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1. The stage at which quantum computers can outperform classical computers in solving specific problems.
2. The process of interpreting the meaning of words and phrases in context.
3. A type of machine learning algorithm that uses principles of evolution to generate solutions to complex problems.
4. A fundamental element of quantum circuits that manipulates qubits with the purpose of executing precise operations.
5. The potential loss of jobs as AI systems become more capable and automated processes replace human labor.
6. The reduction in expenses or overhead through the implementation of AI.
7. A set of well-defined instructions or rules used to solve a particular computational problem in DNA computing.
8. A field of study that uses statistical algorithms to enable a machine to improve its performance on a specific task.
9. A type of machine learning algorithm modeled after the structure of the human brain, capable of learning complex patterns and relationships.
10. Microscopic particles, used in nano computing for data storage, sensing, and drug delivery.
11. The process of forming DNA strands through the binding of complementary base pairs, an essential step in DNA computing.
12. A quantum gate transforms a qubit from the base states to a superposition state with equal probability of different outcomes.
13. A type of machine learning where the model learns patterns and relationships in data without explicit supervision or labeled examples.
14. The process of breaking text into smaller pieces, called tokens, which can be words or subwords.
15. Computing that harnesses the principles of quantum mechanics to carry out computations.
16. The manipulation and control of matter on an atomic, molecular, and supramolecular scale, which plays a significant role in DNA computing.
17. The loss of coherence or information in a quantum system occurs when it interacts with the external environment.