Sample Complexity of Learning Parametric Quantum Circuits
원문 초록
Quantum computers hold unprecedented potentials for machine learning applications. Here, we prove that physical quantum circuits are PAC (probably approximately correct) learnable on a quantum computer via empirical risk minimization: to learn a parametric quantum circuit with at most $n^c$ gates and each gate acting on a constant number of qubits, the sample complexity is bounded by $\tilde{O}(n^{c+1})$. In particular, we explicitly construct a family of variational quantum circuits with $O(n^{c+1})$ elementary gates arranged in a fixed pattern, which can represent all physical quantum circuits consisting of at most $n^c$ elementary gates. Our results provide a valuable guide for quantum machine learning in both theory and practice.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
The interplay of robustness and generalization in quantum machine learning
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
QuXAI: Explainers for Hybrid Quantum Machine Learning Models
Distributed Quantum Neural Networks on Distributed Photonic Quantum Computing
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
Fine-Tuning Large Language Models on Quantum Optimization Problems for Circuit Generation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.