IONQ QUANTUM AI
Explore IonQ's latest work in trapped-ion quantum computing, fault-tolerant architectures, quantum error correction, quantum algorithms and application-focused research.
FEATURED RESEARCH
A fault-tolerant architecture for trapped-ion quantum computing that explores how logical qubits and error correction can be organized into a scalable quantum computing system.
A framework for discovering learnable parity representations and recovering encoders that transform classical machine-learning data into useful quantum representations.
Research into practical quantum topological data analysis for extracting high-dimensional structure and analyzing complex time-series data using quantum computing techniques.
An experimental study of protein-folding optimization using counterdiabatic quantum optimization on a 64-qubit trapped-ion quantum processor.
A hybrid quantum-classical optimization framework for planning energy storage systems, combining quantum computing techniques with classical optimization methods.
Research applying quantum lattice Boltzmann methods to transport problems with three-dimensional spatially varying advection, demonstrated on trapped-ion quantum hardware.
Browse the complete collection of IonQ's quantum research, papers and breakthroughs.
