20 research outputs found

    Tunable phonon blockade in weakly nonlinear coupled mechanical resonators via Coulomb interaction

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    Realizing quantum mechanical behavior in micro- and nanomechanical resonators has attracted continuous research effort. One of the ways for observing quantum nature of mechanical objects is via the mechanism of phonon blockade. Here, we show that phonon blockade could be achieved in a system of two weakly nonlinear mechanical resonators coupled by a Coulomb interaction. The optimal blockade arises as a result of the destructive quantum interference between paths leading to two-phonon excitation. It is observed that, in comparison to a single drive applied on one mechanical resonator, driving both the resonators can be beneficial in many aspects; such as, in terms of the temperature sensitivity of phonon blockade and also with regard to the tunability, by controlling the amplitude and the phase of the second drive externally. We also show that via a radiation pressure induced coupling in an optomechanical cavity, phonon correlations can be measured indirectly in terms of photon correlations of the cavity mode

    No-Collapse Accurate Quantum Feedback Control via Conditional State Tomography

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    The effectiveness of measurement-based feedback control (MBFC) protocols is hampered by the presence of measurement noise, which affects the ability to accurately infer the underlying dynamics of a quantum system from noisy continuous measurement records to determine an accurate control strategy. To circumvent such limitations, this work explores a real-time stochastic state estimation approach that enables noise-free monitoring of the conditional dynamics including the full density matrix of the quantum system using noisy measurement records within a single quantum trajectory -- a method we name as `conditional state tomography'. This, in turn, enables the development of precise MBFC strategies that lead to effective control of quantum systems by essentially mitigating the constraints imposed by measurement noise and has potential applications in various feedback quantum control scenarios. This approach is particularly useful for reinforcement-learning (RL)-based control, where the RL-agent can be trained with arbitrary conditional averages of observables, and/or the full density matrix as input (observation), to quickly and accurately learn control strategies.Comment: 4 pages, 4 figures + 12 page supplementar

    Zeptometer displacement sensing using cavity opto-magneto-mechanics

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    Optomechanical systems have been proven to be very useful for precision sensing of a variety of forces and effects. In this work, we propose an opto-magno-mechanical setup for spatial displacement sensing where one mirror of the optical cavity is levitated in vacuum via diamagnetic forces in an inhomogenous magnetic field produced by two layers of permanent magnets. We show that the optomechanical system can sense small changes in separation between the magnet layers, as the mechanical frequency of the levitated mirror shifts with changing magnet layer separation dd. We use Quantum Fisher Information (QFI) as a figure of merit of the displacement sensing precision, and study the fundamental precision bound that can be reached in our setup. Nonlinear interaction inherently present in the optomechanical Hamiltonian improves the precision, and we show that in the case of a pure state of the optical cavity, one can achieve extremely small displacement sensing precision of Δd∼36×10−21m\Delta d\sim36\times10^{-21}\text{m}. Further, we incorporate decoherence into our system to study the effect of leaking photons from the optical cavity on the QFI

    Accelerated motional cooling with deep reinforcement learning

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    Achieving fast cooling of motional modes is a prerequisite for leveraging such bosonic quanta for high-speed quantum information processing. In this Letter, we address the aspect of reducing the time limit for cooling, below that constrained by the conventional sideband cooling techniques, and propose a scheme to apply deep reinforcement learning (DRL) to achieve this. In particular, we have numerically demonstrated how the scheme can be used effectively to accelerate the dynamic motional cooling of a macroscopic magnonic sphere, and how it can be uniformly extended to more complex systems, for example, a tripartite opto-magno-mechanical system, to obtain cooling of the motional mode below the time bound of coherent cooling. While conventional sideband cooling methods do not work beyond the well-known rotating wave approximation (RWA) regimes, our proposed DRL scheme can be applied uniformly to regimes operating within and beyond the RWA, and thus, this offers a new and complete toolkit for rapid control and generation of macroscopic quantum states for application in quantum technologies.journal articl

    Optomechanical cooling by STIRAP-assisted energy transfer: an alternative route towards the mechanical ground state

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    Standard optomechanical cooling methods ideally require weak coupling and cavity damping rates which enable the motional sidebands to be well resolved. If the coupling is too large then sideband-resolved cooling is unstable or the rotating wave approximation can become invalid. In this work we describe a protocol to cool a mechanical resonator coupled to a driven optical mode in an optomechanical cavity, which is also coupled to an optical mode in another auxiliary optical cavity, and both the cavities are frequency-modulated. We show that by modulating the amplitude of the drive as well, one can execute a type of STIRAP transfer of occupation from the mechanical mode to the lossy auxiliary optical mode which results in cooling of the mechanical mode. We show how this protocol can outperform normal optomechanical sideband cooling in various regimes such as the strong coupling and the unresolved sideband limit
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