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Review
Physics, Multidisciplinary
Weikang Li et al.
Summary: Machine learning has achieved significant success in various applications, and its integration with quantum physics opens up new frontiers for quantum machine learning. This review provides a comprehensive overview of quantum classifiers, with a focus on recent advancements. Different quantum classification algorithms are reviewed, along with the introduction of variational quantum classifiers and the challenges they face. The vulnerability of quantum classifiers in adversarial learning and recent experimental progress are also discussed.
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
(2022)
Article
Computer Science, Information Systems
Mensah Kwabena Patrick et al.
Summary: Capsule Networks, as a new sensation in Deep Learning, show better performance in image recognition and other areas compared to Convolutional Neural Networks. However, researchers still need to address the lack of architectural knowledge and inner workings of Capsules.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Physics, Multidisciplinary
Kunal Sharma et al.
Summary: This article analyzes the gradient scaling performance of a recently proposed architecture called dissipative quantum neural networks (DQNNs), and finds that DQNNs can exhibit gradient vanishing. Moreover, we quantitatively bound the scaling of the gradient for DQNNs under different conditions and demonstrate that trainability is not always guaranteed.
PHYSICAL REVIEW LETTERS
(2022)
Article
Physics, Multidisciplinary
Raoul Heese et al.
Article
Quantum Science & Technology
Bobak Toussi Kiani et al.
Summary: This paper introduces a solution to the problem of commonly used distance metrics in machine learning in quantum settings. It proposes a quantum EM distance as a quantum analog to the classical EM distance, which possesses unique properties that make quantum learning more stable and efficient. The paper also presents a quantum Wasserstein generative adversarial network (qWGAN) that takes advantage of the quantum EM distance for learning on quantum data.
QUANTUM SCIENCE AND TECHNOLOGY
(2022)
Article
Quantum Science & Technology
Andrew Arrasmith et al.
Summary: This research investigates the relationship between cost function landscapes of parameterized quantum circuits (PQCs). It is analytically proven that the phenomena of exponentially vanishing gradients, exponential cost concentration about the mean, and the exponential narrowness of minima occur together. The key implication of this result is that BPs can be diagnosed numerically through cost differences instead of computationally expensive gradients.
QUANTUM SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Interdisciplinary Applications
Wenhui Ren et al.
Summary: Quantum computing can enhance machine learning and artificial intelligence, but quantum classifiers are susceptible to adversarial perturbations. Experimental demonstration using programmable superconducting qubits showed that adversarial training can significantly improve the classifiers' resistance to perturbations.
NATURE COMPUTATIONAL SCIENCE
(2022)
Article
Quantum Science & Technology
Junyu Liu et al.
Summary: In this paper, the design and performance prediction of variational quantum circuits for learning and optimization tasks are discussed using the theory of neural tangent kernels. Quantum neural tangent kernels are defined, and dynamical equations for their loss function in optimization tasks are derived. Analytical solutions in the frozen limit and dynamical settings are explored, showing that a hybrid quantum classical neural network has approximate Gaussian behavior.
Article
Physics, Multidisciplinary
Zidu Liu et al.
Summary: Deep quantum neural networks provide a promising way to achieve a quantum learning advantage with noisy intermediate-scale quantum devices. This approach uses deep quantum feed-forward neural networks to represent the mixed states of open quantum many-body systems, and introduces a variational method with quantum derivatives to solve the dynamics and stationary states. The special structure of the quantum networks allows for efficient quantum analog of back-propagation algorithm, resource-saving reuse of hidden qubits, general applicability, and convenient implementation of symmetries.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Quantum Science & Technology
Chen Zhao et al.
Summary: This paper proposes a general scheme to analyze the gradient vanishing phenomenon in training quantum neural networks using the ZX-calculus. By representing integrations as ZX-diagrams and computing them with the ZX-calculus, the barren plateau phenomenon is studied on four concrete quantum neural networks with different structures. It is found that there are barren plateaus for hardware efficient ansatz and MPS-inspired ansatz, while no barren plateau exists for QCNN ansatz and tree tensor network ansatz.
Article
Physics, Multidisciplinary
Yulin Wu et al.
Summary: In this study, a two-dimensional programmable superconducting quantum processor named "Zuchongzhi" with 66 functional qubits was developed and used for random quantum circuits sampling to demonstrate quantum computational advantage. The high-precision and programmable quantum computing platform showed exponential outpacing of classical hardware and algorithmic improvements.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Han-Sen Zhong et al.
Summary: Phase-programmable Gaussian boson sampling (GBS) is a new quantum technology that allows for high-purity and high-efficiency photon sampling through tuning the phase of squeezed input states. The experimental results demonstrate the capability of GBS to pass nonclassicality tests and exhibit nontrivial genuine high-order correlations, indicating robustness against classical simulation schemes.
PHYSICAL REVIEW LETTERS
(2021)
Article
Multidisciplinary Sciences
Samson Wang et al.
Summary: The study demonstrates that local Pauli noise can render VQAs untrainable. It shows that noise-induced barren plateaus cause the gradient to exponentially vanish during the training process.
NATURE COMMUNICATIONS
(2021)
Article
Physics, Multidisciplinary
Arthur Pesah et al.
Summary: Analyzing the gradient scaling in QCNN architecture shows that this type of network does not exhibit barren plateaus, indicating that QCNNs are trainable even with random initialization. This result provides an analytical guarantee for the trainability of quantum neural networks.
Article
Physics, Applied
Zhide Lu et al.
Summary: Neuroevolution is a field that constructs artificial neural networks using evolutionary algorithms inspired by the evolution of brains in nature. A quantum neuroevolution algorithm has been introduced in this paper to autonomously find near-optimal quantum neural networks for different machine-learning tasks. The algorithm establishes a one-to-one mapping between quantum circuits and directed graphs, simplifying the task of finding appropriate gate sequences to searching suitable paths in the corresponding graph as a Markovian process.
PHYSICAL REVIEW APPLIED
(2021)
Article
Physics, Multidisciplinary
A. Uvarov et al.
Summary: The text discusses variational quantum algorithms and the phenomenon of barren plateaus in parametrized quantum circuits, where gradients vanish exponentially. By deriving a lower bound on the variance of the gradient, researchers clarify the conditions under which barren plateaus can occur. The onset of a barren plateau regime is shown to depend on the cost function and the width of the circuit causal cone.
JOURNAL OF PHYSICS A-MATHEMATICAL AND THEORETICAL
(2021)
Article
Quantum Science & Technology
Andrew Arrasmith et al.
Summary: Barren plateau landscapes are shown to significantly impact gradient-based optimizers, and this study confirms that gradient-free optimizers are also unable to solve the barren plateau problem. The research reveals the limitations of gradient-free optimization and sheds light on the challenges of training quantum neural networks in barren plateaus.
Article
Physics, Multidisciplinary
Zoe Holmes et al.
Summary: In this paper, the authors investigate the possibility of using quantum machine learning to study scrambling processes, and demonstrate the difficulty of learning unknown scrambling processes. The study shows that there are limitations in learning unitaries without prior information.
PHYSICAL REVIEW LETTERS
(2021)
Article
Multidisciplinary Sciences
Ming Gong et al.
Summary: The study designed and fabricated a high-fidelity two-dimensional superconducting qubit array, demonstrating single- and double-particle quantum walks, realizing a Mach-Zehnder interferometer, and observing interference fringes. This work represents a milestone in bringing larger-scale quantum applications closer to realization on noisy intermediate-scale quantum processors.
Article
Multidisciplinary Sciences
M. Cerezo et al.
Summary: In this study, the authors rigorously prove that defining cost functions with local observables can avoid the barren plateau problem, while defining them with global observables leads to exponentially vanishing gradients. The results indicate a connection between locality and trainability in variational quantum algorithms (VQAs).
NATURE COMMUNICATIONS
(2021)
Article
Quantum Science & Technology
M. Cerezo et al.
Summary: The phenomenon of barren plateaus in quantum neural networks causes exponential suppression of elements in the Hessian matrix, making estimation at this situation require exponential precision with system size n. This indicates that Hessian-based approaches do not overcome the exponential scaling associated with barren plateaus. Additionally, higher order derivatives are also exponentially suppressed, impacting optimization strategies beyond first-order gradient descent.
QUANTUM SCIENCE AND TECHNOLOGY
(2021)
Article
Physics, Multidisciplinary
Ewin Tang
Summary: The study introduces a new classical algorithm input model that captures the features and nuances of quantum linear algebra algorithms. Through this model, the authors describe classical analogs to quantum algorithms for principal component analysis and nearest-centroid clustering.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Junhua Liu et al.
Summary: The study introduces a hybrid quantum-classical convolutional neural network that can efficiently perform feature mapping on noisy intermediate-scale quantum computers, proposes a framework for automatic computation of loss function gradients, and demonstrates the architecture's potential in surpassing classical CNN in learning accuracy for classification tasks.
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
(2021)
Article
Physics, Multidisciplinary
Weikang Li et al.
Summary: Private distributed learning explores collaborative training of deep networks with private data using quantum protocols, showing potential for handling computationally expensive tasks with privacy guarantees.
SCIENCE CHINA-PHYSICS MECHANICS & ASTRONOMY
(2021)
Article
Physics, Multidisciplinary
Mario Krenn et al.
Summary: Artificial intelligence (AI) has the potential to disrupt physics and science by helping scientists acquire new scientific understanding. The THESEUS algorithm demonstrates how AI can contribute at a conceptual level in physics, specifically in experimental quantum optics, by providing solutions that human scientists can interpret and derive new scientific concepts from.
Article
Quantum Science & Technology
Carlos Ortiz Marrero et al.
Summary: The article discusses how excess entanglement between visible and hidden units in quantum neural networks can hinder learning. Through arguments from quantum thermodynamics, it is shown that the volume law in entanglement entropy is typical and can lead to barren plateaus in the optimization landscape due to entanglement. This could cause both gradient-descent and gradient-free methods to fail.
Review
Physics, Applied
M. Cerezo et al.
Summary: Variational quantum algorithms, utilizing classical optimizers to train parameterized quantum circuits, have emerged as a leading strategy to address the limitations of quantum computing. Despite challenges, they appear to be the best hope for achieving quantum advantage.
NATURE REVIEWS PHYSICS
(2021)
Article
Physics, Multidisciplinary
Taylor L. Patti et al.
Summary: This study reveals the role of entanglement in barren plateaus, proposes techniques to ameliorate them, and emphasizes the impact of entanglement on training and the avoidability of barren plateaus in the learning process.
PHYSICAL REVIEW RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Ramprasaath R. Selvaraju et al.
INTERNATIONAL JOURNAL OF COMPUTER VISION
(2020)
Article
Physics, Multidisciplinary
Andreas Elben et al.
PHYSICAL REVIEW LETTERS
(2020)
Article
Multidisciplinary Sciences
Kerstin Beer et al.
NATURE COMMUNICATIONS
(2020)
Article
Quantum Science & Technology
YaoChong Li et al.
QUANTUM SCIENCE AND TECHNOLOGY
(2020)
Article
Multidisciplinary Sciences
Han-Sen Zhong et al.
Article
Computer Science, Artificial Intelligence
Lifei Wang et al.
NATURE MACHINE INTELLIGENCE
(2020)
Article
Optics
Hideyuki Miyahara et al.
Article
Optics
Nana Liu et al.
Article
Physics, Multidisciplinary
Sirui Lu et al.
PHYSICAL REVIEW RESEARCH
(2020)
Article
Physics, Multidisciplinary
Sankar Das Sarma et al.
Editorial Material
Multidisciplinary Sciences
Samuel G. Finlayson et al.
Article
Multidisciplinary Sciences
Ling Hu et al.
Article
Physics, Multidisciplinary
Iris Cong et al.
Article
Multidisciplinary Sciences
Chao Song et al.
Article
Multidisciplinary Sciences
Frank Arute et al.
Article
Multidisciplinary Sciences
K. Wright et al.
NATURE COMMUNICATIONS
(2019)
Review
Quantum Science & Technology
Marcello Benedetti et al.
QUANTUM SCIENCE AND TECHNOLOGY
(2019)
Article
Physics, Multidisciplinary
Connor T. Hann et al.
PHYSICAL REVIEW LETTERS
(2019)
Article
Multidisciplinary Sciences
Daniel K. Park et al.
SCIENTIFIC REPORTS
(2019)
Article
Quantum Science & Technology
Edward Grant et al.
Review
Physics, Multidisciplinary
Vedran Dunjko et al.
REPORTS ON PROGRESS IN PHYSICS
(2018)
Article
Physics, Multidisciplinary
Mohammad H. Amin et al.
Article
Physics, Multidisciplinary
Seth Lloyd et al.
PHYSICAL REVIEW LETTERS
(2018)
Article
Multidisciplinary Sciences
Jarrod R. McClean et al.
NATURE COMMUNICATIONS
(2018)
Article
Quantum Science & Technology
Edward Grant et al.
NPJ QUANTUM INFORMATION
(2018)
Article
Multidisciplinary Sciences
X. Gao et al.
Article
Quantum Science & Technology
John Preskill
Article
Optics
Pierre-Luc Dallaire-Demers et al.
Review
Multidisciplinary Sciences
Jacob Biamonte et al.
Review
Multidisciplinary Sciences
Yann LeCun et al.
Review
Multidisciplinary Sciences
M. I. Jordan et al.
Article
Physics, Multidisciplinary
Seth Lloyd et al.
Article
Physics, Multidisciplinary
Patrick Rebentrost et al.
PHYSICAL REVIEW LETTERS
(2014)
Article
Multidisciplinary Sciences
Xiaolin Hu et al.
Article
Quantum Science & Technology
Songfeng Lu et al.
QUANTUM INFORMATION PROCESSING
(2014)
Article
Optics
Pietro Smacchia et al.
Review
Multidisciplinary Sciences
H. J. Kimble
Article
Optics
Vittorio Giovannetti et al.
Article
Physics, Multidisciplinary
Vittorio Giovannetti et al.
PHYSICAL REVIEW LETTERS
(2008)
Article
Physics, Mathematical
Dominic W. Berry et al.
COMMUNICATIONS IN MATHEMATICAL PHYSICS
(2007)
Article
Physics, Multidisciplinary
H Buhrman et al.
PHYSICAL REVIEW LETTERS
(2001)
