Globally optimal interferometry with lossy twin Fock probes
- URL: http://arxiv.org/abs/2308.05871v2
- Date: Wed, 15 May 2024 21:27:36 GMT
- Title: Globally optimal interferometry with lossy twin Fock probes
- Authors: T. J. Volkoff, Changhyun Ryu,
- Abstract summary: We show that a method of moments readout of two quadratic spin observables $J_z2$ and $J_+2+J_-2$ is globally optimal for Dicke state probes.
In the lossy setting, we derive the time-inhomogeneous Markov process describing the effect of particle loss on twin Fock states.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Parity or quadratic spin (e.g., $J_{z}^{2}$) readouts of a Mach-Zehnder (MZ) interferometer probed with a twin Fock input state allow to saturate the optimal sensitivity attainable among all mode-separable states with a fixed total number of particles, but only when the interferometer phase $\theta$ is near zero. When more general Dicke state probes are used, the parity readout saturates the quantum Fisher information (QFI) at $\theta=0$, whereas better-than-standard quantum limit performance of the $J_{z}^{2}$ readout is restricted to an $o(\sqrt{N})$ occupation imbalance. We show that a method of moments readout of two quadratic spin observables $J_{z}^{2}$ and $J_{+}^{2}+J_{-}^{2}$ is globally optimal for Dicke state probes, i.e., the error saturates the QFI for all $\theta$. In the lossy setting, we derive the time-inhomogeneous Markov process describing the effect of particle loss on twin Fock states, showing that method of moments readout of four at-most-quadratic spin observables is sufficient for globally optimal estimation of $\theta$ when two or more particles are lost. The analysis culminates in a numerical calculation of the QFI matrix for distributed MZ interferometry on the four mode state $\vert {N\over 4},{N\over 4},{N\over 4},{N\over 4}\rangle$ and its lossy counterparts, showing that an advantage for estimation of any linear function of the local MZ phases $\theta_{1}$, $\theta_{2}$ (compared to independent probing of the MZ phases by two copies of $\vert {N\over 4},{N\over 4}\rangle$) appears when more than one particle is lost.
Related papers
- Distributed quantum multiparameter estimation with optimal local measurements [0.0]
We study a sensor made by an array of $d$ spatially-distributed Mach-Zehnder interferometers (MZIs)
We show that local measurements, independently performed on each MZI, are sufficient to provide a sensitivity saturating the quantum Cram'er-Rao bound.
We find that the $d$ independent interferometers can achieve the same sensitivity of the entangled protocol but at the cost of using additional $d$ non-classical states.
arXiv Detail & Related papers (2024-05-28T17:45:07Z) - Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks [54.177130905659155]
Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks.
In this paper, we study a suitable function space for over- parameterized two-layer neural networks with bounded norms.
arXiv Detail & Related papers (2024-04-29T15:04:07Z) - Fundamental limits of metrology at thermal equilibrium [0.0]
We consider the estimation of an unknown parameter $theta$ through a quantum probe at thermal equilibrium.
We find the maximal Quantum Fisher Information attainable via arbitrary $HC$, which provides a fundamental bound on the measurement precision.
arXiv Detail & Related papers (2024-02-09T18:01:45Z) - A Law of Robustness beyond Isoperimetry [84.33752026418045]
We prove a Lipschitzness lower bound $Omega(sqrtn/p)$ of robustness of interpolating neural network parameters on arbitrary distributions.
We then show the potential benefit of overparametrization for smooth data when $n=mathrmpoly(d)$.
We disprove the potential existence of an $O(1)$-Lipschitz robust interpolating function when $n=exp(omega(d))$.
arXiv Detail & Related papers (2022-02-23T16:10:23Z) - Multiparameter simultaneous optimal estimation with an SU(2) coding
unitary evolution [5.789743084845758]
In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal estimation of multiple parameters is difficult.
We propose a method, characterized by the nested cross-products of the coefficient vector $mathbfX$ of $SU(2)$ generators.
Our work reveals that quantum control is not always functional in improving the estimation precision.
arXiv Detail & Related papers (2022-02-08T06:05:20Z) - Random quantum circuits transform local noise into global white noise [118.18170052022323]
We study the distribution over measurement outcomes of noisy random quantum circuits in the low-fidelity regime.
For local noise that is sufficiently weak and unital, correlations (measured by the linear cross-entropy benchmark) between the output distribution $p_textnoisy$ of a generic noisy circuit instance shrink exponentially.
If the noise is incoherent, the output distribution approaches the uniform distribution $p_textunif$ at precisely the same rate.
arXiv Detail & Related papers (2021-11-29T19:26:28Z) - Extreme expected values and their applications in quantum information
processing [7.4733340808812505]
We consider the probability distribution when the monotonic function $F(X)$ of the independent variable $X$ takes the maximum or minimum expected value.
We apply the proved theory to solve three problems in quantum information processing.
arXiv Detail & Related papers (2021-10-31T11:10:39Z) - Heisenberg-limited estimation robust to detector inefficiency in a
multi-parameter Mach-Zehnder network with squeezed light [0.0]
A simple and intuitive geometrical picture of the state evolution is provided by the Wigner functions of the state at each interferometer output channel.
The protocol allows to detect the value of the sum $beta=frac12(varphi_2)+theta_mathrmin-theta_mathrmout$.
arXiv Detail & Related papers (2021-04-06T10:46:29Z) - A Random Matrix Analysis of Random Fourier Features: Beyond the Gaussian
Kernel, a Precise Phase Transition, and the Corresponding Double Descent [85.77233010209368]
This article characterizes the exacts of random Fourier feature (RFF) regression, in the realistic setting where the number of data samples $n$ is all large and comparable.
This analysis also provides accurate estimates of training and test regression errors for large $n,p,N$.
arXiv Detail & Related papers (2020-06-09T02:05:40Z) - Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and
Variance Reduction [63.41789556777387]
Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP)
We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
arXiv Detail & Related papers (2020-06-04T17:51:00Z) - Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings.
In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$.
We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
arXiv Detail & Related papers (2020-02-25T19:18:36Z)
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.