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Experimental Semi-Autonomous Eigensolver Using Reinforcement Learning
(Nature, 2021-06-10)
The characterization of observables, expressed via Hermitian operators, is a crucial task in quantum mechanics. For this reason, an eigensolver is a fundamental algorithm for any quantum technology. In this work, we implement ...
Superconducting circuit architecture for digital-analog quantum computing
(Springer, 2022)
[EN] We propose a superconducting circuit architecture suitable for digital-analog quantum computing (DAQC) based on an enhanced NISQ family of nearest-neighbor interactions. DAQC makes a smart use of digital steps (single ...
Digitized-counterdiabatic quantum approximate optimization algorithm
(American Physical Society, 2022)
[EN] The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state ...
Portfolio optimization with digitized counterdiabatic quantum algorithms
(American Physical Society, 2022-12)
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete meanvariance portfolio ...