2020
DOI: 10.1088/1367-2630/ab7bf3
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Extending qubit coherence by adaptive quantum environment learning

Abstract: Decoherence, resulting from unwanted interaction between a qubit and its environment, poses a serious challenge towards the development of quantum technologies. Recently, researchers have started analysing how real-time Hamiltonian learning approaches, based on estimating the qubit state faster than the environmental fluctuations, can be used to counteract decoherence. In this work, we investigate how the back-action of the quantum measurements used in the learning process can be harnessed to extend qubit cohe… Show more

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Cited by 16 publications
(9 citation statements)
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“…Largely, the motivation for this development has been rooted in quantum information processing, where efficient and precise measurements and state manipulation is required [12], and indeed significant progress towards the implementation of quantum gates and algorithms has been made in optical setups [13,14], NV centers in diamond [15,16], trapped ions [17,18] and superconducting circuits [19,20]. Since it employs similar control and measurement techniques as quantum computing, the exploration of quantum-enhancing techniques has grown as a separate field, usually referred to as quantum metrology [21,22], with applications such as ultrasensitive force detection [23], adaptive environment sensing [24], near-surface electric field measurements [25], sensing of weak signals [26] and even detection of gravitational waves [27].…”
Section: Introductionmentioning
confidence: 99%
“…Largely, the motivation for this development has been rooted in quantum information processing, where efficient and precise measurements and state manipulation is required [12], and indeed significant progress towards the implementation of quantum gates and algorithms has been made in optical setups [13,14], NV centers in diamond [15,16], trapped ions [17,18] and superconducting circuits [19,20]. Since it employs similar control and measurement techniques as quantum computing, the exploration of quantum-enhancing techniques has grown as a separate field, usually referred to as quantum metrology [21,22], with applications such as ultrasensitive force detection [23], adaptive environment sensing [24], near-surface electric field measurements [25], sensing of weak signals [26] and even detection of gravitational waves [27].…”
Section: Introductionmentioning
confidence: 99%
“…Finally, the Gaussian-approximation described here could be applied to track a quantum signal, such as the magnetic field arising from a bath of nuclear spins surrounding a central electron spin. Previous theoretical work has shown that, by adaptively tracking the fluctuating nuclear magnetic field and narrowing its distribution through the back-action of the quantum measurement process, one can considerably extended the coherence time of the central spin [42]. The protocol described here can reduce the computational complexity of this task, enabling faster and more precise tracking.…”
Section: Discussionmentioning
confidence: 99%
“…We numerically show the robustness of our method to imperfect readout fidelities and realistic heating rates. Our adaptive sensing scheme is directly applicable to DC field sensing tasks and differs from previous schemes [30][31][32] in its focus on maximal information gain in each measurement and the possibility to mitigate back-action effects. The use of a TLS sensor also distinguishes our protocol from existing feedback schemes for optical setups, which operate in the continuous measurement regime with infinitesimal information gain and simultaneous feedback [14,[33][34][35] as well as in the pulsed regime [36].…”
mentioning
confidence: 99%
“…Although it does not affect the spatial probability distribution, it must be kept track of, as it becomes relevant in the part of the protocol where the momentum quadrature is mapped onto the position quadrature. The fact that we are measuring a quantum system that can suffer back action effects precludes an application of existing Ramsey based adaptive sensing schemes [30][31][32], because their back action inevitably leads to a broadening of the momentum distribution [27].…”
mentioning
confidence: 99%
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