Applications In Quantum Computing Research Articles

Tomography of a Spatially Resolved Single-Atom Detector in the Presence of Shot-to-Shot Number Fluctuations

The tomography of single-particle-resolved detectors is of primary importance for characterizing particle correlations, with applications in quantum metrology, quantum simulation, and quantum computing. However, it is a nontrivial task in practice due to the uncertainties on the statistics of the state impinging on the detector and to the unavoidable presence of noise that affects the measurement but does not originate from the detector. In this work, we address this problem for a three-dimensional single-atom-resolved detector where shot-to-shot atom-number fluctuations are a central issue in performing a quantum detector tomography. We overcome this difficulty by exploiting the parallel measurement of the counting statistics in subvolumes of the detector, from which we evaluate the effect of shot-to-shot fluctuations and perform a local tomography of the detector. In addition, we illustrate the validity of our method from applying it to Gaussian quantum states with different number statistics. Finally, we show that the response of microchannel plate detectors is well described by using a binomial distribution with the detection efficiency as a single parameter. Published by the American Physical Society 2024

Observation of planar Hall effect in the topological insulator NaCd4As3

The observation of the planar Hall effect (PHE) illuminates the spin textures and topological properties of materials, indicating potential applications in quantum computing and electronic devices. Here, we present a study on the planar Hall transport of topological insulator NaCd4As3 single crystals. When the magnetic field is rotated within the sample plane relative to the current direction, we observe remarkable planar Hall resistivity and giant planar anisotropic magnetoresistance (AMR), both consistent with the theoretical expression of the PHE. Further analysis reveals that the orbital magnetoresistance effect, unrelated to surface electrons from topological surface states or bulk electrons from nontrivial Berry phases, lays a dominant role in the PHE in NaCd4As3. Additionally, the AMR ratio reaches −43% at 3 K under 14 T and remains −9% at room temperature, markedly exceeding that of traditional ferromagnetic metals. These findings provide a platform for understanding the PHE mechanism in topological insulators and highlight the potential of NaCd4As3 for angle and magnetic field detection applications.

Low-noise soliton molecule generation with an in-phase controllable pulse number by third- and second-order dispersion compensation in an all-fiber erbium-doped oscillator

We report on the experimental results of high-order soliton molecule (SM) generation in an erbium-doped oscillator. The generation of 14 solitons with an average power of 48 mW, a pulse duration of 508 fs, a separation of 2.59 ps between pulses, and a repetition rate of 19 MHz at 1560 nm central wavelength was demonstrated. The stability of this generation was also evidenced by its low relative intensity noise (0.08% in the frequency range [10 kHz–1 MHz]), and a low Allan deviation of frequency repetition rate of 1.98⋅10−9 at an interval of 1 s (during 10 h of free-running measurements). Moreover, the accurate manipulation of SM order by changing the pump power was investigated; we showed the ability to generate with four, six, seven, nine, 10, 11, 13, and 14 pulses with high stable states between solitons. The change in the number of pulses occurred repeatably by increasing and decreasing the pump power, offering a stable source of a controllable number of pulses for potential applications in quantum computing.

Advances in flight control systems for modern commercial aircraft

Abstract. In consequence of the accelerated evolution of aviation technology, the flight control systems of commercial aircraft have undergone substantial technological advancement and system optimization. This paper presents a comprehensive analysis of the recent advancements and future trends in flight control systems for commercial aircraft. The fundamental concepts and historical evolution of flight control systems are delineated, with a particular emphasis on the evolution of autopilot systems and the technological advancements of flight control systems (FCS). Furthermore, it examines innovations in flight control algorithms, including adaptive control and intelligent algorithms, and investigates the impact of fault-tolerant design on flight safety. In terms of system integration, the integration of flight control systems with navigation and communication systems, as well as performance enhancements resulting from system optimization, is explored. In addition, it examines the potential impact of intelligence and networking on future flight control systems, with a particular focus on the prospects for the application of artificial intelligence, quantum computing, and new material technologies. The synthesis of these areas provides a systematic understanding of the technological evolution and future development of flight control systems for commercial aircraft.

Analysis of the Principles of Quantum Computing and State-of-the-Art Applications

Abstract. Contemporarily, quantum computing has emerged as a promising field, offering potential breakthroughs in various computational tasks that are currently limited by classical computing. With this in mind, this study delves into the principles of quantum computing, exploring the fundamental concept of quantum entanglement and its implications for computation. After outlining the historical development and research significance of quantum computing, this research presents an overview of the latest advancements in the field. The paper then focuses on the principles of quantum computation, including the use of qubits and quantum gates, illustrated with relevant mathematical formulations and diagrams. Furthermore, this study discusses the state-of-the-art applications of quantum computing, showcasing recent achievements and results obtained from these cutting-edge technologies. A comparative analysis with traditional algorithms highlights the advantages and potential gains offered by quantum computing. Finally, the current limitations of quantum computing are discussed and the insights into future research directions and prospects are proposed for this exciting field.

Qubit: The Leap into Quantum Computation

Abstract. This article delves into the fundamental aspects of qubits in quantum computation, emphasizing their coordination, mathematical representation through the Bloch Sphere, and the unique advantages and challenges of superconducting, trapped ion, and photonic qubits. It highlights the transformative potential of quantum algorithms in solving complex problems in cryptography and information security, while addressing key sources of quantum errors such as decoherence and quantum noise. The study discusses advanced error correction methods and underscores the necessity of improving qubit coherence, stability, scalability, and integration for the practical application and commercialization of quantum computers. Ongoing research and collaboration are deemed essential for overcoming technical challenges and unlocking the full potential of quantum computation across various industries.

Quantum Entanglement and Nonlocality: Challenging the Local Realism of Classical Physics

Abstract. This paper delves into the significance and impact of quantum entanglement across the fields of physics, information science, and philosophy. Utilizing a literature review approach, the paper first introduces the concept and historical background of quantum entanglement, emphasizing its challenge to the limitations of classical physics and its potential applications in quantum communication and quantum computing. This paper thoroughly explains the theoretical foundations of the Einstein-Podolsky-Rosen experiment and Bell's inequality, along with related experimental validations and key results. In particular, it elucidates how quantum entanglement reveals the nonlocality in quantum mechanics and the characteristics of nonlocal information transfer. The discussion then shifts to the challenge quantum entanglement poses to physical ontology, exploring the philosophical implications and the new perspectives it offers in information theory. Finally, this study concludes by summarizing the profound impact of quantum entanglement as one of the most challenging and inspiring research areas in contemporary physics on scientific theory and philosophical thought, as well as anticipating future research directions and potential applications.

Hybrid Hamiltonian Simulation for Excitation Dynamics.

Hamiltonian simulation is one of the most anticipated applications of quantum computing. Quantum circuit depth for implementing Hamiltonian simulation is commonly time dependent using Trotter-Suzuki product formulas so that long time quantum dynamic simulations (QDSs) become impratical for near-term quantum processors. Hamiltonian simulation based on Cartan decomposition (CD) provides an appealing scheme for QDSs with fixed-depth circuits, while it is limited to a time-independent Hamiltonian. In this work, we generalize this CD-based Hamiltonian simulation algorithm for studying time-dependent systems by combining it with variational quantum algorithms. The time-dependent and time-independent parts of the Hamiltonian are treated by using variational and CD-based Hamiltonian simulation algorithms, respectively. As such, this hybrid Hamiltonian simulation requires only fixed-depth quantum circuits to handle time-dependent cases while maintaining a high accuracy. We apply this new algorithm to study the response of spin and molecular systems to δ-kick electric fields and obtain accurate spectra for these excitation processes.

Single-Step Synthesis of An Ideal Chain Antiferromagnet [H2(4,4'-bipyridyl)](H3O)2Fe2F10 with Spin S=5/2.

One-dimensional (1D) magnets are of great interest owing to their intriguing quantum phenomena and potential application in quantum computing. We successfully synthesized an ideal antiferromagnetic spin S=5/2 chain compound [H2(4,4'-bpy)](H3O)2Fe2F10 (4,4'-bpy=4,4'-bipyridyl) 1, using a single-step low-temperature hydrothermal method under conditions that favors the protonation of the bulky bidentate ligand 4,4'-bpy. Compound 1 consists of well-separated (Fe3+-F-)∞ chains with a large Fe-F-Fe angle of 174.8°. Both magnetic susceptibility and specific heat measurements show that 1 does not undergo a magnetic long-range ordering down to 0.5 K, despite the strong Fe-F-Fe intrachain spin exchange J with J/kB=-16.2(1) K. This indicates a negligibly weak interchain spin exchange J'. The J'/J value estimated for 1 is extremely small (<2.8×10-6), smaller than those reported for all other S=5/2 chain magnets. Our hydrothermal synthesis incorporates both [H2(4,4'-bpy)]2+ and (H3O)+ cations into the crystal lattice with numerous hydrogen bonds, hence effectively separating the (Fe3+-F-)∞ spin chains. This single-step hydrothermal synthesis under conditions favoring the protonation of bulky bidentate ligands offers an effective synthetic strategy to prepare well-separated 1D spin chain systems of magnetic ions with various spin values.

Single‐Step Synthesis of An Ideal Chain Antiferromagnet [H2(4,4′‐bipyridyl)](H3O)2Fe2F10 with Spin S=5/2

AbstractOne‐dimensional (1D) magnets are of great interest owing to their intriguing quantum phenomena and potential application in quantum computing. We successfully synthesized an ideal antiferromagnetic spin S=5/2 chain compound [H2(4,4′‐bpy)](H3O)2Fe2F10 (4,4′‐bpy=4,4′‐bipyridyl) 1, using a single‐step low‐temperature hydrothermal method under conditions that favors the protonation of the bulky bidentate ligand 4,4′‐bpy. Compound 1 consists of well‐separated (Fe3+−F−)∞ chains with a large Fe−F−Fe angle of 174.8°. Both magnetic susceptibility and specific heat measurements show that 1 does not undergo a magnetic long‐range ordering down to 0.5 K, despite the strong Fe−F−Fe intrachain spin exchange J with J/kB=−16.2(1) K. This indicates a negligibly weak interchain spin exchange J′. The J′/J value estimated for 1 is extremely small (&lt;2.8×10−6), smaller than those reported for all other S=5/2 chain magnets. Our hydrothermal synthesis incorporates both [H2(4,4′‐bpy)]2+ and (H3O)+ cations into the crystal lattice with numerous hydrogen bonds, hence effectively separating the (Fe3+−F−)∞ spin chains. This single‐step hydrothermal synthesis under conditions favoring the protonation of bulky bidentate ligands offers an effective synthetic strategy to prepare well‐separated 1D spin chain systems of magnetic ions with various spin values.

Survey Paper on Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation

Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of 78.8%. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance.

Efficient Implementation of Interior-Point Methods for Quantum Relative Entropy

Quantum relative entropy (QRE) programming is a recently popular and challenging class of convex optimization problems with significant applications in quantum computing and quantum information theory. We are interested in modern interior-point (IP) methods based on optimal self-concordant barriers for the QRE cone. A range of theoretical and numerical challenges associated with such barrier functions and the QRE cones have hindered the scalability of IP methods. To address these challenges, we propose a series of numerical and linear algebraic techniques and heuristics aimed at enhancing the efficiency of gradient and Hessian computations for the self-concordant barrier function, solving linear systems, and performing matrix-vector products. We also introduce and deliberate about some interesting concepts related to QRE such as symmetric quantum relative entropy. We design a two-phase method for performing facial reduction that can significantly improve the performance of QRE programming. Our new techniques have been implemented in the latest version (DDS 2.2) of the software package Domain-Driven Solver (DDS). In addition to handling QRE constraints, DDS accepts any combination of several other conic and nonconic convex constraints. Our comprehensive numerical experiments encompass several parts, including (1) a comparison of DDS 2.2 with Hypatia for the nearest correlation matrix problem, (2) using DDS 2.2 for combining QRE constraints with various other constraint types, and (3) calculating the key rate for quantum key distribution (QKD) channels and presenting results for several QKD protocols. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This work was supported by the National Science Foundation [Grant CMMI-2347120] and Discovery Grants from the Natural Sciences and Engineering Research Council of Canada. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2024.0570 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2024.0570 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Tunable Liquid Crystal Twisted‐Planar Fresnel Lens for Vortex Topology Determination

AbstractThe development of straightforward and effective methods to determine the topological charge of phase singular beams is crucial for broadening the applications of optical vortices. Here, a straightforward and elegant method for determining the topological charge of an optical vortex using an innovative switchable achromatic nematic liquid crystal Fresnel lens is proposed. The approach allows for the unambiguous determination of both the absolute value and the sign of the topological charge of a singular beam across a wide range of the visible spectrum, as demonstrated both experimentally and theoretically. The research outcomes are promising for applications in optical information transmission systems, cryptography, and quantum computing.

Reverse-Engineered Exact Control of Population Transfer in Lossy Nonlinear Three-State Systems

We introduce a reverse-engineered scheme for achieving the precise control of population transfer in nonlinear quantum systems characterized by a 1:2 resonance. This scheme involves the use of two resonant laser pulses that transition from initial and final states to an intermediate level exhibiting irreversible losses. In comparison to alternative techniques, our approach offers computational efficiency advantages. Notably, the analytically defined form of the pump pulse enables tailored control strategies, enhancing robustness against decoherence and imperfections. This flexibility extends to choosing dump pulses and designing time evolution scenarios. These features open doors for practical implementation and scalability in quantum technologies, with potential applications in quantum information processing, quantum computing, and quantum communication.

Option pricing under stochastic volatility on a quantum computer

We develop quantum algorithms for pricing Asian and barrier options under the Heston model, a popular stochastic volatility model, and estimate their costs, in terms of T-count, T-depth and number of logical qubits, on instances under typical market conditions. These algorithms are based on combining well-established numerical methods for stochastic differential equations and quantum amplitude estimation technique. In particular, we empirically show that, despite its simplicity, weak Euler method achieves the same level of accuracy as the better-known strong Euler method in this task. Furthermore, by eliminating the expensive procedure of preparing Gaussian states, the quantum algorithm based on weak Euler scheme achieves drastically better efficiency than the one based on strong Euler scheme. Our resource analysis suggests that option pricing under stochastic volatility is a promising application of quantum computers, and that our algorithms render the hardware requirement for reaching practical quantum advantage in financial applications less stringent than prior art.

Quantum Machine Learning for Advanced Data Processing in Business Analytics: A Path Toward Next-Generation Solutions

Quantum Machine Learning (QML) is a rising paradigm of computing that holds high possibilities to revolutionise data analysis in the business analytics industry. This paper aims at presenting how QML can solve the increasingly challenging demands of big contributions to large-scale data analysis in business contexts, achieving better performance than orthodox machine learning methods in terms of time and efficacy. LIQUID: This research utilises actual business data and scenarios and solving problems by using quantum algorithms – VQE and QSVM. At the core of our approach, we perform extensive validation of QML models on platforms such as IBM’s Quantum Experience and D-Wave systems pertaining to critical use cases for businesses would include financial modelling, supply chain management, and customer behaviour prediction. The results presented here suggest that quantum algorithm speeds processing by 30-50% when compared with conventions approaches of computation. Moreover, QML has shown better results for prediction in analytics for various firms and provide them better aptitudes for decision-making. Hence the uniqueness of this research by grounding real world business problems in quantum algorithms and demonstrating empirically how superior they are compared to the classical alternatives in dealing with large datasets. Considering the future of business analytics, QML looks set to be a real game changer for industries that heavily rely on big data analysis. In this it has filled a gap which would cause a scholarly without hard gap between theoretical quantum computing and real business applications.

Doubly Optimal Parallel Wire Cutting without Ancilla Qubits

A restriction in the quality and quantity of available qubits presents a substantial obstacle to the application of near-term and early fault-tolerant quantum computers in practical tasks. To confront this challenge, some techniques for effectively augmenting the system size through classical processing have been proposed; one promising approach is quantum circuit cutting. The main idea of quantum circuit cutting is to decompose an original circuit into smaller subcircuits and combine outputs from these subcircuits to recover the original output. Although this approach enables us to simulate larger quantum circuits beyond physically available circuits, it needs classical overheads quantified by two metrics: the sampling overhead in the number of measurements to reconstruct the original output, and the number of channels in the decomposition. Thus, it is crucial to devise a decomposition method that minimizes both of these metrics, thereby reducing the overall execution time. This paper studies the problem of decomposing the n-qubit identity channel, i.e., n-parallel wire cutting, into a set of local operations and classical communication; then we give an optimal wire-cutting method composed of channels based on mutually unbiased bases that achieves minimal overheads in both the sampling overhead and the number of channels, without ancilla qubits. This is in stark contrast to the existing method that achieves the optimal sampling overhead yet with ancilla qubits. Moreover, we derive a tight lower bound on the number of channels in parallel wire cutting without ancilla systems and show that only our method achieves this lower bound among the existing methods. Notably, our method shows an exponential improvement in the number of channels, compared to the aforementioned ancilla-assisted method that achieves optimal sampling overhead. Our work significantly alleviates the additional overheads for the wire-cutting method and may provide essential components for the early success of quantum computing. Published by the American Physical Society 2024

Satellite dish-like nanocomposite as a breakthrough in single photon detection for highly developed optoelectronic applications

A nanocomposite with a unique satellite dish-like structure, termed arsenic (III) oxide iodide (AsO2I)/polypyrrole (Ppy) intercalated with iodide ions (AsO2I/Ppy-I), has been meticulously developed via a two-step process. It features natural satellite dish-like nanostructures, potentially serving as nanoresonators for efficient single photon absorption. With a bandgap of 2.8 eV, the AsO2I/Ppy-I nanocomposite efficiently absorbs photons in the UV and visible light regions, making it suitable for single photon detection. Impressive performance is seen in photocurrent sensitivity measurements, recording values of 0.017 mA.cm−2 under white light and 0.009 mA.cm−2 under monochromatic light at 340 nm. Additionally, it exhibits high responsivity and detectivity, with peak values at wavelengths of 340 nm and 440 nm associated with the diameter of the Satellite dish nanostructure. Cost-effectiveness and simple synthesis methods make it attractive for industrial applications, while its unique structural characteristics and enhanced optical properties position it as a valuable asset in optoelectronic technologies. It holds promise as a leading material in advanced quantum technology, marking a significant leap forward in optoelectronic technologies, with potential applications in quantum cryptography, communication, and computing.

A Study of q-Deformed Bosons, and Their Implications to Quantum Optics

In this study, we investigate three types of q-deformed boson oscillators, focusing on their mathematical frameworks and thermodynamic properties. We calculate key thermodynamic quantities, such as internal energy and entropy, as functions of the deformation parameter q. Our results reveal that these oscillators are eigenstates of specific deformed boson annihilation operators. We also analyze their unique characteristics and implications in deformed quantum optics. Furthermore, we examine the impact of q-deformation on qutrit logic gates, including cycle, self-shift, controlled cycle, controlled self-shift, Feynman, ternary Toffoli, and Fredkin gates, highlighting their altered computational properties. This research contributes to a deeper understanding of q-deformed systems and their applications in quantum computing. Overall, it opens new avenues for exploring the interplay between deformation parameters and quantum information processing.