Noise Reversal by Entropy Quantum Computing
- URL: http://arxiv.org/abs/2502.08591v1
- Date: Wed, 12 Feb 2025 17:24:47 GMT
- Title: Noise Reversal by Entropy Quantum Computing
- Authors: Yu-Ping Huang, Yongxiang Hu,
- Abstract summary: In this paper, we explore a hardware-based approach to noise removal using entropy quantum computing.<n>Distinct to any existing de-noising approach, it observes and reproduces the quantum statistical properties of noise in an optical system to emulate and thereby reverse the noise from data.
- Score: 0.6906005491572401
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Signal to noise ratio is key to any measurement. Recent progress in semi/super-conductor technology have pushed the signal detection sensitivity to the ultimate quantum level, but the noise issue remains largely untouched and, in many cases, becomes even more severe because of the high sensitivity. In this paper, we explore a hardware-based approach to noise removal using entropy quantum computing. Distinct to any existing de-noising approach, it observes and reproduces the quantum statistical properties of noise in an optical system to emulate and thereby reverse the noise from data. We show how it can recover 1D and 2D image data mixed with much stronger noise.
Related papers
- Eliminating Incoherent Noise: A Coherent Quantum Approach in Multi-Sensor Dark Matter Detection [6.685649498751827]
We propose a novel dark matter detection scheme by leveraging quantum coherence across a network of multiple quantum sensors.
This method effectively eliminates incoherent background noise, thereby significantly enhancing detection sensitivity.
We present a comprehensive analytical analysis and complement it with practical numerical simulations.
arXiv Detail & Related papers (2024-10-29T18:00:03Z) - Quantum error mitigation for Fourier moment computation [49.1574468325115]
This paper focuses on the computation of Fourier moments within the context of a nuclear effective field theory on superconducting quantum hardware.
The study integrates echo verification and noise renormalization into Hadamard tests using control reversal gates.
The analysis, conducted using noise models, reveals a significant reduction in noise strength by two orders of magnitude.
arXiv Detail & Related papers (2024-01-23T19:10:24Z) - Readout error mitigated quantum state tomography tested on superconducting qubits [0.0]
We test the ability of readout error mitigation to correct realistic noise found in systems composed of quantum two-level objects (qubits)
By treating readout error mitigation in the context of state tomography the method becomes largely readout mode-, architecture-, noise source-, and quantum state-independent.
We identify noise sources for which readout error mitigation worked well, and observed decreases in readout by a factor of up to 30.
arXiv Detail & Related papers (2023-12-07T10:54:17Z) - General noise-resilient quantum amplitude estimation [0.0]
We present a novel algorithm that enhances the estimation of amplitude and observable under noise.
Remarkably, our algorithm exhibits robustness against noise that varies across different depths of the quantum circuits.
arXiv Detail & Related papers (2023-12-02T09:27:40Z) - Noise-Agnostic Quantum Error Mitigation with Data Augmented Neural Models [9.023862258563893]
We build a neural model that achieves quantum error mitigation without prior knowledge of the noise and without training on noise-free data.
Our approach applies to quantum circuits and to the dynamics of many-body and continuous-variable quantum systems.
arXiv Detail & Related papers (2023-11-03T05:52:14Z) - Error-mitigated fermionic classical shadows on noisy quantum devices [0.3775283002059579]
Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed.
We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise.
Our algorithm efficiently estimates $k$-RDMs with $widetildemathcal O(knk)$ state copies and $widetildemathcal O(sqrtn)$ calibration measurements for GTM noise.
arXiv Detail & Related papers (2023-10-19T13:27:19Z) - Scalable noisy quantum circuits for biased-noise qubits [37.69303106863453]
We consider biased-noise qubits affected only by bit-flip errors, which is motivated by existing systems of stabilized cat qubits.
For realistic noise models, phase-flip will not be negligible, but in the Pauli-Twirling approximation, we show that our benchmark could check the correctness of circuits containing up to $106$ gates.
arXiv Detail & Related papers (2023-05-03T11:27:50Z) - Suppressing Amplitude Damping in Trapped Ions: Discrete Weak
Measurements for a Non-unitary Probabilistic Noise Filter [62.997667081978825]
We introduce a low-overhead protocol to reverse this degradation.
We present two trapped-ion schemes for the implementation of a non-unitary probabilistic filter against amplitude damping noise.
This filter can be understood as a protocol for single-copy quasi-distillation.
arXiv Detail & Related papers (2022-09-06T18:18:41Z) - Learning Noise via Dynamical Decoupling of Entangled Qubits [49.38020717064383]
Noise in entangled quantum systems is difficult to characterize due to many-body effects involving multiple degrees of freedom.
We develop and apply multi-qubit dynamical decoupling sequences that characterize noise that occurs during two-qubit gates.
arXiv Detail & Related papers (2022-01-26T20:22:38Z) - Rethinking Noise Synthesis and Modeling in Raw Denoising [75.55136662685341]
We introduce a new perspective to synthesize noise by directly sampling from the sensor's real noise.
It inherently generates accurate raw image noise for different camera sensors.
arXiv Detail & Related papers (2021-10-10T10:45:24Z) - Removing Noise from Extracellular Neural Recordings Using Fully
Convolutional Denoising Autoencoders [62.997667081978825]
We propose a Fully Convolutional Denoising Autoencoder, which learns to produce a clean neuronal activity signal from a noisy multichannel input.
The experimental results on simulated data show that our proposed method can improve significantly the quality of noise-corrupted neural signals.
arXiv Detail & Related papers (2021-09-18T14:51:24Z) - Achieving fault tolerance against amplitude-damping noise [1.7289359743609742]
We develop a protocol for fault-tolerant encoded quantum computing components in the presence of amplitude-damping noise.
We describe a universal set of fault-tolerant encoded gadgets and compute the pseudothreshold for the noise.
Our work demonstrates the possibility of applying the ideas of quantum fault tolerance to targeted noise models.
arXiv Detail & Related papers (2021-07-12T14:59:54Z) - Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies.
We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived.
This is the first quantum protocol that can be used against the most general adversaries.
arXiv Detail & Related papers (2020-03-20T17:56:14Z)
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.