Improving Phase Estimation by Post Processing QPE Circuit Output

Quantum Phase Estimation (QPE) is one of the most important and widely applied quantum computing algorithms known. Fundamental to Shor's algorithm, eigenvalue estimation, and other algorithms, improving the accuracy of phase estimation is central to the progression of quantum computing as a field. As quantum computers transition into the noisy intermediate-scale quantum (NISQ) era and eventually into more fault-tolerant implementations, the effective post-processing of QPE output will become increasingly pivotal to quantum computing applications. The classic method for evaluating QPE output is to take the "highest peaks" of the results for the estimate of the phases, leaving the other information behind. This method is accurate to one part in 2^(-n), where n is the number of qubits. In this talk, we present a software package that uses various numerical optimization methods along with machine learning to more accurately estimate the phases of unitary operators, reaching, especially for circuits with few qubits, well beyond the 2^(-n) limit of the traditional method. Using the techniques of the software package, a user can achieve the accuracy otherwise offered only by increasing the number of qubits of the QPE circuit or by doing iterative phase estimation, which requires mid-circuit measurement. Using this method allows the user to expand the capability of current QPE research, and we analyze the performance and applications of our method.