Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866914616180736000 |
|---|---|
| author | Aoun, Kevin W. Kiwit, Florian J. Riofrío, Carlos A. Saab Jr., Samer Bateh, Charbel Al Tekli, Joe Luckow, Andre |
| author_facet | Aoun, Kevin W. Kiwit, Florian J. Riofrío, Carlos A. Saab Jr., Samer Bateh, Charbel Al Tekli, Joe Luckow, Andre |
| contents | A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data into a quantum state. Although amplitude encoding can represent $2^n$-dimensional data using only $n$ qubits in principle, preparing arbitrary states remains computationally expensive, typically requiring variational optimization of a parameterized quantum circuit for each individual data instance. In this work, we propose a method that avoids iterative optimization by training a classical neural network to map input data directly to the continuous parameters of a fixed quantum circuit. We demonstrate the generation of quantum image states with high fidelity on data not seen during training. Since all optimization is performed once during training, the resulting model encodes new inputs in a single inference step, providing a scalable pathway for data loading in near-term quantum algorithms. We validate our method on the MNIST and Fashion-MNIST datasets, achieving fidelities up to 0.992 on unseen images and reducing the per-data-instance runtime by more than 5000-fold. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_31006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning Aoun, Kevin W. Kiwit, Florian J. Riofrío, Carlos A. Saab Jr., Samer Bateh, Charbel Al Tekli, Joe Luckow, Andre Quantum Physics A central challenge in quantum machine learning is the state preparation bottleneck that describes the prohibitive computational cost of loading high-dimensional classical data into a quantum state. Although amplitude encoding can represent $2^n$-dimensional data using only $n$ qubits in principle, preparing arbitrary states remains computationally expensive, typically requiring variational optimization of a parameterized quantum circuit for each individual data instance. In this work, we propose a method that avoids iterative optimization by training a classical neural network to map input data directly to the continuous parameters of a fixed quantum circuit. We demonstrate the generation of quantum image states with high fidelity on data not seen during training. Since all optimization is performed once during training, the resulting model encodes new inputs in a single inference step, providing a scalable pathway for data loading in near-term quantum algorithms. We validate our method on the MNIST and Fashion-MNIST datasets, achieving fidelities up to 0.992 on unseen images and reducing the per-data-instance runtime by more than 5000-fold. |
| title | Quantum State Preparation via Neural Network Encoding in Quantum Machine Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2605.31006 |