Fault Tolerance Enhancements for Quantum Data Encodings
Aviraj Sinha, Mitchell Aaron Thornton · 2025
Data quality is an important factor in quantum computation, since quantum algorithms need high quality representations in order to run with low error rates. In addition, quantum algorithms benefit from different types of quantum data encodings that are tailored to specific applications. In order to improve data quality while maintaining data variety, this work applies fault tolerance techniques specific to different types of data encodings. Thus, fault tolerance improvements, aimed at reducing both noise and bias errors, are applied to two types of data encodings. The first type, angle encoding, benefits from an enhanced dynamic range representation. The second type, distribution encoding, is enhanced through the use of error correcting states. Our experiments encode classical data as a quantum state, apply noise simulations, and provide error analyses on the decoded data. Experimental results are assessed by comparing the post-processed output statistics to those of the original encodings and show improved accuracy through the inclusion of fault tolerance methods.