Logarithmic Quantum Forking

Quantum algorithms evolve an initial quantum state into another during computation to obtain meaningful results. However, this evolution introduces the cost of re-preparing the same initial quantum state for different tasks. Unfortunately, since quantum memory is not yet available, this cost cannot be ignored in Quantum Artificial Intelligence (QAI), where the initial quantum state typically coincides with a quantum dataset. Redundant state preparations for different tasks on the same dataset can reduce the advantages of quantum computation. To address this issue, this work proposes a new technique: the Logarithmic Quantum Forking (LQF). LQF performs state preparation for an initial quantum state once and employs additional qubits to compute an exponential number of tasks over the initial quantum state. LQF enables more efficient use of quantum computation in QAI by amortizing the cost of preparing the initial quantum state.