Quantum machine learning with D‐wave quantum computer

Citations Over TimeTop 10% of 2019 papers

Abstract

The new era of artificial intelligence (AI) aims to entangle the relationships among models (characterizations), algorithms, and implementations toward the high-level intelligence with general cognitive ability, strong robustness, and interpretability, which is intractable for machine learning (ML). Quantum computer provides a new computing paradigm for ML. Although universal quantum computers are still in infancy, special-purpose D-Wave machine hopefully becomes the breaking point of commercialized quantum computing. The core principle, quantum annealing (QA), enables the quantum system to naturally evolve toward the low-energy states. D-Wave's quantum computer has developed some applications of quantum ML based on quantum-assisted ML algorithms, quantum Boltzmann machine, etc. Additionally, working with CPUs, quantum processing units is likely to advance ML in a quantum-inspired way. Thus, a new advanced computing architecture, quantum-classical hybrid approach consisting of QA, classical computing, and brain-inspired cognitive science, is required to explore its superiority to universal quantum algorithms and classical ML algorithms. It is important to explore hybrid quantum/classical approaches to overcome the defects of ML such as high dependence on training data, low robustness to the noises, and cognitive impairment. The new framework is expected to gradually form a highly effective, accurate, and adaptive intelligent computing architecture for the next generation of AI.

Related Papers

• → Implementation of Quantum Support Vector Machine Algorithm Using a Benchmarking Dataset(2022)22 cited
• → Quantum algorithm and quantum circuit for A-optimal projection: Dimensionality reduction(2019)55 cited
• → Quantum Machine Learning: A Case Study of Grover’s Algorithm(2021)13 cited
• → Developing a Model for Predicting Lung Cancer Using Variational Quantum-Classical Algorithm: A Survey(2022)3 cited
• → Optimized Algorithms for Quantum Machine Learning Circuits(2023)