Article
Optics
S. E. Rasmussen, N. T. Zinner
Summary: In this paper, the entangling quantum generative adversarial network (EQ-GAN) is investigated for multiqubit learning. It is shown that EQ-GAN can learn circuits more efficiently than SWAP test and generate excellent overlap matrix elements for learning VQE states of small molecules. However, the lack of phase estimation prevents it from directly estimating energy. Additionally, EQ-GAN demonstrates its potential in learning random states.
Article
Physics, Multidisciplinary
Jeremy T. Young, Przemyslaw Bienias, Ron Belyansky, Adam M. Kaufman, Alexey Gorshkov
Summary: Rydberg atoms with strong and tunable interactions can be utilized to realize fast two-qubit entangling gates. A generalization of these gates to multiqubit Rydberg-blockade gates involving many control and target qubits simultaneously is proposed, achieved by using strong microwave fields to dress nearby Rydberg states. The implementation of these multiqubit gates has the potential to simplify quantum algorithms and state preparation, as demonstrated by the creation of a 25-atom Greenberger-Horne-Zeilinger state using only three gates with a 5.8% error rate.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Luigi Giannelli, Pierpaolo Sgroi, Jonathon Brown, Gheorghe Sorin Paraoanu, Mauro Paternostro, Elisabetta Paladino, Giuseppe Falci
Summary: Quantum Optimal Control and Reinforcement Learning are effective methods for solving control problems in quantum systems, which can be implemented using machine learning techniques. This tutorial introduces these methods and provides examples using the problem of three-level population transfer.
Article
Mathematics, Interdisciplinary Applications
A. C. Tzemos, G. Contopoulos
Summary: The study explores the Bohmian trajectories of a 4-qubit system and finds that the chaotic trajectories are both ergodic and effectively ordered for longer times compared to the 2 and 3-qubit cases. It also shows that the higher the dimensionality of the system, the larger the proportion of chaotic trajectories within the Born distribution, making Born's rule accessible by practically all initial distributions in multiqubit systems.
CHAOS SOLITONS & FRACTALS
(2022)
Article
Physics, Multidisciplinary
Michael R. Geller
Summary: Several techniques have been introduced to mitigate errors in near-term quantum computers, focusing on gate errors and measurement errors. A widely used transition matrix error mitigation technique has limitations when state-preparation errors are present. A new measurement error mitigation technique, a conditionally rigorous TMEM, has been developed to address this issue and has been demonstrated on IBM Q superconducting qubits.
PHYSICAL REVIEW LETTERS
(2021)
Article
Quantum Science & Technology
Chen-Lu Zhu, Bin Hu, Bo Li, Zhi-Xi Wang, Shao-Ming Fei
Summary: In this paper, the geometric measure of multipartite quantum discord is evaluated and the results for a large family of multiqubit states are obtained. The dynamic behavior of geometric discord for the family of two-, three- and four-qubit states under phase noise acting on the first qubit is investigated. It is discovered that sudden change of multipartite geometric discord can appear when phase noise act only on one part of the two-, three- and four-qubit states.
QUANTUM INFORMATION PROCESSING
(2022)
Review
Chemistry, Multidisciplinary
Jose D. Martin-Guerrero, Lucas Lamata
Summary: Machine learning techniques have greatly advanced scientific research in recent years, with reinforcement learning being a key approach for optimizing scientific discovery in fields such as physics, chemistry, and biology. Physical systems, especially quantum systems, may offer more efficient reinforcement learning protocols. This review highlights the recent progress in reinforcement learning and physics, emphasizing the potential of quantum reinforcement learning in improving computational capabilities.
APPLIED SCIENCES-BASEL
(2021)
Article
Physics, Applied
Dongmin Yu, Han Wang, Jin-Ming Liu, Shi-Lei Su, Jing Qian, Weiping Zhang
Summary: This paper demonstrates a method for implementing a multiqubit blockade gate using atoms arranged in a three-dimensional spheroidal array. By optimizing the control qubit distributions, an enhanced asymmetric Rydberg blockade is achieved, with robustness and negligible position error. Numerical calculations show that high-fidelity multiqubit quantum computation can be achieved.
PHYSICAL REVIEW APPLIED
(2022)
Article
Automation & Control Systems
Weichao Liang, Nina H. Amini, Paolo Mason
Summary: This article investigates stochastic master equations describing the interaction between a multiqubit system and electromagnetic fields undergoing continuous-time measurements, providing general conditions on feedback controllers and control Hamiltonians to ensure almost sure exponential convergence to a predetermined GHZ state. The effectiveness of the methodology is demonstrated for a three-qubit system through numerical simulations.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Optics
Xian Shi, Lin Chen, Mengyao Hu
Summary: The study introduces a multilinear monogamy relation for multiqubit systems based on entanglement of formation and concurrence, and applies it to investigate genuine entangled states and absolutely maximally entangled states. Additionally, the research examines a three-qubit pure state in terms of quantum discord.
Article
Automation & Control Systems
Linfei Yin, Lichun Chen, Dongduan Liu, Xiao Huang, Fang Gao
Summary: This paper proposes a novel online control algorithm based on quantum deep reinforcement learning for double-fed induction generator-based wind turbines, which can achieve online control and better control performance.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Physics, Multidisciplinary
Yuxiang Qiu, Min Zhuang, Jiahao Huang, Chaohong Lee
Summary: This study proposes a scheme to optimize the state preparation pulse sequence for accelerating entanglement generation using deep reinforcement learning. By maximizing the quantum Fisher information, the pulse sequence can generate entangled states with ultimate precision bounds following the Heisenberg-limited scalings, and show better robustness against differences between simulation and experiment.
NEW JOURNAL OF PHYSICS
(2022)
Article
Physics, Applied
Xudan Chai, Teng Ma, Qihao Guo, Zhangqi Yin, Hao Wu, Qing Zhao
Summary: Quantum state characterization is a critical issue in quantum information transformation and quantum computation, with a focus on security and accuracy. The current challenges lie in the burden of measurements and data processing, as well as the postprocessing of large amounts of data. Therefore, building an efficient framework to improve accuracy is crucial.
PHYSICAL REVIEW APPLIED
(2023)
Review
Optics
Lucas Lamata
Summary: Quantum machine learning, particularly quantum reinforcement learning, is a promising field aiming to design quantum agents to adapt to and interact with their environment. Quantum technology in photonics has the potential to enhance quantum computation, communication, and machine learning through their integration.
Article
Quantum Science & Technology
Niyazi Furkan Bar, Hasan Yetis, Mehmet Karakose
Summary: Nowadays, machine learning techniques are widely applied to various fields, and the idea of using quantum computing to solve problems is gaining popularity. Researchers are experimenting with quantum circuits in machine learning methods to overcome the limitations of qubits. In this study, a variational quantum circuit (VQC) using amplitude encoding is proposed and applied to a navigation problem, showing promising performance.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Physics, Multidisciplinary
R. Grimaudo, A. S. Magalhaes de Castro, A. Messina, E. Solano, D. Valenti
Summary: This study investigates a two-interacting-qubit quantum Rabi-like model with vanishing transverse fields on the qubit pair. Regardless of the coupling regime, this model can be exactly and unitarily reduced to two independent single-spin quantum Rabi models, where the spin-spin coupling acts as the transverse field. This transformation and the analytical treatment of the single-spin quantum Rabi model are crucial in proving the integrability of our model. The study reveals the existence of different first-order quantum phase transitions characterized by discontinuous two-spin magnetization, mean photon number, and concurrence.
PHYSICAL REVIEW LETTERS
(2023)
Article
Physics, Multidisciplinary
Yongcheng Ding, Javier Gonzalez-Conde, Lucas Lamata, Jose D. Martin-Guerrero, Enrique Lizaso, Samuel Mugel, Xi Chen, Roman Orus, Enrique Solano, Mikel Sanz
Summary: In this study, a novel approach using a D-Wave quantum annealer is experimentally explored to predict financial crashes in a complex financial network. The performance of the quantum annealer in achieving financial equilibrium is benchmarked. The equilibrium condition of a nonlinear financial model is embedded into a higher-order unconstrained binary optimization (HUBO) problem, which is then transformed into a spin-1/2 Hamiltonian with at most, two-qubit interactions. The problem is equivalent to finding the ground state of an interacting spin Hamiltonian, which can be approximated with a quantum annealer. The experiment paves the way for the codification of this quantitative macroeconomics problem in quantum annealers.
Article
Physics, Multidisciplinary
Jia-Liang Tang, Gabriel Alvarado Barrios, Enrique Solano, Francisco Albarran-Arriagada
Summary: We investigated the tunable control of non-Markovianity in a bosonic mode by coupling it to auxiliary qubits in a thermal reservoir. By considering the Tavis-Cummings model for a single cavity mode and auxiliary qubits, we studied the manipulation of dynamical non-Markovianity with respect to the qubit frequency. Our findings reveal that controlling the auxiliary systems can influence the cavity dynamics as a time-dependent decay rate. Finally, we demonstrate how this tunable time-dependent decay rate can be used to engineer bosonic quantum memristors, which are essential for developing neuromorphic quantum technologies.
Editorial Material
Computer Science, Information Systems
Lucas Lamata
Article
Quantum Science & Technology
Lucas Lamata
Summary: This article provides an overview of recent theoretical proposals and experimental implementations in the field of quantum machine learning. It reviews specific topics such as quantum reinforcement learning, quantum autoencoders, and quantum memristors, and their realization in quantum photonics and superconducting circuits. The field of quantum machine learning has the potential to produce significant results for industry and society, making it necessary to advance initial quantum implementations in noisy intermediate-scale quantum computers for better machine learning calculations.
ADVANCED QUANTUM TECHNOLOGIES
(2023)
Article
Physics, Applied
Pranav Chandarana, Narendra N. Hegade, Iraitz Montalban, Enrique Solano, Xi Chen
Summary: We propose a hybrid classical-quantum digitized counterdiabatic algorithm to solve the protein-folding problem on a tetrahedral lattice. Our method outperforms state-of-the-art quantum algorithms using problem-inspired and hardware-efficient variational quantum circuits. We apply our method to proteins with up to nine amino acids, achieving high success probabilities with low-depth circuits on various quantum hardware.
PHYSICAL REVIEW APPLIED
(2023)
Article
Physics, Applied
Lucas C. Celeri, Daniel Huerga, Francisco Albarran-Arriagada, Enrique Solano, Mikel Garcia de Andoin, Mikel Sanz
Summary: Simulating quantum many-body systems is challenging, especially for fermionic systems due to the emergence of nonlocal interactions. We present a digital-analog quantum algorithm that can simulate a wide range of fermionic Hamiltonians, including the well-known Fermi-Hubbard model. These methods allow quantum algorithms to go beyond digital versions by efficiently utilizing coherence time. Additionally, we demonstrate a low-connected architecture for realistic digital-analog implementations of specific fermionic models.
PHYSICAL REVIEW APPLIED
(2023)
Article
Quantum Science & Technology
Pranav Chandarana, Pablo Suarez Vieites, Narendra N. Hegade, Enrique Solano, Yue Ban, Xi Chen
Summary: In this paper, we use meta-learning with recurrent neural networks to address the difficulties in finding suitable variational parameters and initial parameters for the QAOA. By combining meta-learning and counterdiabaticity, we find suitable variational parameters and reduce the number of optimization iterations required. Our method improves the performance of the state-of-the-art QAOA by offering a short-depth circuit ansatz with optimal initial parameters.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Astronomy & Astrophysics
Decheng Ma, Chenglong Jia, Enrique Solano, Lucas Chibebe Celeri
Summary: The propagation of phonons in the presence of a particle sink with radial flow in a Bose-Einstein condensate is considered. It is found that due to the particle sink, which simulates a static acoustic black hole, the phonon experiences significant spacetime curvature at a considerable distance from the sink. The trajectory of the phonons is bent after passing by the particle sink, simulating the gravitational lensing effect in a Bose-Einstein condensate. Possible experimental implementations are discussed.
Article
Physics, Multidisciplinary
Francisco Andres Cardenas-Lopez, Juan Carlos Retamal, Xi Chen
Summary: The reverse-engineering approach is proposed to design the longitudinal coupling between qubits and field modes, achieving a fast generation of multi-partite quantum gates in photonic or qubit-based architectures. The study shows that the generation time is at the nanosecond scale and does not depend on the number of system components. Furthermore, the protocol is not significantly affected by dissipative dynamics. The possible implementation with circuit quantum electrodynamics architecture is discussed.
COMMUNICATIONS PHYSICS
(2023)
Article
Quantum Science & Technology
Giancarlo Gatti, Daniel Huerga, Enrique Solano, Mikel Sanz
Summary: We propose a protocol to encode classical bits using quantum correlations for a random access code. Measurement contexts built with many-body Pauli observables enable efficient and random access to the encoded data, which is useful for large-data storage with partial retrieval.
Article
Quantum Science & Technology
Maria Laura Olivera-Atencio, Lucas Lamata, Jesus Casado-Pascual
Summary: Quantum machine learning (QML), which has the potential to revolutionize data processing, faces challenges from environmental noise and dissipation.While traditional efforts seek to combat these hindrances, this perspective proposes harnessing them for potential advantages.Surprisingly, under certain conditions, noise and dissipation can benefit QML.Adapting to open quantum systems holds potential for groundbreaking discoveries that may reshape the future of quantum computing.
ADVANCED QUANTUM TECHNOLOGIES
(2023)
Article
Optics
Jie Peng, Jianing Tang, Pinghua Tang, Zhongzhou Ren, Junlong Tian, Nancy Barraza, Gabriel Alvarado Barrios, Lucas Lamata, Enrique Solano, F. Albarran-Arriagada
Summary: In this study, we propose a high-quality deterministic single-photon source that can emit two single photons with any time separation. By utilizing special solutions and adiabatic evolutions, this proposal can be achieved rapidly, taking advantage of the ultrastrong coupling.
