Recent progress on coherent computation based on quantum squeezing

Lü, Bo; Liu, Lu; Song, Jun-Yang; Wen, Kai; Wang, Chuan

doi:10.1007/s43673-023-00077-4

Cited by 31 publications

(5 citation statements)

References 99 publications

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“…With its scalability to high-dimensional multi-frequency states and the potential for miniaturization through single-mode propagation, quantum information processing in the frequency domain has attracted significant attention, especially in the context of quantum communications 41 . As shown in this study, frequency-domain quantum information processing highlights the compatibility with optical fiber networks, in addition to demonstrating the remarkable stability of our interferometer, a crucial resource in both fundamental research and application 42 – 44 , including quantum communications 45 and linear quantum computing 46 .…”

Section: Discussionmentioning

confidence: 64%

NOON-state interference in the frequency domain

Lee,

Shin,

Park

et al. 2024

Light Sci Appl

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The examination of entanglement across various degrees of freedom has been pivotal in augmenting our understanding of fundamental physics, extending to high dimensional quantum states, and promising the scalability of quantum technologies. In this paper, we demonstrate the photon number path entanglement in the frequency domain by implementing a frequency beam splitter that converts the single-photon frequency to another with 50% probability using Bragg scattering four-wave mixing. The two-photon NOON state in a single-mode fiber is generated in the frequency domain, manifesting the two-photon interference with two-fold enhanced resolution compared to that of single-photon interference, showing the outstanding stability of the interferometer. This successful translation of quantum states in the frequency domain will pave the way toward the discovery of fascinating quantum phenomena and scalable quantum information processing.

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Section: Discussionmentioning

confidence: 64%

NOON-state interference in the frequency domain

Lee,

Shin,

Park

et al. 2024

Light Sci Appl

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“…Furthermore, it is noteworthy that quantum neural networks can be efficiently simulated in coherent Ising machines (CIMs) [57]. Additionally, there are other noteworthy advancements in the realm of quantum neural networks, as evidenced by studies such as those conducted by Hou et al [58], Zhao et al [59], Zhou et al [60], Ding et al [61], Tian et al [62], and a comprehensive review by Jeswal et al [63].…”

Section: Quantum Neural Networkmentioning

confidence: 99%

Brain-Inspired Agents for Quantum Reinforcement Learning

Andrés,

Cuéllar,

Navarro

2024

Mathematics

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In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quantum spiking neural networks (QSNN) and quantum long short-term memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conducted a comparative analysis of our model against existing quantum models, including quantum neural networks (QNNs), QLSTM, QSNN and their classical counterparts, elucidating its unique contributions. Through empirical results, we demonstrated the effectiveness of utilizing quantum models inspired by the brain, which outperform the classical approaches and other quantum models in optimizing energy use case. Specifically, in terms of average, best and worst total reward, test reward, robustness, and learning curve.

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“…h i is the bias term provided by the external magnetic field, and J i,j captures the coefficients for the coupling between qubits [18]. Solving for the ground state of an Ising model is NP-hard, but by taking advantage of the QPU's ability to better simulate quantum systems, we can solve this problem more efficiently [19].…”

Section: Quantum Boltzmann Machinementioning

confidence: 99%

Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis

Jain,

Geraci,

Ruda

2023

Technologies

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The field of computer vision has long grappled with the challenging task of image synthesis, which entails the creation of novel high-fidelity images. This task is underscored by the Generative Learning Trilemma, which posits that it is not possible for any image synthesis model to simultaneously excel at high-quality sampling, achieve mode convergence with diverse sample representation, and perform rapid sampling. In this paper, we explore the potential of Quantum Boltzmann Machines (QBMs) for image synthesis, leveraging the D-Wave 2000Q quantum annealer. We undertake a comprehensive performance assessment of QBMs in comparison to established generative models in the field: Restricted Boltzmann Machines (RBMs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Denoising Diffusion Probabilistic Models (DDPMs). Our evaluation is grounded in widely recognized scoring metrics, including the Fréchet Inception Distance (FID), Kernel Inception Distance (KID), and Inception Scores. The results of our study indicate that QBMs do not significantly outperform the conventional models in terms of the three evaluative criteria. Moreover, QBMs have not demonstrated the capability to overcome the challenges outlined in the Trilemma of Generative Learning. Through our investigation, we contribute to the understanding of quantum computing’s role in generative learning and identify critical areas for future research to enhance the capabilities of image synthesis models.

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Recent progress on coherent computation based on quantum squeezing

Cited by 31 publications

References 99 publications

NOON-state interference in the frequency domain

NOON-state interference in the frequency domain

Brain-Inspired Agents for Quantum Reinforcement Learning

Comparing Classical and Quantum Generative Learning Models for High-Fidelity Image Synthesis

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