Applications In Quantum Computing Research Articles

Optical vortex ladder via Sisyphus pumping of Pseudospin

Robust high-order optical vortices are much in demand for applications in optical manipulation, optical communications, quantum entanglement and quantum computing. However, in numerous experimental settings, a controlled generation of optical vortices with arbitrary orbital angular momentum remains a challenge. Here, we present a concept of “optical vortex ladder” for the stepwise generation of optical vortices through Sisyphus pumping of pseudospin modes in photonic graphene. The ladder is applicable in various lattices with Dirac-like structures. Instead of conical diffraction and incomplete pseudospin conversion under conventional Gaussian beam excitations, the vortices produced in the ladder arise from non-trivial topology and feature diffraction-free Bessel profiles, thanks to the refined excitation of the ring spectrum around the Dirac cones. By employing a periodic “kick” to the photonic graphene, effectively inducing the Sisyphus pumping, the ladder enables tunable generation of optical vortices of any order even when the initial excitation does not involve any orbital angular momentum. The optical vortex ladder stands out as an intriguing non-Hermitian dynamical system, and, among other possibilities, opens a pathway for applications of topological singularities in beam shaping and wavefront engineering.

Design principles for >90% efficiency and >99% indistinguishability broadband quantum dot cavities

Abstract Quantum dots have the potential to be one of the brightest deterministic single photon sources with high end applications in quantum computing and cluster state generation. In this work, we re-examine the design of simple micropillars by meticulously examining the structural effects of the decay into leaky channels beyond an atom-like cavity estimation. We show that precise control of the side losses with the diameter and avoidance of propagating Bloch modes in the distributed Bragg reflectors can result in easy-to-manufacture broadband (Q≈ 750 − 2500) micropillars, allowing for broad optical coherent control pulses necessary for high single photon purity (> 99.2 − 99.99% achievable) while simultaneously demonstrating extremely high efficiency out the top (90.5% − 96.4%). We also demonstrate that such cavities naturally decouple from the phonon sideband, with the phonon sideband reducing by a factor of 5 − 33 allowing us to predict that the photons should show 98.5% − 99.8% indistinguishability without the need for filtering.

Possible application of quantum computing in the field of ocean engineering: optimization of an offshore wind farm layout with the Ising model

AbstractAs a possible application of quantum computing in the field of ocean engineering, an attempt is made to identify the optimum layout of an offshore wind farm with the quasi-quantum annealing method while assuming a wake model that conforms to the standard Jensen–Katic wake model but can be implemented in the Ising model. The optimum layouts obtained in the present study are compared with those identified with other conventional methods such as the genetic algorithm. It is confirmed that the quasi-quantum annealing method works as well as other methods in identifying the optimum layouts, while the optimum layout of an offshore wind farm composed of even more than 100 wind turbines can be identified with reasonable CPU time.

Application of Big Data and Quantum Computing in the Secure Federated Internet of Things

Federated Internet of Things (IoT) presents both unprecedented opportunities and challenges in security and data management. This study explores the integration of big data analytics and Quantum Computing as potential solutions to address security concerns within the Federated IoT ecosystem. The study examines the implications of leveraging big data analytics to process and analyze the massive volume of data generated by IoT devices. Advanced analytics techniques, including machine learning and anomaly detection algorithms, are employed to enhance the detection and mitigation of security threats such as unauthorized access, data breaches and malicious attacks. Furthermore, the study investigates the role of Quantum Computing infrastructure in providing scalable and reliable resources for securely storing, processing and transmitting IoT data. By offloading computational tasks to quantum-based platforms, the aim is to alleviate the burden on edge devices while ensuring robust security measures are in place to safeguard sensitive information. A comprehensive review of existing literature and case studies identifies key challenges and opportunities in implementing big data and Quantum Computing solutions within the Federated IoT environment. The study also proposes potential frameworks and methodologies for integrating these technologies effectively, considering factors such as data privacy, scalability and interoperability. Overall, this research aims to advance secure IoT systems by leveraging big data analytics and cloud computing. By addressing security concerns proactively and adopting innovative approaches, the goal is to create a more resilient and trustworthy Federated IoT ecosystem, benefiting society at large.

Quantum computing integration with multi-cloud architectures: enhancing computational efficiency and security in advanced cloud environments

The objective of this research is to explore the integration of quantum computing with multi-cloud architectures, aiming to enhance computational efficiency and security in advanced cloud environments. The study seeks to identify the potential benefits and challenges of incorporating quantum computing capabilities within a multi-cloud framework and to evaluate the impact on performance and security metrics. The research employs a hybrid methodological approach, combining both theoretical analysis and practical implementation. Initially, a detailed literature review is conducted to understand the current state of quantum computing and multi-cloud architectures. This is followed by the design and development of an integration framework that leverages quantum computing technologies in a multi-cloud environment. Key steps include developing a multi-cloud architecture that integrates quantum computing resources alongside classical computing resources, deploying quantum algorithms and protocols within the multi-cloud setup, implementing advanced security measures to protect data and computational processes, using a set of predefined metrics to evaluate computational efficiency and security, and employing statistical tools and techniques to analyze the collected data and draw meaningful insights. The integration of quantum computing with multi-cloud architectures resulted in significant improvements in computational efficiency, particularly in tasks that are traditionally resource-intensive. Key findings include enhanced computational speed, where quantum algorithms demonstrated superior performance in solving complex problems compared to classical algorithms, optimized resource utilization through dynamic allocation of quantum and classical resources leading to cost efficiency, improved security with quantum-enhanced protocols providing robust protection against cyber threats, and high scalability of the integrated architecture to accommodate increasing computational demands without compromising performance. The research concludes that integrating quantum computing with multi-cloud architectures offers substantial benefits in terms of computational efficiency and security. The findings indicate that such integration can revolutionize cloud computing, providing a powerful platform for handling complex computations and enhancing data security. However, the study also highlights several challenges, including the need for specialized hardware, the complexity of integration, and the necessity for ongoing research to fully harness the potential of quantum computing in cloud environments. Future research should focus on addressing these challenges and exploring further applications of quantum computing in various cloud-based scenarios.

Crystal Growth, Structure, and Diverse Magnetic Behaviors in Frustrated Triangular Lattice REBO3 (RE = Tb-Yb).

Triangular lattice (TL) materials are a rich playground for investigating exotic quantum spin states and related applications in quantum computing and quantum information. Millimeter-level single crystals of REBO3 (RE = Tb-Yb) with a nearly perfect RE-based TL have been successfully grown via a high-temperature flux method and structurally characterized via single-crystal X-ray diffraction. These 113-type materials crystallize in a monoclinic crystal system with a C2/c space group. Anisotropic magnetism and dominant antiferromagnetic interactions are found for the above materials based on DC magnetic susceptibility measurements. The comprehensive low-temperature specific heat data of REBO3 (RE = Tb-Tm) are characterized on single crystals for the first time, which exhibit diverse magnetic behaviors. Specifically, two weak-field-induced transitions could be found in the case of DyBO3 based on the specific heat measurements. Our results suggest that REBO3 (RE = Tb-Yb) is a TL magnetic system for investigating potential quantum magnetism.

Grating Coupler Design for Low-Cost Fabrication in Amorphous Silicon Photonic Integrated Circuits

Photonic circuits find applications in biomedicine, manufacturing, quantum computing and communications. Photonic waveguides are crucial components, typically having cross-section orders of magnitude inferior when compared with other photonic components (e.g., optical fibers, light sources and photodetectors). Several light-coupling methods exist, consisting of either on-plane (e.g., adiabatic and end-fire coupling) or off-plane methods (e.g., grating and vertical couplers). The grating coupler is a versatile light-transference technique which can be tested at wafer level, not requiring specific fiber terminations or additional optical components, like lenses, polarizers or prisms. This study focuses on fully-etched grating couplers without a bottom reflector, made from hydrogenated amorphous silicon (a-Si:H), deposited over a silica substrate. Different coupler designs were tested, and of these we highlight two: the superimposition of two lithographic masks with different periods and an offset between them to create a random distribution and a technique based on the quadratic refractive-index variation along the device’s length. Results were obtained by 2D-FDTD simulation. The designed grating couplers achieve coupling efficiencies for the TE-like mode over −8 dB (mask overlap) and −3 dB (quadratic variation), at a wavelength of 1550 nm. The coupling scheme considers a 220 nm a-Si:H waveguide and an SMF-28 optical fiber.

A Chronicle of Quantum Technologies in Game and Software Development.

The ongoing second quantum revolution, marked by advancements harnessing quantum phenomena, has permeated various fields, including communications, computation, and networking, collectively known as quantum technologies (QT). Quantum computing, a focal point within QT, has led to the emergence of quantum games, a novel research and development area exploring creative applications of quantum computing in game development. While more than 300 quantum games have been developed by enthusiasts and commercial parties, a comprehensive research program or state-of-the-art review is notably absent. This paper addresses this gap by conducting a thorough literature review, presenting current advancements and examples in quantum games and interactive designs, and exploring future prospects for quantum technologies in game development practices.

Computing marginal and conditional divergences between decomposable models with applications in quantum computing and earth observation

AbstractThe ability to compute the exact divergence between two high-dimensional distributions is useful in many applications, but doing so naively is intractable. Computing the $$\alpha \beta $$ α β -divergence—a family of divergences that includes the Kullback–Leibler divergence and Hellinger distance—between the joint distribution of two decomposable models, i.e., chordal Markov networks, can be done in time exponential in the treewidth of these models. Extending this result, we propose an approach to compute the exact $$\alpha \beta $$ α β -divergence between any marginal or conditional distribution of two decomposable models. In order to do so tractably, we provide a decomposition over the marginal and conditional distributions of decomposable models. We then show how our method can be used to analyze distributional changes by first applying it to the benchmark image dataset QMNIST and a dataset containing observations from various areas at the Roosevelt Nation Forest and their cover type. Finally, based on our framework, we propose a novel way to quantify the error in contemporary superconducting quantum computers.

Revolutionizing Healthcare: The Emerging Role of Quantum Computing in Enhancing Medical Technology and Treatment.

The healthcare sector faces complex challenges that call for innovative solutions to improve diagnostic accuracy, treatment efficacy, and data management. Quantum computing, with its unique capabilities, holds the potential to revolutionize various aspects of healthcare. This narrative review critically examines the existing literature on the application of quantum computing in healthcare, focusing on its utility in enhancing diagnostics, data processing, and treatment planning. Quantum computing's ability to handle large, complex datasets more efficiently than classical computers can significantly impact domains such as genomics, medical imaging, and personalized medicine. Quantum algorithms can accelerate the identification of genetic markers associated with diseases, facilitate the analysis of medical images, and optimize treatment plans based on individual genetic profiles. Moreover, quantum cryptography offers a robust security solution for safeguarding sensitive patient data, a critical need as healthcare increasingly relies on digital platforms. Despite the promising outlook, the integration of quantum computing into healthcare faces technical, ethical, and regulatory challenges. The delicate nature of quantum hardware, the need for error correction, and the scalability of quantum systems pose barriers to widespread adoption. Additionally, concerns around patient privacy and data security, as well as the need for updated regulatory frameworks, must be addressed. Ongoing research and collaborative efforts involving researchers, healthcare providers, and technology developers are crucial to overcoming these hurdles and realizing the full potential of quantum computing in transforming healthcare. As quantum computing continues to evolve, its impact on the future of healthcare could be profound, leading to earlier disease detection, more personalized treatments, and improved patient outcomes.For instance, quantum computing has already been applied to enhance drug discovery processes, with companies like D-Wave Systems (Burnaby, Canada) demonstrating faster molecular simulations for pharmaceutical research and IBM's (Armonk, USA) quantum systems being used to model chemical reactions for new drug development.

A new generalized perspective to establish vortex beams

Vortex beams are powerful primitives for optical operations and communications. We propose general mathematical methods to establish new vortex beams and optimize existing vortex beams. Based on these methods, two new types of vortex beams are demonstrated theoretically and experimentally. The propagation characteristics of these vortex beams can be finely controlled through specific parameters. Additionally, influences of periodic discontinuities and orbital angular momentum of two vortex beams are analyzed. The innovative concept of beam modulation and the introduction of new vortex beams in this paper indicate significant potential for applications in particle trapping, spatial communication, and quantum computation.

Majorana modes in striped two-dimensional inhomogeneous topological superconductors

Majorana zero modes have gained significant interest due to their potential applications in topological quantum computing and in the realization of exotic quantum phases. These zero-energy quasiparticle excitations localize at the vortex cores of two-dimensional topological superconductors or at the ends of one-dimensional topological superconductors. Here we describe an alternative platform: a two-dimensional topological superconductor with inhomogeneous superconductivity, where Majorana modes localize at the ends of topologically nontrivial one-dimensional stripes induced by the spatial variations of the order parameter phase. In certain regimes, these Majorana modes hybridize into a single highly nonlocal state delocalized over spatially separated points, with exactly zero energy at finite system sizes and with emergent quantum-mechanical supersymmetry. We then present detailed descriptions of braiding and fusion protocols and showcase the versatility of our proposal by suggesting possible setups that can potentially lead to the realization of Yang-Lee anyons and the Sachdev-Ye-Kitaev model.

Information compression via hidden subgroup quantum autoencoders

We design a quantum method for classical information compression that exploits the hidden subgroup quantum algorithm. We consider sequence data in a database with a priori unknown symmetries of the hidden subgroup type. We prove that data with a given group structure can be compressed with the same query complexity as the hidden subgroup problem, which is exponentially faster than the best-known classical algorithms. We moreover design a quantum algorithm that variationally finds the group structure and uses it to compress the data. There is an encoder and a decoder, along the paradigm of quantum autoencoders. After the training, the encoder outputs a compressed data string and a description of the hidden subgroup symmetry, from which the input data can be recovered by the decoder. In illustrative examples, our algorithm outperforms the classical autoencoder on the mean squared value of test data. This classical-quantum separation in information compression capability has thermodynamical significance: the free energy assigned by a quantum agent to a system can be much higher than that of a classical agent. Taken together, our results show that a possible application of quantum computers is to efficiently compress certain types of data that cannot be efficiently compressed by current methods using classical computers.

Investigating and improving student understanding of the basics of quantum computing

Quantum information science and engineering (QISE) is a rapidly developing field that leverages the skills of experts from many disciplines to utilize the potential of quantum systems in a variety of applications. It requires talent from a wide variety of traditional fields, including physics, engineering, chemistry, and computer science, to name a few. To prepare students for such opportunities, it is important to give them a strong foundation in the basics of QISE, in which quantum computing plays a central role. In this study, we discuss the development, validation, and evaluation of a Quantum Interactive Learning Tutorial, on the basics and applications of quantum computing. These include an overview of key quantum mechanical concepts relevant to quantum computation (including ways a quantum computer is different from a classical computer), properties of single- and multiqubit systems, and the basics of single-qubit quantum gates. The tutorial uses guided inquiry-based teaching-learning sequences. Its development and validation involved conducting cognitive task analysis from both expert and student perspectives and using common student difficulties as a guide. For example, before engaging with the tutorial, after traditional lecture-based instruction, one reasoning primitive that was common in student responses is that a major difference between an N-bit classical and N-qubit quantum computer is that various things associated with a number N for a classical computer should be replaced with the number 2N for a quantum computer (e.g., 2N qubits must be initialized and 2N bits of information are obtained as the output of the computation on the quantum computer). This type of reasoning primitive also led many students to incorrectly think that there are only N distinctly different states available when computation takes place on a classical computer. Research suggests that this type of reasoning primitive has its origins in students learning that quantum computers can provide exponential advantage for certain problems, e.g., Shor’s algorithm for factoring products of large prime numbers, and that the quantum state during the computation can be in a superposition of 2N linearly independent states. The inquiry-based learning sequences in the tutorial provide scaffolding support to help students develop a functional understanding. The final version of the validated tutorial was implemented in two distinct courses offered by the physics department with slightly different student populations and broader course goals. Students’ understanding was evaluated after traditional lecture-based instruction on the requisite concepts and again after engaging with the tutorial. We analyze and discuss their improvement in performance on concepts covered in the tutorial. Published by the American Physical Society 2024

Lattice holography on a quantum computer

We explore the potential application of quantum computers to the examination of lattice holography, which extends to the strongly coupled bulk theory regime. With adiabatic evolution, we compute the ground state of a spin system on a (2+1)-dimensional hyperbolic lattice, and measure the spin-spin correlation function on the boundary. Notably, we observe that with achievable resources for coming quantum devices, the correlation function demonstrates an approximate scale-invariant behavior, aligning with the pivotal theoretical predictions of the anti–de Sitter/conformal field theory correspondence. Published by the American Physical Society 2024

Measuring the Loschmidt Amplitude for Finite-Energy Properties of the Fermi-Hubbard Model on an Ion-Trap Quantum Computer

Calculating the equilibrium properties of condensed-matter systems is one of the promising applications of near-term quantum computing. Recently, hybrid quantum-classical time-series algorithms have been proposed to efficiently extract these properties from a measurement of the Loschmidt amplitude ⟨ψ|e−iH^t|ψ⟩ from initial states |ψ⟩ and a time evolution under the Hamiltonian H^ up to short times t. In this work, we study the operation of this algorithm on a present-day quantum computer. Specifically, we measure the Loschmidt amplitude for the Fermi-Hubbard model on a 16-site ladder geometry (32 orbitals) on the Quantinuum H2-1 trapped-ion device. We assess the effect of noise on the Loschmidt amplitude and implement algorithm-specific error-mitigation techniques. By using a thus-motivated error model, we numerically analyze the influence of noise on the full operation of the quantum-classical algorithm by measuring expectation values of local observables at finite energies. Finally, we estimate the resources needed for scaling up the algorithm. Published by the American Physical Society 2024

The Rise of Xene Hybrids.

Xenes, mono-elemental atomic sheets, exhibit Dirac/Dirac-like quantum behavior. When interfaced with other 2D materials such as boron nitride, transition metal dichalcogenides, and metal carbides/nitrides/carbonitrides, it enables them with unique physicochemical properties, including structural stability, desirable bandgap, efficient charge carrier injection, flexibility/breaking stress, thermal conductivity, chemical reactivity, catalytic efficiency, molecular adsorption, and wettability. For example, BN acts as an anti-oxidative shield, MoS2 injects electrons upon laser excitation, and MXene provides mechanical flexibility. Beyond precise compositional modulations, stacking sequences, and inter-layer coupling controlled by parameters, achieving scalability and reproducibility in hybridization is crucial for implementing these quantum materials in consumer applications. However, realizing the full potential of these hybrid materials faces challenges such as air gaps, uneven interfaces, and the formation of defects and functional groups. Advanced synthesis techniques, a deep understanding of quantum behaviors, precise control over interfacial interactions, and awareness of cross-correlations among these factors are essential. Xene-based hybrids show immense promise for groundbreaking applications in quantum computing, flexible electronics, energy storage, and catalysis. In this timely perspective, recent discoveries of novel Xenes and their hybrids are highlighted, emphasizing correlations among synthetic parameters, structure, properties, and applications. It is anticipated that these insights will revolutionize diverse industries and technologies.

Current status and future development of quantum computing

To overcome the scaling limitations of conventional semiconductor chips, quantum computing has emerged and experienced significant growth in recent years. Quantum computing utilizes resources such as quantum superposition and quantum entanglement to encode and process information. Compared to classical computing, this technology can provide exponential acceleration in the field of ultra-large-scale parallel computing. Consequently, its practical application is anticipated to have profound impacts on information and related technologies. The paper employs a comparative analysis approach to systematically examine the research progress of quantum computer technology. Specifically, the study emphasizes the analysis and consolidation of various quantum computing technology routes, critically evaluates their advantages and disadvantages, and provides a foresight into their future development trends. Currently, research on quantum computing technology is in its early stages and is evolving rapidly. Multiple technical paths are being pursued, resulting in a diverse and competitive landscape that is expected to persist in the short term, as it will take some time before the practical application of quantum computers becomes feasible.

Observation of In-Gap States in a Two-Dimensional CrI2/NbSe2 Heterostructure.

Low-dimensional magnetic structures coupled with superconductors are promising platforms for realizing Majorana zero modes, which have potential applications in topological quantum computing. Here, we report a two-dimensional (2D) magnetic-superconducting heterostructure consisting of single-layer chromium diiodide (CrI2) on a niobium diselenide (NbSe2) superconductor. Single-layer CrI2 nanosheets, which hold antiferromagnetic (AFM) ground states by our first-principles calculations, were epitaxially grown on the layered NbSe2 substrate. Using scanning tunneling microscopy/spectroscopy, we observed robust in-gap states spatially located at the edge of the nanosheets and defect-induced zero-energy peaks inside the CrI2 nanosheets. Magnetic-flux vortices induced by an external field exhibit broken 3-fold rotational symmetry of the pristine NbSe2 superconductor, implying the efficient modulation of the interfacial superconducting states by the epitaxial CrI2 layer. A phenomenological model suggests the existence of chiral edge states in a 2D AFM-superconducting hybrid system with an even Chern number, providing a qualitatively plausible understanding for our experimental observation.

A hybrid quantum computing pipeline for real world drug discovery

Quantum computing, with its superior computational capabilities compared to classical approaches, holds the potential to revolutionize numerous scientific domains, including pharmaceuticals. However, the application of quantum computing for drug discovery has primarily been limited to proof-of-concept studies, which often fail to capture the intricacies of real-world drug development challenges. In this study, we diverge from conventional investigations by developing a hybrid quantum computing pipeline tailored to address genuine drug design problems. Our approach underscores the application of quantum computation in drug discovery and propels it towards more scalable system. We specifically construct our versatile quantum computing pipeline to address two critical tasks in drug discovery: the precise determination of Gibbs free energy profiles for prodrug activation involving covalent bond cleavage, and the accurate simulation of covalent bond interactions. This work serves as a pioneering effort in benchmarking quantum computing against veritable scenarios encountered in drug design, especially the covalent bonding issue present in both of the case studies, thereby transitioning from theoretical models to tangible applications. Our results demonstrate the potential of a quantum computing pipeline for integration into real world drug design workflows.