Quantum Noise Sensing by Generating Fake Noise

Paolo, Braccia,; Banchi, Leonardo; Caruso, Filippo

doi:10.1103/physrevapplied.17.024002

Cited by 8 publications

(5 citation statements)

References 48 publications

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“…The extension of GANs to quantum data introduces a compelling approach for data synthesis. qGANs employ a generator 𝐺 and a discriminator 𝐷 in a two-player minimax game [80]. Given a quantum dataset ℚ, the generator aims to synthesize quantum states |𝜓 𝑠𝑦𝑛𝑡ℎ,𝑖 ⟩ that resemble the true data, while the discriminator distinguishes between real and synthesized quantum states.…”

Section: Qgms For Data Synthesismentioning

confidence: 99%

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Sihare

2024

Preprint

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This research explores the potential of quantum computing in data analysis, focusing on the efficient analysis of high-dimensional quantum datasets using dimensionality reduction techniques. The study aims to fill the knowledge gap by developing robust quantum dimensionality reduction techniques that can mitigate noise and errors. The research methodology involved a comprehensive review and analysis of existing quantum dimensionality reduction techniques, such as quantum principal component analysis, quantum linear discriminant analysis and quantum generative models. The study also explored the limitations imposed by NISQ devices and proposed strategies to adapt these techniques to work efficiently within these constraints. The key results demonstrate the potential of quantum dimensionality reduction techniques to effectively reduce the dimensionality of high-dimensional quantum datasets while preserving critical quantum information. The evaluation of quantum principal component analysis, quantum linear discriminant analysis and quantum generative models showed their effectiveness in improving quantum data analysis, particularly in improving simulation speed and predicting properties. Despite the challenges posed by noise and errors, robust quantum dimensionality reduction methods showed promise in mitigating these effects and preserving quantum information. Finally, this research contributes to the advancement of quantum data analysis by presenting a comprehensive analysis of quantum dimensionality reduction techniques and their applications. It highlights the importance of developing robust quantum feature learning methods that can operate efficiently in noisy quantum environments, especially in the NISQ era.

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Section: Qgms For Data Synthesismentioning

confidence: 99%

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Sihare

2024

Preprint

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“…discriminator trying to distinguish real states from generated ones [84]. As such, classical and quantum Generative Adversarial Networks (GANs) have been employed for QST [85,86], with fewer measurements required when prior knowledge is available.…”

Section: [N]mentioning

confidence: 99%

“…In cases when one is only interested in some specific properties of ρ, the intractability of full QST can be avoided by tailoring the measurement and postprocessing to the specific property. Most prominently, several methods have been developed to efficiently mea- [87][88][89] and regression [90] of measurement data, and the prediction of quantum dynamics [91], (b) as RBMs for QST [11,80], (c) as GANs for the tomography of quantum states [85] and processes [86], and (d) as NODEs for optimal quantum control [92,93]. Alternatively to NODE, PINNs (that include the differential equation in the cost function) have been used for creating robust quantum gates [94].…”

Section: Extracting Specific Features Of a Quantum Statementioning

confidence: 99%

“…II B), tensor networks can lead to drastically reduced resource requirements [17,137,138]. GAN-based approximations of the superoperator Λ have also been proposed as an efficient method for QPT [86]. Other methods exploit NNs to generalise QPT to the characterisation of time dependent spin systems [139], while yet another class reconstructs a unitary quantum process by inverting the dynamics using a variational algorithm [140,141].…”

Section: A Learning Dynamics With Quantum Process Tomographymentioning

confidence: 99%

“…Different examples of neural networks for learning quantum systems. NNs have been employed after the acquisition of measurement data (i.e., in the post-processing) as (a) feedforward NNs for the classification[87][88][89] and regression[90] of measurement data, and the prediction of quantum dynamics[91], (b) as RBMs for QST[11,80], (c) as GANs for the tomography of quantum states[85] and processes[86], and (d) as NODEs for optimal quantum control[92,93]. Alternatively to NODE, PINNs (that include the differential equation in the cost function) have been used for creating robust quantum gates[94].…”

mentioning

confidence: 99%

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Statistical Complexity of Quantum Learning

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et al. 2024

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Learning problems involve settings in which an algorithm has to make decisions based on data, and possibly side information such as expert knowledge. This study has two main goals. First, it reviews and generalizes different results on the data and model complexity of quantum learning, where the data and/or the algorithm can be quantum, focusing on information‐theoretic techniques. Second, it introduces the notion of copy complexity, which quantifies the number of copies of a quantum state required to achieve a target accuracy level. Copy complexity arises from the destructive nature of quantum measurements, which irreversibly alter the state to be processed, limiting the information that can be extracted about quantum data. As a result, empirical risk minimization is generally inapplicable. The paper presents novel results on the copy complexity for both training and testing. To make the paper self‐contained and approachable by different research communities, an extensive background material is provided on classical results from statistical learning theory, as well as on the distinguishability of quantum states. Throughout, the differences between quantum and classical learning are highlighted by addressing both supervised and unsupervised learning, and extensive pointers are provided to the literature.

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Quantum Noise Sensing by Generating Fake Noise

Cited by 8 publications

References 48 publications

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Utilising Dimensionality Reduction for Improved Data Analysis with Quantum Feature Learning

Learning Quantum Systems

Statistical Complexity of Quantum Learning

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