  Matrix product state (MPS) offers a framework for encoding classical data
into quantum states, enabling the efficient utilization of quantum resources
for data representation and processing. This research paper investigates
techniques to enhance the efficiency and accuracy of MPS representations
specifically designed for encoding classical data. Based on the observations
that MPS truncation error depends on the pattern of the classical data, we
devised an algorithm that finds optimal qubit mapping for given classical data,
thereby improving the efficiency and fidelity of the MPS representation.
Furthermore, we evaluate the impact of the optimized MPS in the context of
quantum classifiers, demonstrating their enhanced performance compared to the
conventional mapping. This improvement confirms the efficacy of the proposed
techniques for encoding classical data into quantum states. MPS representation
combined with optimal qubit mapping can pave a new way for more efficient and
accurate quantum data representation and processing.
