4 papers
Learning ground state observables from quantum computing experiments
Ben Jaderberg, Freya Shah, Minjun Jeon +3
Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained…
Setting angles in quantum approximate optimization at utility-scale
Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh +16
The quantum approximate optimization algorithm (QAOA) is a powerful heuristic that seeks to solve combinatorial optimization problems using quantum hardware and classical optimizat…
Quantum resources in resource management systems
Utz Bacher, Mark Birmingham, Christopher D. Carothers +14
Quantum computing resources are increasingly being incorporated into high-performance computing (HPC) environments as co-processors for hybrid workloads. To support this paradigm,…
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
M. Emre Sahin, Edoardo Altamura, Oscar Wallis +6
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primi…