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The computational cost of preparing a quantum state can be substantialdepending on the structure of data to be encoded. Many quantum algorithmsrequire repeated sampling to find the answer, mandating reconstruction of thesame input state for every execution of an algorithm. Thus, the advantage ofquantum computation can diminish due to redundant state initialization. Wepresent a framework based on quantum forking that bypasses this fundamentalissue and expedites a family of tasks that require sampling from independentquantum processes. Quantum forking propagates an input state to multiplequantum trajectories in superposition, and a weighted power sum of individualresults from each trajectories is obtained in one measurement via quantuminterference. The significance of our work is demonstrated via applications toimplementing non-unitary quantum channels, studying entanglement andbenchmarking quantum control. A proof-of-principle experiment is implemented onthe IBM and Rigetti quantum cloud platforms.

Parallel quantum trajectories via forking for sampling without redundancy