Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Ferdinando Di Martino, Alfredo Massa, Roberto Schiattarella, Autilia Vitiello
Summary: This paper introduces the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) as a reference computational model to design innovative frameworks for quantum devices to interact and solve complex problems collaboratively. Through two case studies, a multi-threaded implementation of the D-NISQ model demonstrates greater reliability in solving problems through quantum computation.
INFORMATION FUSION
(2023)
Article
Engineering, Electrical & Electronic
Yifan Zhou, Peng Zhang, Fei Feng
Summary: This paper presents a new NISQ-QEMTP methodology that overcomes the existing challenges in practical and scalable quantum computing for electromagnetic transient programs. It includes the design of shallow-depth quantum circuits for reducing noise on NISQ quantum devices, practical QEMTP linear solvers with executable quantum state preparation and measurements, a noise-resilient QEMTP algorithm, quantum shifted frequency analysis for faster computations, and a systematic analysis of QEMTP performance under various quantum environments. Extensive experiments confirm the effectiveness and noise-resilience of QEMTP on both noise-free simulators and IBM real quantum computers.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Review
Physics, Multidisciplinary
Bin Cheng, Xiu-Hao Deng, Xiu Gu, Yu He, Guangchong Hu, Peihao Huang, Jun Li, Ben-Chuan Lin, Dawei Lu, Yao Lu, Chudan Qiu, Hui Wang, Tao Xin, Shi Yu, Man-Hong Yung, Junkai Zeng, Song Zhang, Youpeng Zhong, Xinhua Peng, Franco Nori, Dapeng Yu
Summary: In the past decade, quantum computers have made remarkable progress and achieved key milestones towards universal fault-tolerant quantum computers. Quantum hardware has become more integrated and architectural, surpassing the fault-tolerant threshold in controlling various physical systems. Quantum computation research has embraced industrialization and commercialization, shaping a vibrant environment that accelerates the development of this field, now in the noisy intermediate-scale quantum era.
FRONTIERS OF PHYSICS
(2023)
Review
Physics, Multidisciplinary
Kishor Bharti, Alba Cervera-Lierta, Thi Ha Kyaw, Tobias Haug, Sumner Alperin-Lea, Abhinav Anand, Matthias Degroote, Hermanni Heimonen, Jakob S. Kottmann, Tim Menke, Wai-Keong Mok, Sukin Sim, Leong-Chuan Kwek, Alan Aspuru-Guzik
Summary: NISQ computers, composed of noisy qubits, are already being used in various fields. This review provides a comprehensive summary of NISQ computational paradigms and algorithms and introduces various benchmarking and software tools for programming and testing NISQ devices.
REVIEWS OF MODERN PHYSICS
(2022)
Article
Physics, Multidisciplinary
Junhua Liu, Kwan Hui Lim, Kristin L. Wood, Wei Huang, Chu Guo, He-Liang Huang
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
Optics
Adam Callison, Nicholas Chancellor
Summary: The article discusses the significance of hybrid quantum-classical algorithms in current research on quantum computing. It explores the definition of hybrid algorithms and their characteristics. The article reviews the historical development of hybrid algorithms and discusses their future prospects. It concludes that the evolution of quantum computing will follow a similar trend as classical computing, with quantum processors augmenting classical processors through specialized tasks.
Article
Physics, Multidisciplinary
Hari Hara Suthan Chittoor, Osvaldo Simeone
Summary: This paper studies the design of LOCC protocols in the presence of noisy communication channels by using quantum machine learning tools. It focuses on quantum entanglement distillation and quantum state discrimination tasks and uses parameterized quantum circuits (PQCs) to optimize local processing while accounting for communication errors. The introduced approach, Noise Aware-LOCCNet (NA-LOCCNet), outperforms existing protocols designed for noiseless communications.
Article
Computer Science, Information Systems
Tayyaba Shahwar, Junaid Zafar, Ahmad Almogren, Haroon Zafar, Ateeq Ur Rehman, Muhammad Shafiq, Habib Hamam
Summary: In this study, a hybrid classical-quantum machine learning model is proposed for the detection of Alzheimer's disease. By combining classical neural networks and quantum processors, the model achieves optimal preprocessing of complex and high-dimensional data, resulting in high accuracy.
Article
Physics, Multidisciplinary
Mahabubul Alam, Swaroop Ghosh
Summary: Quantum machine learning (QML) has the potential to accelerate and improve conventional machine learning (ML) tasks. To address the issue of gate errors and decoherence in existing QML models using deep parametric quantum circuits (PQC), we propose QNet, a new QML architecture consisting of small quantum neural networks (QNN). QNet can be executed on small quantum computers, allowing for solving supervised ML tasks of any scale and enabling heterogeneous technology integration. Through empirical studies, QNet demonstrates better accuracy (43% on average) compared to existing models on noisy quantum hardware emulators, providing a blueprint for building noise-resilient QML models with near-term noisy quantum devices.
FRONTIERS IN PHYSICS
(2022)
Article
Quantum Science & Technology
Nelson Filipe Costa, Omar Yasser, Aidar Sultanov, Gheorghe Sorin Paraoanu
Summary: This study demonstrates that adaptive methods based on classical machine learning algorithms can enhance the precision of quantum phase estimation when using noisy non-entangled qubits as sensors. Using Differential Evolution and Particle Swarm Optimization algorithms, optimal feedback policies are identified to minimize the Holevo variance. Benchmarking against different scenarios, the robustness of these schemes in real experimental setups is evaluated.
EPJ QUANTUM TECHNOLOGY
(2021)
Article
Optics
Kishor Bharti, Tobias Haug, Vlatko Vedral, Leong-Chuan Kwek
Summary: Semidefinite programs are widely used convex optimization problems with applications in various fields. Noisy intermediate-scale quantum algorithms aim to efficiently use current quantum hardware. We propose a NISQ algorithm for solving SDPs and provide numerical evidence of its improvements in estimating ground-state energies.
Article
Multidisciplinary Sciences
S. Moradi, C. Brandner, C. Spielvogel, D. Krajnc, S. Hillmich, R. Wille, W. Drexler, L. Papp
Summary: Quantum machine learning has made significant progress in recent years and shows promising performance on real clinical datasets. We propose two quantum machine learning algorithms and demonstrate their effectiveness through experiments. We also find that different algorithms have different advantages depending on the sample and feature counts.
SCIENTIFIC REPORTS
(2022)
Review
Quantum Science & Technology
Lin Jiao, Wei Wu, Si-Yuan Bai, Jun-Hong An
Summary: This paper provides a review of the principle, categories, and applications of quantum metrology. Attention is focused on achieving high precision measurements in the presence of noise, and the effects of noise-induced decoherence on quantum resources are discussed. The paper also explores methods for actively controlling the effects of noise in noisy quantum metrology.
ADVANCED QUANTUM TECHNOLOGIES
(2023)
Review
Physics, Multidisciplinary
Kai Xu, Heng Fan
Summary: This article reviews the research progress on noisy multiqubit quantum computation and quantum simulation, focusing on multiqubit state generations, quantum computational advantage, and simulating physics of quantum many-body systems. The perspectives of near term noisy intermediate-quantum processors are also discussed.
Article
Computer Science, Artificial Intelligence
Poojith U. Rao, Balwinder Sodhi
Summary: Building a reliable network of electric vehicle charging stations is crucial for the success of electric vehicles. However, solving the optimization problem of determining the optimal spatial placement of such charging stations becomes non-scalable using classical computers. In this study, we propose a quantum-classical solution that significantly improves the speed of solving this problem, making it suitable for scalability scenarios.
Article
Quantum Science & Technology
Mateusz Ostaszewski, Edward Grant, Marcello Benedetti
Summary: Our proposed method efficiently optimizes both the structure and parameter values of quantum circuits with minimal computational overhead, showing better performance for shallow circuits with structure optimization, making it suitable for noisy intermediate-scale quantum computers. Demonstrated by optimizing a variational quantum eigensolver for finding ground states of Lithium Hydride and the Heisenberg model in simulation, and for finding the ground state of Hydrogen gas on the IBM Melbourne quantum computer.
Article
Quantum Science & Technology
Stefan H. Sack, Maksym Serbyn
Summary: The study shows that random initialization for QAOA tends to converge to local minima with sub-optimal performance, while the TQA initialization method can circumvent this issue and achieve the same performance as random initialization over a broad range of time steps. The optimal time step coincides with the proliferation of Trotter errors in quantum annealing.
Article
Physics, Multidisciplinary
Chiara Leadbeater, Louis Sharrock, Brian Coyle, Marcello Benedetti
Summary: This work explores a hybrid quantum-classical approach to generative modelling using a quantum circuit born machine, training it with f-divergences. Two heuristics are introduced to enhance training, one involving f-divergence switching and the other introducing locality to the divergence to mitigate barren plateaus. The long-term implications of quantum devices for computing f-divergences are discussed, including the development of algorithms for estimating f-divergences with quadratic speedups.
Article
Physics, Applied
Marcello Benedetti, Brian Coyle, Mattia Fiorentini, Michael Lubasch, Matthias Rosenkranz
Summary: Inference is the task of drawing conclusions about unobserved variables given observations of related variables, and variational inference is an effective approximation method. In this work, quantum Born machines are used as variational distributions over discrete variables, implementing the framework of operator variational inference.
PHYSICAL REVIEW APPLIED
(2021)
Article
Quantum Science & Technology
David Amaro, Matthias Rosenkranz, Nathan Fitzpatrick, Koji Hirano, Mattia Fiorentini
Summary: In this study, four variational quantum heuristics were applied to the job shop scheduling problem on IBM's superconducting quantum processors. The results showed that the filtering variational quantum eigensolver (F-VQE) outperformed other algorithms in terms of convergence speed and sampling the global optimum.
EPJ QUANTUM TECHNOLOGY
(2022)
Article
Quantum Science & Technology
David Amaro, Carlo Modica, Matthias Rosenkranz, Mattia Fiorentini, Marcello Benedetti, Michael Lubasch
Summary: This article introduces a method that utilizes filtering operators to enhance combinatorial optimization efficiency, and explores the application of causal cones to reduce the number of qubits required. Through numerical analysis and experimental validation, the method outperforms traditional algorithms in the context of maximum weighted graph cut problems.
QUANTUM SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Samuel Duffield, Marcello Benedetti, Matthias Rosenkranz
Summary: Currently available quantum computers face challenges such as hardware noise and limited qubit count. Variational quantum algorithms, which employ a classical optimizer to train parameterized quantum circuits, have gained significant attention for practical applications of quantum technology in the near term. This study reframes classical optimization as an approximation of a Bayesian posterior from a probabilistic viewpoint. The posterior is induced by combining the cost function to be minimized with a prior distribution over the quantum circuit's parameters. A dimension reduction strategy based on a maximum a posteriori point estimate with a Laplace prior is described. Experiments on the Quantinuum H1-2 computer demonstrate that the resulting circuits execute faster and with less noise compared to circuits trained without dimension reduction. Additionally, a posterior sampling strategy based on stochastic gradient Langevin dynamics is described, showing capability to generate samples from the full posterior and avoid local optima on numerical simulations of three different problems.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Article
Quantum Science & Technology
Luuk Coopmans, Yuta Kikuchi, Marcello Benedetti
Summary: We propose a quantum algorithm that predicts M linear functions of an arbitrary Gibbs state by evolving a random pure state in imaginary time and constructing classical shadows. We implement this algorithm using quantum signal processing and verify its efficiency numerically. Furthermore, we demonstrate its successful application as a subroutine for training a fully connected quantum Boltzmann machine with eight qubits.
Article
Physics, Multidisciplinary
Lidia Stocker, Stefan H. Sack, Michael S. Ferguson, Oded Zilberberg
Summary: This paper proposes using the purity as an indicator for the formation of strong correlations to observe impurity physics and verifies the feasibility of this approach by solving the open Kondo box model in the small box limit. The study characterizes the metal-to-insulator phase transition in the system and identifies the quenching of the conducting dot-lead Kondo singlet by the formation of an insulating intraimpurity singlet. Additionally, an experimentally feasible tomography protocol for the measurement of purity is proposed, which motivates the observation of impurity physics through the buildup of entanglement.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Quantum Science & Technology
Stefan H. Sack, Raimel A. Medina, Alexios A. Michailidis, Richard Kueng, Maksym Serbyn
Summary: Variational quantum algorithms show promise for achieving quantum advantage on near-term devices. However, the presence of barren plateaus, with vanishing gradients, in the optimization landscape hinders efficient optimization. In this work, a general algorithm is proposed to avoid barren plateaus by introducing the concept of weak barren plateaus (WBPs) and utilizing shadow tomography with classical computers. The study demonstrates that avoiding WBPs ensures non-negligible gradients during initialization and can be further achieved during the optimization process by decreasing the gradient step size based on entropies.
Article
Quantum Science & Technology
Kirill Plekhanov, Matthias Rosenkranz, Mattia Fiorentini, Michael Lubasch
Summary: This study proposes amplitude estimation using constant-depth quantum circuits that approximately represent states during amplitude amplification. In the context of Monte Carlo integration, it is numerically shown that shallow circuits can accurately approximate multiple amplitude amplification steps. The variational approach is combined with maximum likelihood amplitude estimation to form variational quantum amplitude estimation (VQAE). To reduce computational requirements, adaptive VQAE is proposed and numerically demonstrated to outperform classical MC sampling in simulations with 6 to 12 qubits.
Article
Physics, Multidisciplinary
Marcello Benedetti, Mattia Fiorentini, Michael Lubasch
Summary: Parameterized quantum circuits are a promising technology for achieving quantum advantage, particularly in variational simulation of time evolution. The authors present hardware-efficient alternatives to the time-dependent variational principle, reducing hardware requirements significantly. The algorithms proposed systematically increase accuracy and hardware requirements for real time evolution scenarios, with numerical analysis demonstrating performance using quantum Hamiltonians with local interactions.
PHYSICAL REVIEW RESEARCH
(2021)
