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)

The most consequential open problem is the encoding bottleneck itself, for which intermediate, patch-based schemes that trade qubit count against state-preparation depth are the natural avenue.

— Experimental evidence of generalization in quantum machine learning in small-data regime  (2609.24666 - Anthony et al., 21 Sep 2026) in Section 'Discussion and conclusion'

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

While the present study demonstrates successful optimization for problem sizes of up to 1500 borrowers, it remains unclear how the trainability of the associated VQA evolves as the number of encoded variables increases substantially beyond this scale.

— Loan Portfolio Optimization with Variational Quantum Algorithms  (2609.30195 - Bhargava et al., 24 Sep 2026) in Section Outlook