Efficient Encoding of High-Dimensional Classical Data into Quantum States

Develop efficient methods for encoding high-dimensional classical data into quantum states for use in quantum machine learning models such as quantum variational circuits, addressing near-term hardware constraints including limited qubits, circuit depth, and noise susceptibility.

Background

The paper highlights scalability challenges for quantum machine learning when dealing with high-dimensional data due to limitations in qubit count, deeper circuits’ susceptibility to noise, and encoding overheads. Efficient quantum data encoding is identified as a bottleneck for applying quantum models to real-world, high-resolution datasets.

The authors situate this issue within ongoing efforts on approximate amplitude encoding and robust encodings, indicating that despite progress, a generally applicable and efficient encoding strategy remains unresolved for practical quantum-enhanced learning.