Quantum generative adversarial learning in photonics
- URL: http://arxiv.org/abs/2310.00585v1
- Date: Sun, 1 Oct 2023 06:08:21 GMT
- Title: Quantum generative adversarial learning in photonics
- Authors: Yizhi Wang, Shichuan Xue, Yaxuan Wang, Yong Liu, Jiangfang Ding, Weixu
Shi, Dongyang Wang, Yingwen Liu, Xiang Fu, Guangyao Huang, Anqi Huang,
Mingtang Deng, Junjie Wu
- Abstract summary: We experimentally demonstrate the QGAN model in photonics for the first time, and investigate the effects of noise and defects on its performance.
Our results show that QGANs can generate high-quality quantum data with a fidelity higher than 90%, even under conditions where up to half of the generator's phase shifters are damaged.
Our work sheds light on the feasibility of implementing QGANs on NISQ-era quantum hardware.
- Score: 12.012483529392465
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum Generative Adversarial Networks (QGANs), an intersection of quantum
computing and machine learning, have attracted widespread attention due to
their potential advantages over classical analogs. However, in the current era
of Noisy Intermediate-Scale Quantum (NISQ) computing, it is essential to
investigate whether QGANs can perform learning tasks on near-term quantum
devices usually affected by noise and even defects. In this Letter, using a
programmable silicon quantum photonic chip, we experimentally demonstrate the
QGAN model in photonics for the first time, and investigate the effects of
noise and defects on its performance. Our results show that QGANs can generate
high-quality quantum data with a fidelity higher than 90\%, even under
conditions where up to half of the generator's phase shifters are damaged, or
all of the generator and discriminator's phase shifters are subjected to phase
noise up to 0.04$\pi$. Our work sheds light on the feasibility of implementing
QGANs on NISQ-era quantum hardware.
Related papers
- Simulating optically-active spin defects with a quantum computer [3.3011710036065325]
We develop fault-tolerant quantum algorithms to simulate optically active defect states and their radiative emission rates.
We conclude by offering a forward-looking perspective on the potential of quantum computers to enhance quantum sensor capabilities.
arXiv Detail & Related papers (2024-05-21T18:00:02Z) - Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation
via Neural Networks [0.4779196219827508]
Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems.
The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes.
This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
arXiv Detail & Related papers (2024-03-10T15:35:41Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - Quantum artificial vision for defect detection in manufacturing [0.0]
We consider several algorithms for quantum computer vision using Noisy Intermediate-Scale Quantum (NISQ) devices.
We benchmark them for a real problem against their classical counterparts.
This is the first implementation of quantum computer vision systems for a problem of industrial relevance in a manufacturing production line.
arXiv Detail & Related papers (2022-08-09T18:30:23Z) - Quantum Noise Sensing by generating Fake Noise [5.8010446129208155]
We propose a framework to characterize noise in a realistic quantum device.
Key idea is to learn about the noise by mimicking it in a way that one cannot distinguish between the real (to be sensed) and the fake (generated) one.
We find that, when applied to the benchmarking case of Pauli channels, the SuperQGAN protocol is able to learn the associated error rates even in the case of spatially and temporally correlated noise.
arXiv Detail & Related papers (2021-07-19T09:42:37Z) - Entangling Quantum Generative Adversarial Networks [53.25397072813582]
We propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN)
We show that EQ-GAN has additional robustness against coherent errors and demonstrate the effectiveness of EQ-GAN experimentally in a Google Sycamore superconducting quantum processor.
arXiv Detail & Related papers (2021-04-30T20:38:41Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - Experimental Quantum Generative Adversarial Networks for Image
Generation [93.06926114985761]
We experimentally achieve the learning and generation of real-world hand-written digit images on a superconducting quantum processor.
Our work provides guidance for developing advanced quantum generative models on near-term quantum devices.
arXiv Detail & Related papers (2020-10-13T06:57:17Z) - On the learnability of quantum neural networks [132.1981461292324]
We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme.
We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
arXiv Detail & Related papers (2020-07-24T06:34:34Z) - Noise robustness and experimental demonstration of a quantum generative
adversarial network for continuous distributions [0.5249805590164901]
We numerically simulate the noisy hybrid quantum generative adversarial networks (HQGANs) to learn continuous probability distributions.
We also investigate the effect of different parameters on the training time to reduce the computational scaling of the algorithm.
Our results pave the way for experimental exploration of different quantum machine learning algorithms on noisy intermediate scale quantum devices.
arXiv Detail & Related papers (2020-06-02T23:14:45Z)
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.