Input Redundancy for Parameterized Quantum Circuits
Francisco Javier Gil Vidal, Dirk Oliver Theis
Abstract
In this paper we deal with a special type of parameterized quantum circuits, the so-called quantum neural networks or QNN's, which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al.\ (Phys.\ Rev.\ A, 2018; see below). The input is encoded into amplitudes of states of qubits. The no-cloning principle of quantum mechanics suggests that there is an advantage in redundantly encoding the input value several times. We follow this suggestion, and prove lower bounds on the number of redundant copies for two types of input encoding. We draw conclusions for the architecture design of QNNs.
Topics & Concepts
Parameterized complexityQubitQuantumRedundancy (engineering)Electronic circuitEncoding (memory)Computer scienceFunction (biology)Type (biology)Topology (electrical circuits)MathematicsTheoretical computer scienceAlgorithmQuantum mechanicsPhysicsCombinatoricsArtificial intelligenceOperating systemEvolutionary biologyBiologyEcologyQuantum Computing Algorithms and ArchitectureLow-power high-performance VLSI designAdvancements in Semiconductor Devices and Circuit Design