to develop such algorithms for quantum computing. Designing a program to operate on larger data with more processing power. The critical issue in such a design is always scalability. While a classical algorithm can be developed along the lines of the Turing machine, to develop an algorithm for Quantum computers, the developer has to base it along the lines of raw physics with no simple formulas that would link it to logic. The developer of algorithms for Quantum computers has to be concerned about their physics. The requirement of computers with a greater circuit length and error correction( with redundancy for every qubit) is also crucial for the field of quantum machine learning. Any wrong rotation can cause an error in the output. Secondly, rotations in quantum computers’ logic gates are prone to error and these are also crucial to change the state of the qubit. Quantum decoherence can be caused by heat and light, when subjected to such conditions qubits can lose their quantum properties like entanglement that further leads to a loss in data stored in these qubits. The frequent challenge that troubles researchers is isolation. #Machine learning quantum error correction softwareThe research proves that though Quantum computing is the solution to problems it has its limitations and challenges due to its dependence on raw physics and the unadvanced nature of other technologies that help in the hardware and software development of quantum computers on which complex algorithms can be created and run. The size of the system determines the scalability and the difficulty in problem-solving increases exponentially when the problem is complex or data is large. The research also found out that Bob can unscramble the book by collecting a few photons from the black hole and learning its dynamics but the answer to that cannot be reached through Quantum Machine Learning. Though the book was pulled out using quantum computing algorithms, the information was scrambled and no quantum machine learning model could unscramble the book back to its original state. ![]() Through any computation bringing the book back to its original state is impossible. ![]() The book was pulled out by Bob, who used entanglement to pull it out. A fictitious character Alice tosses her book inside the black hole. The study was based on a Hayden-Preskill thought experiment. This places a big limit on the learning of any new process linked to it through Quantum computing. Recent research at the Los Alamos National Laboratory showed that Quantum Machine Learning cannot be used to investigate processes like Quantum Chaos and terminalization. Government Has No Business To Be In Data Business ![]()
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