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Quantum computing uses quantum mechanics to process and store information in a way that is different from classical computers. While classical computers rely on bits like tiny switches that can be either 0 or 1, quantum computers use quantum bits (qubits). Qubits are unique because they can be in a mixture of 0 and 1 simultaneously - a state referred to as superposition. This unique property enables quantum computers to solve specific problems significantly faster than classical ones.
In a recent publication in EPJ Quantum Technology, Le Bin Ho from Tohoku University's Frontier Institute for Interdisciplinary Sciences has developed a technique called "Time-dependent Stochastic Parameter Shift" in the realm of quantum computing and quantum machine learning. This breakthrough method revolutionizes the estimation of gradients or derivatives of functions, a crucial step in many computational tasks.
Typically, computing derivatives requires dissecting the function and calculating the rate of change over a small interval. But even classical computers cannot keep dividing indefinitely. In contrast, quantum computers can accomplish this task without having to discrete the function. This feature is achievable because quantum computers operate in a realm known as "quantum space," characterized by periodicity, and no need for endless subdivisions.