Quantum computing for data science
arXiv:2302.08666 · doi:10.1088/1742-6596/2438/1/012007
Abstract
I provide a perspective on the development of quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms and the potential for quantum machine learning
arXiv comment20th International Workshop on Advanced Computing and Analysis Techniques in Physics Research 29/11/2021 - 03/12/2021 (current as of December 2021)
References in corpus (17)
- Quantum Computing in the NISQ era and beyond
- Variational Quantum Algorithms
- Advances in Quantum Cryptography
- An introduction to quantum machine learning
- Quantum computation over continuous variables
- Multi-particle entanglement of hot trapped ions
- Quantum optimization using variational algorithms on near-term quantum devices
- Dynamically protected cat-qubits: a new paradigm for universal quantum computation
- Defining and detecting quantum speedup
- Operator Quantum Error Correcting Subsystems for Self-Correcting Quantum Memories
- Efficient Classical Simulation of Continuous Variable Quantum Information Processes
- Quantum versus Classical Annealing of Ising Spin Glasses
- Support vector machines on the D-Wave quantum annealer
- Quantum-inspired algorithms in practice
- Prospects for Quantum Enhancement with Diabatic Quantum Annealing
- The emerging commercial landscape of quantum computing
- How many qubits are needed for quantum computational supremacy?