Article
Quantum Science & Technology
Diego H. Useche, Andres Giraldo-Carvajal, Hernan M. Zuluaga-Bucheli, Jose A. Jaramillo-Villegas, Fabio A. Gonzalez
Summary: This paper presents a hybrid classical-quantum program for density estimation and supervised classification in a high-dimensional quantum computer. The proposed quantum protocols allow for estimating probability density functions and making predictions using supervised learning. The model can be generalized to find expected values of density matrices in high-dimensional quantum computers. Experimental results demonstrate that this method is a feasible strategy for implementing supervised classification and density estimation in a high-dimensional quantum computer.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Materials Science, Multidisciplinary
Ali A. Abu-Nada, Moataz A. Salhab
Summary: In an open quantum system, the noise induced by the environment is often assumed to be the reason for the disappearance of quantum properties. However, Barreiro et al. (2011) experimentally demonstrate that an engineered and controlled environment state can actually pump an arbitrary quantum system towards maximal entanglement, making it a resource for quantum information processing. To validate this idea, we simulate a quantum circuit on one of the IBM Q processors, aiming to pump an arbitrary maximally mixed state into a Greenberger-Horne-Zeilinger (GHZ) state. We propose a different circuit structure tailored for the IBM Q platform due to the limitations of the available circuits offered by Barreiro et al. (2011).
RESULTS IN PHYSICS
(2023)
Article
Computer Science, Hardware & Architecture
Sonia Lopez Alarcon, Federico Rueda
Summary: Gaussian boson sampling (GBS) has a strong mathematical connection with combinatorics problems and is applied through a software stack that focuses on high-level description and hardware implementation. This paper describes the general compilation process of GBS and specifically discusses the Strawberry Fields (Xanadu) compilation and simulation framework, including its time profiling and implications on computationally significant problem sizes. Additionally, it presents a compilation step to reduce the complexity of hardware description, resulting in a linear reduction in the overall number of operators as the number of Gaussian operators increases.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Quantum Science & Technology
Run-Hua Shi, Bai Liu, Mingwu Zhang
Summary: This paper defines two specific secure multiparty summations and presents corresponding measurement-device-independent quantum secure multiparty summation protocols. In these protocols, each party only performs simple single-particle operators, not complex quantum measurements. The proposed protocols achieve information-theoretical security, feasibility, and high performance.
QUANTUM INFORMATION PROCESSING
(2022)
Article
Physics, Multidisciplinary
Conrad Strydom, Mark Tame
Summary: We propose and demonstrate an interleaved randomised benchmarking protocol for measurement-based quantum computers, which can estimate the fidelity of any single-qubit measurement-based gate. We tested the protocol on IBM superconducting quantum processors, successfully estimating the fidelities of Hadamard and T gates and detecting noise variations in different gate implementations.
Article
Engineering, Electrical & Electronic
Nima Nikmehr, Peng Zhang
Summary: This paper proposes a quantum computing approach for power system reliability assessment, which includes an innovative quantum model, a quantum circuit achieving quadratic speed up, and an efficient quantum amplitude estimation algorithm. The accuracy and effectiveness of the quantum method are demonstrated on both radial and mesh distribution systems.
IEEE TRANSACTIONS ON POWER SYSTEMS
(2023)
Article
Quantum Science & Technology
Frederik Kofoed Marqversen, Nikolaj Thomas Zinner
Summary: We discuss the procedure for obtaining measurement-based implementations of quantum algorithms given by quantum circuit diagrams and how to reduce the required resources needed for a given measurement-based computation. This forms the foundation for quantum computing on photonic systems in the near term. To demonstrate that these ideas are well grounded we present three different problems which are solved by employing a measurement-based implementation of the variational quantum eigensolver algorithm (MBVQE). We show that by utilising native measurement-based gates rather than standard gates, such as the standard controlled not gate (CNOT), measurement-based quantum computations may be obtained that are both shallow and have simple connectivity while simultaneously exhibiting a large expressibility. We conclude that MBVQE has promising prospects for resource states that are not far from what is already available today.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Hardware & Architecture
Xincheng Chen
Summary: This paper focuses on the resource allocation problem in mobile edge computing from the perspective of operating systems, specifically addressing the challenges posed by multiple operating systems used by end users and fog nodes. By introducing a user satisfaction mechanism and differentiated services, the study aims to optimize resource allocation based on real-time application requirements and OS heterogeneity. Through a variable-substitution convexity transformation scheme, the proposed approach effectively addresses the complexity of the problem and demonstrates improved performance compared to existing optimization methods.
Article
Automation & Control Systems
Hongsheng Qi, Biqiang Mu, Ian R. Petersen, Guodong Shi
Summary: In this article, we study the recursion corresponding to measurement outcomes for open quantum networks under sequential measurements. We show that the state transition of the induced Boolean network can be explicitly represented through a real version of the master equation. We also demonstrate that structural properties of the induced Boolean network are direct consequences of the relaxing property of open quantum dynamics.
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
(2023)
Article
Physics, Multidisciplinary
Yulu Zhang, Hua Lu
Summary: With the development of quantum computing, the application of quantum neural networks will become more extensive, but its security will face more challenges. This paper proposes a quantum sampling method to detect the state of quantum neural networks at each stage, ensuring their security.
FRONTIERS IN PHYSICS
(2023)
Article
Computer Science, Hardware & Architecture
Daniel Bristot de Oliveira, Daniel Casini, Tommaso Cucinotta
Summary: With the shift from hardware-based to software-based network infrastructure using Network Function Virtualization, the development of operating systems faces new requirements. Linux, utilizing real-time kernel options and advanced CPU isolation features, serves as a fundamental component in this new architecture to enable low latency networked services. Optimizing Linux for such applications is challenging as it necessitates a comprehensive comprehension of the Linux execution model and the amalgamation of user-space tooling and tracing features. This paper delves into the internal aspects of Linux that impact Operating System Noise from a timing perspective, introduces Linux's osnoise tracer, an in-kernel tracer that measures and traces the Operating System Noise experienced by a workload in an integrated manner, facilitating system analysis and debugging. Additionally, this paper presents a series of experiments demonstrating Linux's capability to deliver low OS noise (in the single-digit μs order) along with the proposed tool's accurate identification of root causes for timing-related OS noise issues.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Article
Quantum Science & Technology
Sascha Wilkens, Joe Moorhouse
Summary: Quantum computing offers a significant speedup for certain mathematical challenges in finance, but current quantum systems are limited in capability. This paper explores the requirements and approaches for using quantum computing in risk management, specifically for market risk and counterparty credit risk. While conceptual solutions and small-scale circuits are feasible, the hardware capacity would need to increase by several magnitudes for real-life applications. Additionally, research into quantum noise control and mitigation is necessary for practical deployment of risk measurement applications. Given the maturity of classical simulation-based approaches, the business case for quantum solutions in risk management is not strong at present.
QUANTUM INFORMATION PROCESSING
(2023)
Review
Computer Science, Interdisciplinary Applications
Jaiteg Singh, Kamalpreet Singh Bhangu
Summary: This study aims to develop a clear understanding of the promises and limitations of the current state-of-the-art quantum computing use cases and to define directions for future research. It bridges the gap between computer professionals and non-physicists by offering conceptual and notational information and surveys existing applications, technological advancements, and contemporary challenges associated with quantum computing.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Hardware & Architecture
Ajay Badita, Parimal Parag, Vaneet Aggarwal
Summary: The study focuses on addressing stragglers in distributed computing systems through redundant computation and a proactive mitigation strategy. It analyzes the impact of different parameter choices on task completion time and server utilization. Experiments show that the proposed strategy effectively captures the random completion times and insights derived from the study hold true.
IEEE-ACM TRANSACTIONS ON NETWORKING
(2021)
Article
Quantum Science & Technology
Yongsoo Hwang, Byung-Soo Choi
Summary: In the context of large-scale quantum computing, accurately and quickly determining the necessary quantum computational resources and performance is crucial. Traditional methods are impractical for solving these issues, leading to the proposal of a method based on hierarchically structured quantum algorithm descriptions, which can significantly reduce processing time and improve efficiency.
QUANTUM INFORMATION PROCESSING
(2021)
Article
Chemistry, Analytical
Anton V. Bourdine, Vladimir V. Demidov, Artem A. Kuznetsov, Alexander A. Vasilets, Egishe V. Ter-Nersesyants, Alexander V. Khokhlov, Alexandra S. Matrosova, Grigori A. Pchelkin, Michael V. Dashkov, Elena S. Zaitseva, Azat R. Gizatulin, Ivan K. Meshkov, Airat Zh. Sakhabutdinov, Eugeniy V. Dmitriev, Oleg G. Morozov, Vladimir A. Burdin, Konstantin V. Dukelskii, Yaseera Ismail, Francesco Petruccione, Ghanshyam Singh, Manish Tiwari, Juan Yin
Summary: This work presents the design and fabrication of a silica few-mode optical fiber with induced twisting and improved refractive index profile. The fiber supports 4 guided modes over the C-band and has been tested for its mode properties after fiber Bragg grating writing.
Article
Quantum Science & Technology
Carsten Blank, Adenilton J. da Silva, Lucas P. de Albuquerque, Francesco Petruccione, Daniel K. Park
Summary: Quantum computing offers exciting opportunities for kernel-based machine learning methods, allowing efficient construction of classifier models through quantum interference effects. To make these methods practical, it is important to minimize circuit size and handle imbalanced data sets.
QUANTUM SCIENCE AND TECHNOLOGY
(2022)
Article
Multidisciplinary Sciences
Betony Adams, Ilya Sinayskiy, Rienk van Grondelle, Francesco Petruccione
Summary: The SARS-CoV-2 pandemic has made the study of viral mechanisms more urgent, and quantum biology may provide important insights into the virus-host invasion process. Research suggests that quantum tunnelling may be relevant to SARS-CoV-2 infection.
SCIENTIFIC REPORTS
(2022)
Article
Computer Science, Artificial Intelligence
Roberto Giuntini, Federico Holik, Daniel K. Park, Hector Freytes, Carsten Blank, Giuseppe Sergioli
Summary: Quantum machine learning is a groundbreaking discipline that exploits the peculiarities of quantum computation for machine learning tasks. Quantum-inspired machine learning has demonstrated relevant benefits for machine learning problems without employing quantum computers. This study introduces a quantum-inspired classifier for multi-class classification that outperforms standard binary classifiers in terms of accuracy and time complexity.
APPLIED SOFT COMPUTING
(2023)
Article
Optics
Shweta Mittal, Ankur Saharia, Yaseera Ismail, Francesco Petruccione, Anton V. Bourdine, Oleg G. Morozov, Vladimir V. Demidov, Juan Yin, Ghanshyam Singh, Manish Tiwari
Summary: This work presents the design and simulation of an all-optical sensor based on surface plasmon resonance effect on a spiral shaped photonic crystal fiber structure for detection of different cancer cells. The sensor showed high sensitivity and resolution for detecting breast cancer cells, with potential applications for detecting other types of cancer such as cervical cancer, skin cancer, blood cancer, and adrenal gland cancer.
Article
Materials Science, Multidisciplinary
Anton V. Bourdine, Vladimir V. Demidov, Konstantin V. Dukelskii, Alexander V. Khokhlov, Egishe V. Ter-Nersesyants, Sergei V. Bureev, Alexandra S. Matrosova, Grigori A. Pchelkin, Artem A. Kuznetsov, Oleg G. Morozov, Ilnur I. Nureev, Airat Zh. Sakhabutdinov, Timur A. Agliullin, Michael V. Dashkov, Alexander S. Evtushenko, Elena S. Zaitseva, Alexander A. Vasilets, Azat R. Gizatulin, Ivan K. Meshkov, Yaseera Ismail, Francesco Petruccione, Ghanshyam Singh, Manish Tiwari, Juan Yin
Summary: This article presents a fabricated silica few-mode microstructured optical fiber (MOF) with a special six GeO2-doped core geometry. The fiber has an outer diameter of 125 mu m and improved induced twisting up to 500 revolutions per 1 m. The article discusses the technological aspects and issues of manufacturing twisted MOFs with complicated structures and geometry.
Article
Quantum Science & Technology
Matt Lourens, Ilya Sinayskiy, Daniel K. Park, Carsten Blank, Francesco Petruccione
Summary: Quantum circuit algorithms, like neural and tensor networks, require hierarchical, modular and repeating architectural design choices. Neural Architecture Search (NAS) automates neural network design and achieves state-of-the-art performance. We propose a framework for representing quantum circuit architectures using NAS techniques, enabling search space design and architecture search. We demonstrate the importance of circuit architecture in quantum machine learning by generating Quantum Convolutional Neural Networks (QCNNs) and evaluating them on a music genre classification dataset. We also employ a genetic algorithm for Quantum Phase Recognition (QPR) as an example of architecture search, and provide an open-source Python package for dynamic circuit creation and NAS circuit search space design.
NPJ QUANTUM INFORMATION
(2023)
Article
Quantum Science & Technology
Israel F. Araujo, Daniel K. Park, Teresa B. Ludermir, Wilson R. Oliveira, Francesco Petruccione, Adenilton J. da Silva
Summary: The theory of quantum algorithms promises the benefits of using the laws of quantum mechanics to solve computational problems. However, a prerequisite for applying these algorithms is loading classical data onto a quantum state. Existing methods either require linear growth in quantum circuit depth or width, nullifying the advantage of representing exponentially many classical data in a quantum state. This paper presents a configurable bidirectional procedure that balances the trade-off between quantum circuit width and depth, allowing for sublinear growth when encoding N-dimensional classical data.
QUANTUM INFORMATION PROCESSING
(2023)
Article
Optics
Adenilton J. da Silva, Daniel K. Park
Summary: The paper presents a systematic procedure for decomposing multiqubit controlled unitary gates into controlled -NOT and single-qubit gates to minimize quantum circuit depth. The algorithm does not require ancillary qubits and achieves a quadratic reduction in circuit depth compared to known methods.
Article
Physics, Multidisciplinary
Kimara Naicker, Ilya Sinayskiy, Francesco Petruccione
Summary: The hierarchical equations of motion (HEOM) are used to simulate the dynamics of an open quantum system, and a classical machine learning (ML) approach is employed to solve the computational problem. The ML models, including convolutional neural networks, are capable of accurately predicting Hamiltonian parameters with a 99.28% accuracy rate.
PHYSICAL REVIEW RESEARCH
(2022)
Article
Optics
Vinayak Jagadish, R. Srikanth, Francesco Petruccione
Summary: This article studies the convex combinations of (d+1)-generalized Pauli dynamical maps in a Hilbert space of dimension d. It is found that for certain choices of the decoherence function, the maps are noninvertible and this noninvertibility remains under convex combinations. We evaluate the fraction of invertible maps obtained upon mixing for dynamical maps characterized by a specific decoherence function, and observe that this fraction increases superexponentially with dimension d.
Article
Optics
Vinayak Jagadish, R. Srikanth, Francesco Petruccione
Summary: This study investigates the conditions under which a semigroup is obtained by convex combinations of channels, specifically focusing on the set of Pauli and generalized Pauli channels. The findings show that merely mixing semigroups cannot result in a semigroup. Contrary to intuition, it is discovered that for a convex combination to yield a semigroup, the majority of input channels must be noninvertible.
Proceedings Paper
Computer Science, Artificial Intelligence
Shivani Mahashakti Pillay, Ilya Sinayskiy, Edgar Jembere, Francesco Petruccione
Summary: This study demonstrates the principle of quantum-kernel-based classifiers applied to non-linearly separable datasets. By applying different post-processing strategies to the kernel matrices, the accuracy of the classifiers can be improved. Quantum-kernel-based classifiers show high effectiveness in the Noisy Intermediate Scale Quantum (NISQ) computing era.
ARTIFICIAL INTELLIGENCE RESEARCH, SACAIR 2021
(2022)
