Stochastic emulation of quantum algorithms
Daniel Braun, Ronny M\"uller

TL;DR
This paper introduces a stochastic classical emulation method for quantum algorithms using derivatives of probability distributions, enabling simulation of quantum interference and entanglement with potential scalability benefits.
Contribution
It presents a novel stochastic framework that reproduces quantum algorithm evolution through universal stochastic maps, bridging quantum and classical computational models.
Findings
Successfully emulated several quantum algorithms
Analyzed the scaling of realizations with qubits
Highlighted the impact of destructive interference on emulation cost
Abstract
Quantum algorithms profit from the interference of quantum states in an exponentially large Hilbert space and the fact that unitary transformations on that Hilbert space can be broken down to universal gates that act only on one or two qubits at the same time. The former aspect renders the direct classical simulation of quantum algorithms difficult. Here we introduce higher-order partial derivatives of a probability distribution of particle positions as a new object that shares these basic properties of quantum mechanical states needed for a quantum algorithm. Discretization of the positions allows one to represent the quantum mechanical state of qubits by classical stochastic bits. Based on this, we demonstrate many-particle interference and representation of pure entangled quantum states via derivatives of probability distributions and find the…
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