Simultaneous cooling by measuring one ancillary system

Yan, Jia-shun; Jing, Jun

doi:10.1103/physreva.105.052607

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…In regard of the time-dependence of the interval τ , the conditional cooling protocols can be categorized into the equal-time-spacing and unequal-time-spacing strategies [18,23]. The unequal-time-spacing strategy has demonstrated a dramatic advantage on the cooling performance by setting the measurement interval as the inverse of the thermal Rabi frequency τ c opt (t) = 1/Ω th (t), where Ω th (t) ≡ g n(t) = g n np n (t) with p n (t) denoting the current population of the resonator on the Fock state |n .…”

Section: Conditional and Unconditional Measurements A Conditional Mea...mentioning

confidence: 99%

“…Analogous to the CM case [23], the cooling efficiency of the UM strategy depends severely on the choice of τ between neighboring measurements. That could be observed in Fig.…”

Section: B Unconditional Measurementmentioning

confidence: 99%

“…Similar to the optimized interval τ c opt (t) for the conditional measurement strategy [23], here τ u opt is also updatable by substituting the time-evolved Ω d and Ω d+1 to Eq. ( 14).…”

Section: B Unconditional Measurementmentioning

confidence: 99%

“…In another word, the resonator is gradually steered by the outcomes of the conditional measurement (CM) to its ground state via dynamically filtering out its vibrational modes. Ranging from cooling the nonlinear mechanical resonators [20], cooling by one shot measurement [21], expanding cooling range by an * Email address: jingjun@zju.edu.cn external driving [22], to accelerating cooling rate by optimizing the measurement intervals [23], an unexplored weakness of all these CM strategies is their small successful probability inherited from the projective operation (postselection). An inevitable amount of cost rises with more samples in ensemble.…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, in a typical measurement-based cooling model, i.e., the Jaynes-Cummings (JC) model, where a mechanical resonator (the target system) is coupled to a qubit (the detector system), the conditional and unconditional measurements are alternatively performed to cool down the resonator to its ground state. In parallel to the optimized measurement-interval obtained for CM [23], we here analytically derive the optimized interval of UM. Then the free-evolution intervals between any neighboring measurements, either UM or CM, can be optimized for cooling.…”

Section: Introductionmentioning

confidence: 99%

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Optimize cooling-by-measurement by reinforcement learning

Yan,

Jing

2022

Preprint

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Cooling by the conditional measurement demonstrates a transparent advantage over that by the unconditional counterpart on the average-population-reduction rate. This advantage, however, is blemished by few percentage of the successful probability of finding the detector system in the measured state. In this work, we propose an optimized architecture to cool down a target resonator, which is initialized as a thermal state, using an interpolation of the conditional and unconditional measurement strategies. Analogous to the conditional measurement, an optimal measurementinterval τ u opt for the unconditional (nonselective) measurement is analytically found for the first time, which is inversely proportional to the collective dominant Rabi frequency Ω d as a function of the resonator's population at the end of the last round. A cooling algorithm under the global optimization by the reinforcement learning results in the maximum value for the cooperative cooling performance, an indicator function to quantify the comprehensive cooling efficiency for arbitrary cooling-by-measurement architecture. In particular, the average population of the target resonator under only 16 rounds of measurements can be reduced by over four orders in magnitude with a successful probability about 30%.

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Section: Conditional and Unconditional Measurements A Conditional Mea...mentioning

confidence: 99%

“…Analogous to the CM case [23], the cooling efficiency of the UM strategy depends severely on the choice of τ between neighboring measurements. That could be observed in Fig.…”

Section: B Unconditional Measurementmentioning

confidence: 99%

Section: B Unconditional Measurementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimize cooling-by-measurement by reinforcement learning

Yan,

Jing

2022

Preprint

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An Almost Deterministic Cooling by Measurements

Yan,

Jing

2024

Adv Quantum Tech

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Nondeterministic measurement‐based techniques are efficient in reshaping the population distribution of a quantum system but suffer from a limited success probability of holding the system in the target state. To save the experimental cost, a two‐step protocol is proposed to cool a resonator down to the ground state with a near‐unit probability by exploiting the state‐engineering mechanisms of both conditional and unconditional measurements on an ancillary qubit. In the first step, the unconditional measurements on the ancillary qubit are applied to reshape the target resonator from a thermal state to a reserved Fock state. The measurement sequence can be efficiently optimized by reinforcement learning for maximum fidelity. In the second step, the population on the reserved state can be faithfully transferred in a stepwise way to the resonator's ground state with a near‐unit fidelity by the conditional measurements on the qubit. Properly designing the projection operator and the measurement interval enables the Kraus operator to act as a lowering operator for neighboring Fock states. Through dozens of measurements in all, the initial thermal average population of the resonator can be reduced by five orders in magnitude with a success probability of over 95%.

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