Optimizing measurement-based cooling by reinforcement learning

Yan, Jia-shun; Jing, Jun

doi:10.1103/physreva.106.033124

Cited by 3 publications

(2 citation statements)

References 51 publications

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“…The algorithm of DPPO is a distributed variant of proximal policy optimization (PPO) [42], in which an updatable policy as an actor is trained to choose the comparatively optimized or correct actions toward the final goal and a critic is trained to evaluate quantitatively if the actions chosen by the policy should be encouraged. In a conventional PPO that was employed in optimizing conditional-measurement-based cooling by reinforcement learning [26], there are two policies and one critic. All of them are constructed by neural networks with individual sets of parameters.…”

Section: Discussionmentioning

confidence: 99%

“…To improve the success probability, a straightforward idea is to reduce the number of projections. Approaches include cooling by one-shot measurement [25], cooling by hybrid measurements with optimized measurement time spacings [26], and cooling with random time spacings [16,19]. An alternative yet surprisingly unexplored idea might be purifying the target system before performing the projective measurements.…”

Section: Introductionmentioning

confidence: 99%

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Nondeterministic efficient cooling with a near-unit probability

Yan¹,

Jing²

2023

Preprint

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Nondeterministic measurement-based cooling is remarkable in the average-population-reduction rate but suffers from a limited success probability of finding the target system in the ground state. In this work, we exploit the population-transfer mechanisms of both conditional and unconditional measurements and propose a two-step qubit-assisted protocol allowing to cool a resonator down to its ground state with a near-unit probability. In the first step, the unconditional measurements on the ancillary qubit are utilized to reshape the target resonator from a thermal state to a reserved Fock state. The measurement sequence is optimized by reinforcement learning for a maximum fidelity. In the second step, the population transfer between neighboring Fock states can be faithfully realized by the conditional measurements on the qubit. The population over the reserved state is then transferred in a step-by-step way toward the resonator's ground state with a near-unit fidelity. Intrinsic nondeterminacy of the projection-based manipulation is effectively inhibited by optimizing the measurement time-spacing. Through our protocol with dozens of measurements, the initial thermal average occupation can be reduced by five orders in magnitude with a success probability over 95%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nondeterministic efficient cooling with a near-unit probability

Yan¹,

Jing²

2023

Preprint

View full text Add to dashboard Cite

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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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Generating entangled states from coherent states in circuit QED

Jing

2023

Phys. Rev. A

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Optimizing measurement-based cooling by reinforcement learning

Cited by 3 publications

References 51 publications

Nondeterministic efficient cooling with a near-unit probability

Nondeterministic efficient cooling with a near-unit probability

An Almost Deterministic Cooling by Measurements

Generating entangled states from coherent states in circuit QED

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