Macroscopic Hyperpolarization Enhanced with Quantum Optimal Control

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

“…Indeed, the defect quantum levels are magnetically sensitive exhibiting Zeeman splitting, the quantum properties are stable in a wide range of temperatures, from cryogenic to room temperature and diamonds are biologically inert [53]. Thus, in the last years, a very fast development of diamond-based magnetometers for nanoscale sensing also in biological and living cells have been explored and achieved [128,237,286,345,353,410,552,617]. Moreover, the sensitivity of the defect properties to strain, electric fields and temperature open the way to a complete new set of sensors of pressure, fields and temperature at the nanoscale [489].…”

“…On similar ground, experiments have succesfully probed Floquet states for robust control of nuclear spins in NV centers [617], and an optimal two-step approach has been used to improve spin manipulation processes for robust magnetometry with single NV centers [448]. Building on these and other demonstrations of optimally controlled quantum sensing protocols, more complex and challenging protocols have been proposed such as the autonomous calibration of single NV center operations [235] and the enhancement of the macroscopic hyperpolarization [410]. Finally, interesting links with many-body theory have been unveiled, such as the determination that for a spin sensor of time-varying fields with dephasing noise, the optimal control problem of finding the optimal driving can be mapped to the search of the ground state of a spin chain [286].…”

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

“…Indeed, the defect quantum levels are magnetically sensitive exhibiting Zeeman splitting, the quantum properties are stable in a wide range of temperatures, from cryogenic to room temperature and diamonds are biologically inert [50]. Thus, in the last years, a very fast development of diamond-based magnetometers for nanoscale sensing also in biological and living cells have been explored and achieved [125,234,282,341,349,406,546,611]. Moreover, the sensitivity of the defect properties to strain, electric fields and temperature open the way to a complete new set of sensors of pressure, fields and temperature at the nanoscale [484].…”

“…On similar ground, experiments have succesfully probed Floquet states for robust control of nuclear spins in NV centers [611], and an optimal two-step approach has been used to improve spin manipulation processes for robust magnetometry with single NV centers [444]. Building on these and other demonstrations of optimally controlled quantum sensing protocols, more complex and challenging protocols have been proposed such as the autonomous calibration of single NV center operations [232] and the enhancement of the macroscopic hyperpolarization [406]. Finally, interesting links with many-body theory have been unveiled, such as the determination that for a spin sensor of time-varying fields with dephasing noise, the optimal control problem of finding the optimal driving can be mapped to the search of the ground state of a spin chain [282].…”

One decade of quantum optimal control in the chopped random basis

“…In quantum many-body physics [1,2,31,32,35,44,[48][49][50][51][52][53][54][55][56][57][58][59] QOC can help to manipulate the dynamics of quantum many-body systems in a controlled way and thereby contribute to the understanding of phenomena such as quantum phase transitions and other open questions in quantum material science and the engineering of quantum technology platforms. The key features of the CRAB ansatz for QOC are its capability to work under experimental constraints-while at the same time being able to maintain favourable convergence properties by an access to the usually trap-free control landscape (see section 2 and references [3,6,[60][61][62][63][64][65])-and its flexibility to switch from open-loop to closed-loop optimization including remote optimization [15,19,29,34,66].…”