3 papers
quant-ph2026
A Transferable Machine Learning Approach to Predict Optimized Orbitals for Electronic Structure Problems
Lucas van der Horst, Maniraman Periyasamy, Abhishek Y. Dubey +3
Variational quantum eigensolver ansätze hold considerable promise for ground-state energy calculations on near-term quantum hardware, yet most promising ansatz designs currently s…
quant-ph2025
A Transferable Machine Learning Approach to Predict Quantum Circuit Parameters for Electronic Structure Problems
Davide Bincoletto, Korbinian Stein, Jonas Motyl +1
The individual optimization of quantum circuit parameters is currently one of the main practical bottlenecks in variational quantum eigensolvers for electronic systems. To this end…
quant-ph2025
State Specific Measurement Protocols for the Variational Quantum Eigensolver
Davide Bincoletto, Jakob S. Kottmann
A central roadblock in the realization of variational quantum eigensolvers on quantum hardware is the high overhead associated with measurement repetitions, which hampers the compu…