Article
Quantum Science & Technology
Antonio Macaluso, Matthias Klusch, Stefano Lodi, Claudio Sartori
Summary: This work proposes a universal, efficient framework named Multiple Aggregator Quantum Algorithm (MAQA) that can reproduce the output of various classical supervised machine learning algorithms by utilizing quantum computation advantages. MAQA can be adopted as the quantum counterpart of ensemble algorithms and neural networks. By increasing the depth of the corresponding quantum circuit linearly, MAQA can generate an exponentially large number of different transformations of the input, providing a powerful model for quantum machine learning with computational advantage over classical methods. Additionally, the adoption of MAQA as a hybrid quantum-classical and fault-tolerant quantum algorithm is discussed.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Mathematics
Emanuel Vega, Ricardo Soto, Pablo Contreras, Broderick Crawford, Javier Pena, Carlos Castro
Summary: Population-based approaches offer new search strategies for optimization problems. This work proposes a hybrid architecture that intelligently balances population size by using learning components and statistical modeling methods. It demonstrates the viability and effectiveness of the approach through solving benchmark functions and the multidimensional knapsack problem.
Article
Computer Science, Artificial Intelligence
Chunnan Wang, Hongzhi Wang, Chang Zhou, Hanxiao Chen
Summary: Machine learning models are highly sensitive to hyperparameters and their evaluations can be costly. The ExperienceThinking algorithm proposed in this paper intelligently optimizes hyperparameter settings based on known evaluation information, effectively improving the performance of machine learning models within limited budgets.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Ye Tian, Tao Zhang, Jianhua Xiao, Xingyi Zhang, Yaochu Jin
Summary: This article proposes a coevolutionary framework for constrained multiobjective optimization problems, which demonstrates high competitiveness in experiments compared to other algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Kangjia Qiao, Kunjie Yu, Boyang Qu, Jing Liang, Hui Song, Caitong Yue
Summary: This article presents an evolutionary multitasking-based constrained multiobjective optimization framework for solving CMOPs. It transforms the optimization problem into two related tasks and utilizes a tentative method to discover and transfer useful knowledge. The approach achieves better performance compared to other state-of-the-art algorithms.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Quantum Science & Technology
Pablo Diez-Valle, Jorge Luis-Hita, Senaida Hernandez-Santana, Fernando Martinez-Garcia, Alvaro Diaz-Fernandez, Eva Andres, Juan Jose Garcia-Ripoll, Escolastico Sanchez-Martinez, Diego Porras
Summary: In this paper, we propose a new method for solving combinatorial optimization problems with challenging constraints using Variational Quantum Algorithms (VQAs). We introduce the Multi-Objective Variational Constrained Optimizer (MOVCO) which updates the variational parameters by a classical multi-objective optimization performed by a genetic algorithm. We test this method on a real-world problem in finance and show significant improvement in terms of cost and the avoidance of local minima that do not satisfy mandatory constraints.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Theory & Methods
Huaqing Li, Jinhui Hu, Liang Ran, Zheng Wang, Qingguo Lu, Zhenyuan Du, Tingwen Huang
Summary: This article focuses on a class of totally non-smooth constrained composite optimization problems over multi-agent systems and presents synchronous and asynchronous decentralized dual proximal gradient algorithms. The theoretical and experimental results demonstrate that the algorithms can achieve the globally optimal solution to the problem.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Cynthia Olvera, Oscar Montiel, Yoshio Rubio
Summary: This paper presents a statistical analysis of classical multiobjective evolutionary algorithms and quantum-inspired multiobjective optimization algorithms to determine whether quantum solvers can provide optimal solutions. The results indicate that the quantum-inspired algorithms perform as well as the classical ones, and the proposed method outperforms other quantum-inspired algorithms in problems with constraints and complicated Pareto sets.
Article
Computer Science, Information Systems
Fei Ming, Wenyin Gong, Shuijia Li, Ling Wang, Zuowen Liao
Summary: More attention should be paid to the constrained many-objective optimization problems (CMaOPs). Traditional methods tend to adopt one-by-one selection or deletion strategies, which rarely consider the quality of the entirety. This article proposes a new algorithm that utilizes determinantal point processes (DPPs) to handle CMaOPs and designs novel Kernel Matrices to represent the qualities of solutions in population and archive.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Aljosa Vodopija, Tea Tusar, Bogdan Filipic
Summary: This article extends landscape analysis to constrained multiobjective optimization and proposes a method for characterizing CMOPs. The method is used to compare artificial test suites and real-world problem suites, revealing the limitations of artificial test problems in representing realistic characteristics. The effectiveness of the proposed features in predicting algorithm performance is demonstrated.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Jianhua Jiang, Yutong Liu, Ziying Zhao
Summary: The paper introduces two tree migration mechanisms, Triple Tree-Seed Algorithm outperforms TSA on 30 test functions and proves its applicability in solving practical problems.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Automation & Control Systems
Rishi Sonthalia, Anna C. Gilbert
Summary: This paper introduces a method called "PROJECT AND FORGET" for solving highly constrained convex optimization problems. The authors provide a theoretical analysis and demonstrate through experiments that this method converges to the global optimal solution with a linear rate of convergence.
JOURNAL OF MACHINE LEARNING RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Anupama Padha, Anita Sahoo
Summary: This paper presents a Quantum LSTM-based Contrastive Learning framework for continuous mental health monitoring, which has shown superior performance in time series data analysis through experiments on seven benchmark datasets.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Telecommunications
Zheyu Chen, Kin K. Leung, Shiqiang Wang, Leandros Tassiulas, Kevin Chan, Don Towsley
Summary: Gradient-based iterative algorithms are widely used for optimization problems. This study proposes a learning approach using Coupled Long Short-Term Memory networks (CLSTMs) to quickly generate optimal solutions for constrained optimization problems with varying system parameters. The advantages of this approach include obtaining approximate near-optimal solutions in a few iterations and the ability to generate solutions with different parameter distributions during training. Numerical experiments using Alibaba datasets show that the CLSTMs reach within 90% or better of the corresponding optimum after 11 iterations, with reduced iteration and CPU time compared to gradient descent with momentum.
IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING
(2023)
Article
Physics, Multidisciplinary
Ningping Cao, Jie Xie, Aonan Zhang, Shi-Yao Hou, Lijian Zhang, Bei Zeng
Summary: In this paper, a neural-network-based method for solving the quantum inverse problem (QIP) is proposed. This method takes advantage of the quantumness of QIPs and utilizes the computational power of neural networks to achieve efficient quantum state estimation. The method is tested numerically and experimentally on the problem of maximum entropy estimation and demonstrates high fidelity, efficiency, and robustness.
NEW JOURNAL OF PHYSICS
(2022)
Article
Chemistry, Multidisciplinary
Soham Saha, Mustafa Goksu Ozlu, Sarah N. Chowdhury, Benjamin T. Diroll, Richard D. Schaller, Alexander Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev
Summary: The unique properties of emerging photonic materials, conducting nitrides and oxides, are explored in this study. The optical properties of polycrystalline titanium nitride and aluminum-doped zinc oxide can be controlled by tailoring the film thickness. The study demonstrates their potential for ENZ-enhanced photonic applications, including optical circuitry, tunable metasurfaces, and nonlinear optical devices.
ADVANCED MATERIALS
(2023)
Article
Optics
Colton Fruhling, Kang Wang, Sarah Chowdhury, Xiaohui Xu, Jeffrey Simon, Alexander Kildishev, Letian Dou, Xiangeng Meng, Alexandra Boltasseva, Vladimir M. Shalaev
Summary: Coherent random lasing in subwavelength quasi-2D perovskite films is observed and studied. Statistical analysis reveals Levy-like intensity fluctuations, replica symmetry breaking confirms random lasing, and spectral and spatial correlation techniques are used to study coherent modes. The observed coherent lasing modes are extended states that result from the random crystal grain structure during fabrication and out-compete diffusive lasing due to their coherence.
LASER & PHOTONICS REVIEWS
(2023)
Article
Nanoscience & Nanotechnology
Omer Yesilyurt, Samuel Peana, Vahagn Mkhitaryan, Karthik Pagadala, Vladimir M. M. Shalaev, Alexander V. V. Kildishev, Alexandra Boltasseva
Summary: This work proposes a neural network based inverse design technique for the realistic design and fabrication of single material variable-index multilayer films. By integrating simulated systematic and random errors, the same neural network that produced the ideal designs can be retrained to compensate for deposition errors and fabrication imperfections. This approach provides a practical and experimentally viable method for designing high-performance single material multilayer films for a wide range of applications.
Article
Astronomy & Astrophysics
Adam Z. Kaczmarek, Dominik Szczesniak, Sabre Kais
Summary: We present a systematic and complementary study of quantum correlations near a black hole by considering measurement-induced nonlocality (MIN). The quantum measure of interest is discussed for the fermionic, bosonic and mixed fermion-boson modes on equal footing with respect to the Hawking radiation. The obtained results show that in the infinite Hawking temperature limit, the physically accessible correlations do not vanish only in the fermionic case. However, the higher frequency modes can sustain correlations for the finite Hawking temperature, with mixed systems being more sensitive towards the increase in the fermionic frequencies than the bosonic ones. Since the MIN for the latter modes quickly diminishes, the increased frequency may be a way to maintain nonlocal correlations for the scenarios at the finite Hawking temperature.
Article
Physics, Multidisciplinary
Zixuan Hu, Sabre Kais
Summary: Most existing quantum algorithms are accidental discoveries or adaptations from classical algorithms. There is a need for a systematic theory to understand and design quantum circuits. The authors propose a unitary dependence theory that characterizes the behaviors of quantum circuits and states, offering practical information on measurement and manipulation of qubits, easier generalization to many-qubit systems, and better robustness upon system partitioning.
COMMUNICATIONS PHYSICS
(2023)
Article
Multidisciplinary Sciences
Zhaxylyk A. Kudyshev, Demid Sychev, Zachariah Martin, Omer Yesilyurt, Simeon I. Bogdanov, Xiaohui Xu, Pei-Gang Chen, Alexander V. Kildishev, Alexandra Boltasseva, Vladimir M. Shalaev
Summary: One of the main characteristics of optical imaging systems is spatial resolution, which is restricted by the diffraction limit. Recently, classical and quantum super-resolution techniques have been developed to break the diffraction limit. We propose a machine learning-assisted approach for rapid antibunching super-resolution imaging, achieving a 12 times speed-up compared to conventional methods. This framework enables the practical realization of scalable quantum super-resolution imaging devices compatible with various quantum emitters.
NATURE COMMUNICATIONS
(2023)
Article
Quantum Science & Technology
Amandeep Singh Bhatia, Sabre Kais, Muhammad Ashraful Alam
Summary: In recent years, the concept of federated machine learning has gained traction among scientists to address privacy concerns. The combination of machine learning and quantum computing is a disruptive force in various industries. Researchers have developed a hybrid quantum-classical algorithm called a quanvolutional neural network for efficient execution on quantum hardware. This study evaluates the performance of the algorithm on real-world data partitioned among healthcare institutions/clients, demonstrating its potential benefits in reducing communication rounds and maintaining accuracy.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Chemistry, Physical
Sangchul Oh, Sabre Kais
Summary: This study examines how a random quantum circuit can quickly transform a quantum state into a Haar-measure random quantum state. The research shows that random quantum states have balanced entropic uncertainty and that random quantum circuits and random unitary matrices exhibit the cutoff phenomenon. The results suggest that random quantum states can be generated using shallow random circuits.
ELECTRONIC STRUCTURE
(2023)
Article
Nanoscience & Nanotechnology
Eran Lustig, Ohad Segal, Soham Saha, Eliyahu Bordo, Sarah N. Chowdhury, Yonatan Sharabi, Avner Fleischer, Alexandra Boltasseva, Oren Cohen, Vladimir M. Shalaev, Mordechai Segev
Summary: We experimentally study optical time-refraction caused by time-interfaces as short as a single optical cycle. By observing the propagation of a probe pulse through a sample with a large refractive index change induced by an intense modulator pulse, we find that increasing the refractive index abruptly leads to red-shifted waves while decreasing it back to the original value causes a subsequent blue-shift. Shortening the temporal width of the modulator pulse leads to a proportionally shorter rise time of the red-shift associated with time-refraction. These experiments are conducted in transparent conducting oxides acting as epsilon-near-zero materials. The findings stimulate questions about the fundamental physics in ultrashort time frames and pave the way for future experiments with photonic time-crystals generated by periodic refractive index changes.
Article
Physics, Multidisciplinary
Manas Sajjan, Vinit Singh, Raja Selvarajan, Sabre Kais
Summary: We introduce unexplored imaginary components of out-of-time order correlators to study the information scrambling capacity of a graph neural network. We relate it to conventional measures of correlation and establish mathematical bounds shared by these seemingly disparate quantities. Additionally, we construct an emergent convex space to analyze the geometrical ramifications of these bounds during training. This analysis demystifies quantum machine learning models and provides insights into the underlying physical mechanisms.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Optics
Sangchul Oh, Sabre Kais
Summary: By comparing the bit strings sampled from classical computers using tensor network simulation with those from the Sycamore quantum processor, it is found that Kalachev et al.'s samples are statistically closer to the Sycamore samples than Pan et al.'s. This finding implies that further study is needed to certify or beat the claims of quantum advantage using random circuit sampling.
Article
Mathematics
Raja Selvarajan, Manas Sajjan, Travis S. Humble, Sabre Kais
Summary: This paper proposes an alternative approach to reduce the dimensionality of states in higher dimensional Hilbert spaces. By building a variational algorithm-based autoencoder circuit, the circuit is able to generate an output state that retains the features of the starting state while reducing the number of qubits. Experimental results show the effectiveness of this method.