Input Redundancy for Parameterized Quantum Circuits
This addresses a foundational issue in quantum machine learning for researchers designing quantum neural network architectures, but it appears incremental as it builds on prior work by Mitarai et al. (2018).
The paper tackles the problem of input encoding in parameterized quantum circuits (quantum neural networks) by investigating the advantage of redundantly encoding input values multiple times, as suggested by the no-cloning principle, and proves lower bounds on the number of redundant copies for two types of input encoding.
The topic area of this paper parameterized quantum circuits (quantum neural networks) which are trained to estimate a given function, specifically the type of circuits proposed by Mitarai et al. (Phys. Rev. A, 2018). 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.