Realization of a quantum autoencoder for lossless compression of quantum data

Huang, Changjiang; Ma, Hailan; Yin, Qi; Tang, Jun-Feng; Dong, Dezun; Chen, Chunlin; Xiang, Guo-Yong; Li, Chuan‐Feng; Guo, Guang-Can

doi:10.1103/physreva.102.032412

Cited by 45 publications

(20 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Since any binary quantum alternative of a photon can serve as a qubit, the polarization and path degrees of freedom can serve as two qubits. We follow the scheme in [20] to generate 2-qubit quantum states. As in Fig.…”

Section: B Results For Quantum Optical Statesmentioning

confidence: 99%

“…Owing to its intrinsic capability of exacting information from high-dimensional data [12], [13], machine learning (ML) has been recently utilized to address various quantum tasks [14]. Among them, the representation of many-body quantum states [15], the verification of quantum devices [16], quantum error correction [17], quantum manipulation [18], [19] and quantum data compression [20], [21] have been investigated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparative study on how neural networks enhance quantum state tomography

Ma¹,

Dong²,

Petersen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Quantum state tomography aiming at reconstructing the density matrix of a quantum state plays an important role in various emerging quantum technologies. Inspired by the intuition that machine learning has favorable robustness and generalization, we propose a deep neural networks based quantum state tomography (DNN-QST) approach, that can be applied to three cases, including few measurement copies and incomplete measurements as well as noisy measurements. Numerical results demonstrate that DNN-QST exhibits a great potential to achieve high fidelity for quantum state tomography with limited measurement resources and can achieve improved estimation when tomographic measurements suffer from noise. In addition, the results for 2-qubit states from quantum optical devices demonstrate the generalization of DNN-QST and its robustness against possible error in the experimental devices.

show abstract

Section: B Results For Quantum Optical Statesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A comparative study on how neural networks enhance quantum state tomography

Ma¹,

Dong²,

Petersen³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…A further enticing extension would be to the case where only near-faithful execution of the strategy is requiredthat is, some error is tolerated [49,53,54]. Our quantum agents bear a similarity to models of quantum walks with memory [55][56][57] and other instances of memory compression through quantum processing [58,59] such as quantum auto-encoders [60][61][62][63]. Moreover, our general form for quantum adaptive agents Eq.…”

Section: Discussionmentioning

confidence: 99%

Quantum adaptive agents with efficient long-term memories

Elliott,

Gu,

Garner

et al. 2021

Preprint

View full text Add to dashboard Cite

Central to the success of adaptive systems is their ability to interpret signals from their environment and respond accordingly -they act as agents interacting with their surroundings. Such agents typically perform better when able to execute increasingly complex strategies. This comes with a cost: the more information the agent must recall from its past experiences, the more memory it will need. Here we investigate the power of agents capable of quantum information processing. We uncover the most general form a quantum agent need adopt to maximise memory compression advantages, and provide a systematic means of encoding their memory states. We show these encodings can exhibit extremely favourable scaling advantages relative to memory-minimal classical agents when information must be retained about events increasingly far into the past.

show abstract

“…Furthermore, QAEs have been proposed to denoise specific quantum states such as GHZ-or W-states [59][60][61]. Compression of quantum data using QAEs has already been achieved in experiments using single photons [62,63] or superconducting qubits [64]. In other works, certain types of quantum neural networks were proposed to find suitable encodings of quantum information into logical states that allow for hardware-specific noise to be corrected [65,66].…”

Section: Introductionmentioning

confidence: 99%

Quantum Error Correction with Quantum Autoencoders

Locher¹,

Cardarelli²,

Müller³

2022

Preprint

View full text Add to dashboard Cite

Active quantum error correction is a central ingredient to achieve robust quantum processors. In this paper we investigate the potential of quantum machine learning for quantum error correction. Specifically, we demonstrate how quantum neural networks, in the form of quantum autoencoders, can be trained to learn optimal strategies for active detection and correction of errors, including spatially correlated computational errors as well as qubit losses. We highlight that the denoising capabilities of quantum autoencoders are not limited to the protection of specific states but extend to the entire logical codespace. We also show that quantum neural networks can be used to discover new logical encodings that are optimally adapted to the underlying noise. Moreover, we find that, even in the presence of moderate noise in the quantum autoencoders themselves, they may still be successfully used to perform beneficial quantum error correction.

show abstract

Realization of a quantum autoencoder for lossless compression of quantum data

Cited by 45 publications

References 40 publications

A comparative study on how neural networks enhance quantum state tomography

A comparative study on how neural networks enhance quantum state tomography

Quantum adaptive agents with efficient long-term memories

Quantum Error Correction with Quantum Autoencoders

Contact Info

Product

Resources

About