Quantum readout of imperfect classical data
- URL: http://arxiv.org/abs/2202.01190v2
- Date: Fri, 4 Feb 2022 17:25:42 GMT
- Title: Quantum readout of imperfect classical data
- Authors: Giuseppe Ortolano and Ivano Ruo-Berchera
- Abstract summary: We propose an optimized quantum sensing protocol to maximize the readout accuracy in presence of imprecise writing.
This work have implications for identification of pattern in biological system, in spectrophotometry, and whenever the information can be extracted from a transmission/reflection optical measurement.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The encoding of classical data in a physical support can be done up to some
level of accuracy due to errors and the imperfection of the writing process.
Moreover, some degradation of the storage data can happen over the time because
of physical or chemical instability of the system. Any read-out strategy should
take into account this natural degree of uncertainty and minimize its effect.
An example are optical digital memories, where the information is encoded in
two values of reflectance of a collection of cells. Quantum reading using
entanglement, has been shown to enhances the readout of an ideal optical
memory, where the two level are perfectly characterized. In this work, we will
analyse the case of imperfect construction of the memory and propose an
optimized quantum sensing protocol to maximize the readout accuracy in presence
of imprecise writing. The proposed strategy is feasible with current technology
and is relatively robust to detection and optical losses. Beside optical
memories, this work have implications for identification of pattern in
biological system, in spectrophotometry, and whenever the information can be
extracted from a transmission/reflection optical measurement.
Related papers
- Optical aberrations in autonomous driving: Physics-informed parameterized temperature scaling for neural network uncertainty calibration [49.03824084306578]
We propose to incorporate a physical inductive bias into the neural network calibration architecture to enhance the robustness and the trustworthiness of the AI target application.
We pave the way for a trustworthy uncertainty representation and for a holistic verification strategy of the perception chain.
arXiv Detail & Related papers (2024-12-18T10:36:46Z) - Optical Quantum Sensing for Agnostic Environments via Deep Learning [59.088205627308]
We introduce an innovative Deep Learning-based Quantum Sensing scheme.
It enables optical quantum sensors to attain Heisenberg limit (HL) in agnostic environments.
Our findings offer a new lens through which to accelerate optical quantum sensing tasks.
arXiv Detail & Related papers (2023-11-13T09:46:05Z) - Nonlinear optical encoding enabled by recurrent linear scattering [16.952531256252744]
We introduce a design that passively induce optical nonlinear random mapping with a continuous-wave laser at a low power.
We demonstrate that our design retains vital information even when the readout dimensionality is reduced.
This capability allows our optical platforms to offer efficient optical information processing solutions across applications.
arXiv Detail & Related papers (2023-07-17T15:15:47Z) - The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
We introduce a data efficient neural decoder that exploits the symmetries of the problem.
We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
arXiv Detail & Related papers (2023-04-14T19:46:39Z) - Quantum-enhanced pattern recognition [0.0]
We show for the first time quantum advantage in the multi-cell problem of pattern recognition.
We use entangled probe states and photon-counting to achieve quantum advantage in classification error over that achieved with classical resources.
This motivates future developments of quantum-enhanced pattern recognition of bosonic-loss within complex domains.
arXiv Detail & Related papers (2023-04-12T13:06:38Z) - Back action evasion in optical lever detection [0.0]
In general, any precision optical measurement is accompanied by optical force induced disturbance leading to a standard quantum limit(mechanical)
Here we give a simple description of how such back action can be evaded in optical lever detection to beat the quantum limit.
We achieve a readout noise floor two orders of magnitude lower than the quantum limit.
arXiv Detail & Related papers (2022-12-15T23:43:15Z) - Quantum-tailored machine-learning characterization of a superconducting
qubit [50.591267188664666]
We develop an approach to characterize the dynamics of a quantum device and learn device parameters.
This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data.
This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
arXiv Detail & Related papers (2021-06-24T15:58:57Z) - Relaxation times do not capture logical qubit dynamics [50.04886706729045]
We show that spatial noise correlations can give rise to rich and counter-intuitive dynamical behavior of logical qubits.
This work will help to guide and benchmark experimental implementations of logical qubits.
arXiv Detail & Related papers (2020-12-14T19:51:19Z) - Rapid characterisation of linear-optical networks via PhaseLift [51.03305009278831]
Integrated photonics offers great phase-stability and can rely on the large scale manufacturability provided by the semiconductor industry.
New devices, based on such optical circuits, hold the promise of faster and energy-efficient computations in machine learning applications.
We present a novel technique to reconstruct the transfer matrix of linear optical networks.
arXiv Detail & Related papers (2020-10-01T16:04:22Z) - Non-Markovian effect on quantum optical metrology under dissipative
environment [1.6058099298620423]
Non-Markovian effects are shown to be effective in performing quantum optical metrology under locally dissipative environments.
Our work provides a recipe to realize ultrasensitive measurements in the presence of noise by utilizing non-Markovian effects.
arXiv Detail & Related papers (2020-02-09T14:50:54Z)
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.