Adiabatic training of VQAs for entanglement detection

Abstract

This paper introduces a hybrid quantum framework for training Variational Quantum Algorithms (VQAs) aimed at addressing the fundamental problem of entanglement detection using adiabatic quantum computing. This problem is of foundational significance in quantum information science and has been approached through both classical and quantum methodologies. We propose an adiabatic-inspired optimization strategy for training VQAs, which combines a discretization of the parameter space with a hierarchical segmentation scheme. The optimization problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model to leverage the capabilities of quantum annealing. Preliminary results are compared against classical neural networks and conventional VQA training techniques. The findings aim to stimulate further investigation and discussion within the scientific community regarding the efficacy and applicability of the proposed approach.

arXiv comment7 pages, 8 figures

References in corpus (11)

• Quantum entanglement
• Separability Criterion for Density Matrices
• Separability of Mixed States: Necessary and Sufficient Conditions
• Three qubits can be entangled in two inequivalent ways
• Variational Quantum Algorithms
• Ising formulations of many NP problems
• Barren Plateaus in Variational Quantum Computing
• QUBO Formulations for Training Machine Learning Models
• Detecting and quantifying entanglement on near-term quantum devices
• Entanglement detection with classical deep neural networks
• Variational determination of multi-qubit geometrical entanglement in NISQ computers