Review
Astronomy & Astrophysics
Leonardo Abbrescia, Jared Speck
Summary: In this article, supplementary notes on the lectures about relativistic Euler equations and shocks are provided. The focus is on the analysis of shock formation in one and three spatial dimensions, the mathematical theory of shock waves, and the discussion of open problems related to shocks.
CLASSICAL AND QUANTUM GRAVITY
(2023)
Review
Quantum Science & Technology
Vaisakh Mannalatha, Sandeep Mishra, Anirban Pathak
Summary: This article reviews existing quantum random number generators (QRNGs) and highlights their distinct features from the classical world. It also discusses the origin and applicability of randomness, as well as the nonclassical theories associated with different types of QRNGs.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Quantum Science & Technology
Alexandru Paler, Austin G. Fowler
Summary: This paper describes a pipeline approach to decoding the surface code using minimum weight perfect matching, taking into account correlations between detection events. The simplified method maintains a stable logical error rate.
Article
Quantum Science & Technology
Carlos Ruiz-Gonzalez, Soeren Arlt, Jan Petermann, Sharareh Sayyad, Tareq Jaouni, Ebrahim Karimi, Nora Tischler, Xuemei Gu, Mario Krenn
Summary: Photons are used to test the foundations of quantum mechanics, and photonic quantum technology plays a significant role in the development of better sensors, secure communications, and quantum-enhanced computation. However, computer-designed experiments have not been widely adopted in the photonic quantum optics community due to closed systems, inefficiency, and limited applicability. PyTheus, an efficient and open-source digital discovery framework, overcomes these challenges by providing interpretable designs for complex experimental problems, which can accelerate the development of quantum optics and inspire new ideas in quantum hardware and technology.
Article
Quantum Science & Technology
Isaac D. Smith, Marius Krumm, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Hans J. Briegel
Summary: In this paper, we formalize the process of learning hidden properties of quantum systems using classical-quantum combs, a generalization of classical-quantum states. We quantify the optimal strategy for learning the hidden property using the comb min-entropy and demonstrate its power in the context of measurement-based quantum computation (MBQC) and verification of unknown MBQC devices. We also introduce a novel connection between MBQC and quantum causal models.
Article
Quantum Science & Technology
Kah Jen Wo, Guus Avis, Filip Rozpedek, Maria Flors Mor-Ruiz, Gregor Pieplow, Tim Schroeder, Liang Jiang, Anders S. Sorensen, Johannes Borregaard
Summary: This study proposes a resource-efficient one-way quantum repeater that utilizes quantum error-correcting codes to counteract loss and operational error rates in a communication channel. By minimizing resource overhead and using tailored error-correcting codes, reliable quantum bit transmission can be achieved over intercontinental distances.
NPJ QUANTUM INFORMATION
(2023)
Article
Quantum Science & Technology
Ruo Cheng Huang, Paul M. Riechers, Mile Gu, Varun Narasimhachar
Summary: This paper combines quantum information processing and computational mechanics to develop a technique for predictive work extraction from non-Markovian stochastic processes with quantum outputs. It is demonstrated that this technique can extract more work than other methods, and a phase transition in the efficacy of memory for work extraction from quantum processes is discovered.
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
Quantum Science & Technology
Francesco Scala, Andrea Ceschini, Massimo Panella, Dario Gerace
Summary: This article presents a generalized approach to applying the dropout technique in quantum neural network models, with different quantum dropout strategies analyzed to avoid overfitting and achieve a high level of generalization. The study highlights that quantum dropout does not impact the expressibility and entanglement of QNN models.
ADVANCED QUANTUM TECHNOLOGIES
(2023)
Article
Quantum Science & Technology
Yumeng Chen, Haozhen Situ, Qiong Huang, Cai Zhang
Summary: This paper proposes a new scheme using a third party to privately compute the intersection of two parties' sets. The scheme can resist both outside and inside attacks over ideal and noisy quantum channels, and is feasible with current quantum technologies.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Quantum Science & Technology
Guo-Dong Li, Jun-Jie Luo, Qing-Le Wang
Summary: Researchers have proposed a twin-field quantum encryption protocol based on blockchain for electronic payment, in response to the security challenges faced by traditional encryption methods in the era of quantum computing. The protocol enables authentication and eavesdropping detection simultaneously and has the potential to break traditional boundaries, while demonstrating resilience against common attacks.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Quantum Science & Technology
Wentao Chen, Shuaining Zhang, Jialiang Zhang, Xiaolu Su, Yao Lu, Kuan Zhang, Mu Qiao, Ying Li, Jing-Ning Zhang, Kihwan Kim
Summary: This article introduces a quantum error mitigation method called probabilistic error cancellation (PEC), which can be applied to various quantum hardware platforms and quantum algorithms. Researchers attempt to benchmark PEC in trapped-ion qubits to improve simulation fidelity and observe the dynamics of the Fermi-Hubbard model.
NPJ QUANTUM INFORMATION
(2023)
Article
Computer Science, Theory & Methods
Massimiliano Guarneri, Andrea Chiuri
Summary: Inferring the process matrix of a quantum channel is a crucial task in quantum information research. Traditional optimization methods have drawbacks, and this paper proposes an alternative approach based on neural networks, which denoises the process matrix by drawing an analogy with image pixels.
INTERNATIONAL JOURNAL OF QUANTUM INFORMATION
(2023)
Article
Materials Science, Multidisciplinary
Sang-Wook Cheong, Fei-Ting Huang
Summary: This article introduces the different phenomena of ferromagnetism and studies the magnetic point groups associated with these phenomena. The research shows that all ferromagnetic point groups, including those related to ferromagnets, ferrimagnets, and weak ferromagnets, can exhibit these phenomena. Additionally, some true antiferromagnetic materials can also display these phenomena through external perturbations.
NPJ QUANTUM MATERIALS
(2023)
Article
Quantum Science & Technology
Jinzhe Jiang, Yaqian Zhao, Rengang Li, Chen Li, Zhenhua Guo, Baoyu Fan, Xuelei Li, Ruyang Li, Xin Zhang
Summary: This paper investigates the generalization of quantum neural networks and compares it with classical neural networks. The research proves that quantum neural networks have a theoretical value for generalization error bound and demonstrates their similar performance on the training dataset and test dataset.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Chemistry, Physical
Khadijah Mohammedsaleh Katubi, Muhammad Saqib, Tayyaba Mubashir, Mudassir Hussain Tahir, Mohamed Ibrahim Halawa, Alveena Akbar, Beriham Basha, Muhammad Sulaman, Z. A. Alrowaili, M. S. Al-Buriahi
Summary: Machine learning analysis is an important tool for predicting parameters and designing efficient solar cell materials. By collecting data from literature and using regression models, accurate predictions can be obtained. Among the tested models, hist gradient boosting (HGB) and light gradient boosting (LGBM) regression models showed better predictive capabilities, particularly for power conversion efficiency (PCE) prediction.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
(2023)
Article
Quantum Science & Technology
Jun-Yi Wu, Kosuke Matsui, Tim Forrer, Akihito Soeda, Pablo Andres-Martinez, Daniel Mills, Luciana Henaut, Mio Murao
Summary: In noisy intermediate-scale quantum computing, distributed quantum computing (DQC) can extend the limited scalability of a single quantum processing unit (QPU) through entanglement-assisted local operations and classical communication. An entanglement-efficient protocol is needed for practical DQC experiments. We propose a packing protocol, based on [Eisert et. al., PRA, 62:052317(2000)], that can locally pack multiple nonlocal controlled-unitary gates using one maximally entangled pair. We introduce distributing processes and embedding processes as building blocks, and show that the structure of a quantum circuit can be represented by corresponding packing graphs and conflict graphs. Based on these graphs, we derive heuristic algorithms for finding an entanglement-efficient packing of distributing processes for a given quantum circuit. These algorithms can determine the required number of local auxiliary qubits in DQC.
Article
Quantum Science & Technology
Emilio Onorati, Tamara Kohler, Toby S. Cubitt
Summary: This study develops efficient algorithms based on the mathematical theory of Markovian master equations to analyze and evaluate unknown noise processes. The algorithms can output the best-fit Lindbladian for dynamics consistent with Markovian evolution and provide a quantitative measure of non-Markovianity for non-Markovian dynamics.
Article
Chemistry, Physical
Dan Zhao, Xiaoqing Liu
Summary: This study investigated the adsorption behavior and interfacial properties of nonpolar oil drops on coal surfaces using molecular dynamics simulations. The contact angle, contact area, and interaction energy between the oil drops and the surface were found to be important factors affecting flotation. The type of functional group on the coal surface also significantly influenced the interaction between the oil drops and the surface.
INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY
(2023)
Article
Quantum Science & Technology
Arshag Danageozian
Summary: This article investigates the real-time parameter estimation problem based on a spectator system, which can estimate the noise parameters of a quantum memory in real-time and feed back the information as classical side-information to the recovery protocol. The research results indicate a trade-off between estimation accuracy and recovery performance in implementing real-time quantum memories. Additionally, fundamental bounds for multi-cycle recovery are provided, revealing the advantage of incomplete knowledge of noise.