A protocol for global multiphase estimation
- URL: http://arxiv.org/abs/2301.07380v2
- Date: Thu, 19 Jan 2023 15:39:30 GMT
- Title: A protocol for global multiphase estimation
- Authors: Giovanni Chesi, Roberto Rubboli, Alberto Riccardi, Lorenzo Maccone and
Chiara Macchiavello
- Abstract summary: We devise a global multiphase protocol based on Holevo's estimation theory.
We show that in the multiphase strategy there is only a constant quantum advantage with respect to a sequence of independent single-phase estimations.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Global estimation strategies allow to extract information on a phase or a set
of phases without any prior knowledge, which is, instead, required for local
estimation strategies. We devise a global multiphase protocol based on Holevo's
estimation theory and apply it to the case of digital estimation, i.e. we
estimate the phases in terms of the mutual information between them and the
corresponding estimators. In the single-phase scenario, the protocol
encompasses two specific known optimal strategies. We extend them to the
simultaneous estimation of two phases and evaluate their performance. Then, we
retrieve the ultimate digital bound on precision when a generic number of
phases is simultaneously estimated. We show that in the multiphase strategy
there is only a constant quantum advantage with respect to a sequence of
independent single-phase estimations. This extends a recent similar result,
which settled a controversy on the search for the multiphase enhancement.
Related papers
- Coherence as a resource for phase estimation [0.8192907805418583]
We quantitatively connect the performance of phase estimation protocols with quantum coherence.<n>We construct a family of coherence measures that directly connect a state's coherence with its value for phase estimation.<n>This establishes coherence as an essential resource for phase estimation and, thus, for any quantum technology relying on it as a subroutine.
arXiv Detail & Related papers (2025-05-24T06:18:15Z) - Quantum multiphase estimation [0.23436632098950458]
This work reviews recent theoretical and experimental advancements in the parallel estimation of multiple arbitrary phases.
We highlight strategies for constructing optimal measurement protocols and discuss the experimental platforms best suited for implementing these techniques.
arXiv Detail & Related papers (2025-02-11T19:00:00Z) - Sequential information theoretic protocols in continuous variable systems [0.0]
We propose schemes for resource reusability, resource-splitting protocol and unsharp homodyne measurements.
We demonstrate the advantage offered by the first scheme in implementing sequential attempts at continuous variable teleportation when the protocol fails in the previous round.
We exhibit that, under specific conditions, it is possible to witness the entanglement of a state an arbitrary number of times via a scheme that differs significantly from any protocol proposed for finite dimensional systems.
arXiv Detail & Related papers (2024-10-19T08:23:16Z) - Experimental benchmarking of quantum state overlap estimation strategies with photonic systems [17.062416865186307]
We compare four strategies for overlap estimation, including tomography-tomography, tomography-projection, Schur collective measurement and optical swap test.
With a photonic system, the overlap-dependent estimation precision for each strategy is quantified in terms of the average estimation variance over uniformly sampled states.
Our results shed new light on extracting the parameter of interest from quantum systems, prompting the design of efficient quantum protocols.
arXiv Detail & Related papers (2024-06-10T21:33:10Z) - Hyperparameters in Continual Learning: A Reality Check [53.30082523545212]
Continual learning (CL) aims to train a model on a sequence of tasks while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge)
The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyper parameters in a given scenario and then evaluates the algorithms in the same scenario.
This protocol has significant shortcomings: it overestimates the CL capacity of algorithms and relies on unrealistic hyper parameter tuning.
We argue that the evaluation of CL algorithms should focus on assessing the generalizability of their CL capacity to unseen scenarios.
arXiv Detail & Related papers (2024-03-14T03:13:01Z) - Predicting the State of Synchronization of Financial Time Series using
Cross Recurrence Plots [75.20174445166997]
This study introduces a new method for predicting the future state of synchronization of the dynamics of two financial time series.
We adopt a deep learning framework for methodologically addressing the prediction of the synchronization state.
We find that the task of predicting the state of synchronization of two time series is in general rather difficult, but for certain pairs of stocks attainable with very satisfactory performance.
arXiv Detail & Related papers (2022-10-26T10:22:28Z) - Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency [105.17746223041954]
Reinforcement learning in partially observed Markov decision processes (POMDPs) faces two challenges.
It often takes the full history to predict the future, which induces a sample complexity that scales exponentially with the horizon.
We propose a reinforcement learning algorithm named Embed to Control (ETC), which learns the representation at two levels while optimizing the policy.
arXiv Detail & Related papers (2022-05-26T16:34:46Z) - Reinforcement learning-enhanced protocols for coherent
population-transfer in three-level quantum systems [50.591267188664666]
We deploy a combination of reinforcement learning-based approaches and more traditional optimization techniques to identify optimal protocols for population transfer.
Our approach is able to explore the space of possible control protocols to reveal the existence of efficient protocols.
The new protocols that we identify are robust against both energy losses and dephasing.
arXiv Detail & Related papers (2021-09-02T14:17:30Z) - Multiple-phase quantum interferometry -- real and apparent gains of
measuring all the phases simultaneously [0.0]
We show that the advantage of the optimal simultaneous estimation scheme amounts to a constant factor improvement when compared with schemes where each phase is estimated separately.
We show that the advantage of the optimal simultaneous estimation scheme amounts to a constant factor improvement when compared with schemes where each phase is estimated separately.
arXiv Detail & Related papers (2021-07-22T18:01:13Z) - Statistical Approach to Quantum Phase Estimation [62.92678804023415]
We introduce a new statistical and variational approach to the phase estimation algorithm (PEA)
Unlike the traditional and iterative PEAs which return only an eigenphase estimate, the proposed method can determine any unknown eigenstate-eigenphase pair.
We show the simulation results of the method with the Qiskit package on the IBM Q platform and on a local computer.
arXiv Detail & Related papers (2021-04-21T00:02:00Z) - Bayesian Quantum Multiphase Estimation Algorithm [0.0]
We study a parallel (simultaneous) estimation of multiple arbitrary phases.
The algorithm proves a certain noise resilience and can be implemented using single photons and standard optical elements.
arXiv Detail & Related papers (2020-10-18T19:32:07Z) - Multiphase estimation without a reference mode [0.0]
We show that the absence of an external reference mode reduces the number of simultaneously estimatable parameters.
We also show that the symmetries of the parameters being estimated dictate the symmetries of the optimal probe states.
arXiv Detail & Related papers (2020-06-23T18:00:03Z) - Bottom-Up Temporal Action Localization with Mutual Regularization [107.39785866001868]
State-of-the-art solutions for TAL involve evaluating the frame-level probabilities of three action-indicating phases.
We introduce two regularization terms to mutually regularize the learning procedure.
Experiments are performed on two popular TAL datasets, THUMOS14 and ActivityNet1.3.
arXiv Detail & Related papers (2020-02-18T03:59:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.