Optimal Universal Quantum Encoding for Statistical Inference

2024-04-12 · Farhad Farokhi

Optimal encoding of classical data for statistical inference using quantum computing is investigated. A universal encoder is sought that is optimal for a wide array of statistical inference tasks. Accuracy of any statistical inference is shown to be upper bounded by a term that is proportional to maximal quantum leakage from the classical data, i.e., the input to the inference model, through its quantum encoding. This demonstrates that the maximal quantum leakage is a universal measure of the quality of the encoding strategy for statistical inference as it only depends on the quantum encoding of the data and not the inference task itself. The optimal universal encoding strategy, i.e., the encoding strategy that maximizes the maximal quantum leakage, is proved to be attained by pure states. When there are enough qubits, basis encoding is proved to be universally optimal. An iterative method for numerically computing the optimal universal encoding strategy is presented.

📄 PDF Abstract BibTeX arXiv:2404.08172

Code (0)

등록된 구현이 없습니다.

Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning

2022-04-14 · Qiuhao Chen, Yuxuan Du, Qi Zhao, Yuling Jiao 외

Efficient quantum compiling tactics greatly enhance the capability of quantum computers to execute complicated quantum algorithms. Due to its fundamental importance, a plethora of quantum compilers has been designed in p…

Deep Reinforcement Learning Q-Learning reinforcement-learning Reinforcement Learning (RL)

Benchmarking data encoding methods in Quantum Machine Learning

2025-05-20 · Orlane Zang, Grégoire Barrué, Tony Quertier

Data encoding plays a fundamental and distinctive role in Quantum Machine Learning (QML). While classical approaches process data directly as vectors, QML may require transforming classical data into quantum states throu…

Benchmarking Quantum Machine Learning

The effect of data encoding on the expressive power of variational quantum machine learning models

2020-08-19 · Maria Schuld, Ryan Sweke, Johannes Jakob Meyer

Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions. While a lot of work has been done to investigate practical implications of th…

BIG-bench Machine Learning Quantum Machine Learning

Re-uploading quantum data: a universal function approximator for quantum inputs

2025-09-23 · Hyunho Cha, Daniel K. Park, Jungwoo Lee arxiv

Quantum data re-uploading has proved powerful for classical inputs, where repeatedly encoding features into a small circuit yields universal function approximation. Extending this idea to quantum inputs remains underexpl…

Quantum Machine Learning

Encoding-dependent generalization bounds for parametrized quantum circuits

2021-06-07 · Matthias C. Caro, Elies Gil-Fuster, Johannes Jakob Meyer, Jens Eisert 외

A large body of recent work has begun to explore the potential of parametrized quantum circuits (PQCs) as machine learning models, within the framework of hybrid quantum-classical optimization. In particular, theoretical…

Generalization Bounds Learning Theory Model Selection