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The difference between quantum computer and ordinary computer
1. Different basic units: quantum computers use quantum bits as basic information units, while traditional computers use binary bits as basic information units. Quantum bits can be in the superposition state of 0 and 1 at the same time, while bits can only be in the single state of 0 or 1. It means that qubits can store and represent more information and realize more complex logical operations.
2. Different operation modes: Quantum computers use the superposition, entanglement, measurement and other characteristics of quantum mechanics to perform quantum operations. Quantum operation can establish quantum entanglement between multiple qubits, making states interdependent. Quantum operation can use quantum superposition to realize parallel information processing. The result of quantum operation usually needs to be observed by quantum measurement, which will lead to the collapse of quantum state and make quantum information unrepeatable. Traditional computers use classical logic gates, such as AND, OR and NOT, to perform classical operations. Classical operations are performed between a single bit or multiple bits, and the states are independent. The result of classical operation can be read directly without changing the state of bits, thus realizing the replication of classical information.
3. Different methods of solving problems: There are great differences between quantum computers and traditional computers in solving problems. Quantum computer is suitable for solving some problems that are difficult or impossible to be solved by traditional computers, such as prime factorization of large numbers, disorderly search, quantum simulation and so on. It usually involves exponential complexity, and quantum computers can use quantum superposition and quantum entanglement to achieve exponential acceleration. Traditional computers are suitable for solving some problems that quantum computers are difficult or unable to solve, such as sorting, graph theory, machine learning and so on. Usually involves a large number of data and structures, ordinary computers can use efficient algorithms and storage to achieve polynomial-level optimization.
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