Speaker
Description
Classical information loading is a crucial task for many quantum algorithms, playing a fundamental role in the field of quantum machine learning. Consequently, the inefficiency of this loading process becomes a significant bottleneck for the application of these algorithms. In this context, we present and compare algorithms for the amplitude classical data into a quantum computer.
We introduce two approximate quantum-state preparation methods for the NISQ era, drawing inspiration from the Grover-Rudolph algorithm. The first method reduces the number of gates required when no ancillary qubits are used, while the second proposes a variational algorithm capable of loading real functions beyond the Grover-Rudolph algorithm. We also examine the encoding of polynomial functions, either through their matrix product state representation or a scheme that involves the block encoding of the linear function using the Walsh-Hadamard transform and a polynomial transformation of the amplitudes, achieved through the quantum singular value transformation (QSVT).
