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.

References

Thirdly, encoding high-dimensional classical data into quantum states efficiently is still an unresolved issue, with ongoing research into effective quantum data encoding strategies .

— Adversarially Robust Quantum Transfer Learning  (2510.16301 - Khatun et al., 18 Oct 2025) in Section 1 (Introduction)

Open questions are how quantum learning models have to be adapted for LOB data and how concepts such as bilinear normalization and attention translate to the quantum model.

— Quantum Weighted Moving Average for Predicting Limit Order Book Trends  (2609.01524 - Kamm et al., 1 Sep 2026) in Section 1, Introduction