Applications In Quantum Computing Research Articles

Applications of Post-Quantum Cryptography

With the constantly advancing capabilities of quantum computers, conventional cryptographic systems relying on complex math problems may encounter unforeseen vulnerabilities. Unlike regular computers, which are often deemed cost-ineffective in cryptographic attacks, quantum computers have a significant advantage in calculation speed. This distinction potentially makes currently used algorithms less secure or even completely vulnerable, compelling the exploration of post-quantum cryptography (PQC) as the most reasonable solution to quantum threats. This review aims to provide current information on applications, benefits, and challenges associated with the PQC. The review employs a systematic scoping review with the scope restricted to the years 2022 and 2023; only articles that were published in scientific journals were used in this paper. The review examined the articles on the applications of quantum computing in various spheres. However, the scope of this paper was restricted to the domain of the PQC because most of the analyzed articles featured this field. Subsequently, the paper is analyzing various PQC algorithms, including lattice-based, hash-based, code-based, multivariate polynomial, and isogeny-based cryptography. Each algorithm is being judged based on its potential applications, robustness, and challenges. All the analyzed algorithms are promising for the post-quantum era in such applications as digital signatures, communication channels, and IoT. Moreover, some of the algorithms are already implemented in the spheres of banking transactions, communication, and intellectual property. Meanwhile, despite their potential, these algorithms face serious challenges since they lack standardization, require vast amounts of storage and computation power, and might have unknown vulnerabilities that can be discovered only with years of cryptanalysis. This overview aims to give a basic understanding of the current state of post-quantum cryptography with its applications and challenges. As the world enters the quantum era, this review not only shows the need for strong security methods that can resist quantum attacks but also presents an optimistic outlook on the future of secure communications, guided by advancements in quantum technology. By bridging the gap between theoretical research and practical implementation, this paper aims to inspire further innovation and collaboration in the field.

Machine Learning Applications of Quantum Computing: A Review

At the intersection of quantum computing and machine learning, this review paper explores the transformative impact these technologies are having on the capabilities of data processing and analysis, far surpassing the bounds of traditional computational methods. Drawing upon an in-depth analysis of 32 seminal papers, this review delves into the interplay between quantum computing and machine learning, focusing on transcending the limitations of classical computing in advanced data processing and applications. This review emphasizes the potential of quantum-enhanced methods in enhancing cybersecurity, a critical sector that stands to benefit significantly from these advancements. The literature review, primarily leveraging Science Direct as an academic database, delves into the transformative effects of quantum technologies on machine learning, drawing insights from a diverse collection of studies and scholarly articles. While the focus is primarily on the growing significance of quantum computing in cybersecurity, the review also acknowledges the promising implications for other sectors as the field matures. Our systematic approach categorizes sources based on quantum machine learning algorithms, applications, challenges, and potential future developments, uncovering that quantum computing is increasingly being implemented in practical machine learning scenarios. The review highlights advancements in quantum-enhanced machine learning algorithms and their potential applications in sectors such as cybersecurity, emphasizing the need for industry-specific solutions while considering ethical and security concerns. By presenting an overview of the current state and projecting future directions, the paper sets a foundation for ongoing research and strategic advancement in quantum machine learning.

Quantum phase structural stability and switching in twist-graphenes

This study examines the electronic structure and potential energy surfaces of migration paths in various types of bilayer graphene. Using periodic boundary conditions, density functional theory (DFT), and the generalized gradient approximation (GGA) exchange–correlation functional, along with the nudged elastic band (NEB) method, to investigate the structural stability and dynamic equilibrium of twisted bilayer graphenes (TBGs) with twist angles of 13.2° and 21.8°. The results suggest that twist angles significantly impact atomic and electronic properties, including moiré patterns, superlattice periods, and interfragment distances, which in turn influence bilayer graphene strongly correlated electronic quantum states. This research elucidates the fundamental mechanisms of superlubricity and mutual migration pathways of graphene fragments in TBGs. The low migration barriers observed could facilitate transitions between different energy-related phases, which are determined by the lattice moiré patterns and the localization character of the electronic states, resulting in superlubricity. External mechanical factors may affect the quantum properties of TBGs, indicating potential applications in quantum computing and quantum sensing.

Path integrals, complex probabilities and the discrete Weyl representation

A discrete formulation of the real-time path integral as the expectation value of a functional of paths with respect to a complex probability on a sample space of discrete valued paths is explored. The formulation in terms of complex probabilities is motivated by a recent reinterpretation of the real-time path integral as the expectation value of a potential functional with respect to a complex probability distribution on cylinder sets of paths. The discrete formulation in this work is based on a discrete version of the Weyl algebra that can be applied to any observable with a finite number of outcomes. The origin of the complex probability in this work is the completeness relation. In the discrete formulation the complex probability exactly factors into products of conditional probabilities and exact unitarity is maintained at each level of approximation. The approximation of infinite dimensional quantum systems by discrete systems is discussed. The method is illustrated by applying it to scattering theory and quantum field theory. The implications of these applications for quantum computing is discussed.

Quantum computing in anthropological research: An interdisciplinary approach

This paper investigates the application of quantum computing in anthropological research. By leveraging quantum algorithms and computational capabilities, we propose innovative methodologies for analyzing complex cultural datasets, modeling social interactions, and reconstructing historical evolutionary patterns. The integration of quantum computing into anthropology has the potential to revolutionize the field by providing unprecedented computational efficiency and novel analytical tools.

Narrow-linewidth exciton-polariton laser

Exciton-polariton lasers are a promising source of coherent light for low-energy applications due to their low-threshold operation. However, a detailed experimental study of their spectral purity, which directly affects their coherence properties, is still missing. Here, we present a high-resolution spectroscopic investigation of the energy and linewidth of an exciton-polariton laser in the single-mode regime, which derives its coherent emission from an optically pumped and confined exciton-polariton condensate. We report an ultra-narrow linewidth of 56 MHz or 0.24 µeV, corresponding to a coherence time of 5.7 ns. The narrow linewidth is consistently achieved by using an exciton-polariton condensate with a high photonic content confined in an optically induced trap. Contrary to previous studies, we show that the excitonic reservoir created by the pump and responsible for creating the trap does not strongly affect the emission linewidth as long as the condensate is trapped and the pump power is well above the condensation (lasing) threshold. The long coherence time of the exciton-polariton system uncovered here opens up opportunities for manipulating its macroscopic quantum state, which is essential for applications in classical and quantum computing.

Electric-magnetic duality and Z2 symmetry enriched Abelian lattice gauge theory

Kitaev’s quantum double model is a lattice realization of Dijkgraaf–Witten topological quantum field theory. Its topologically protected ground state space has broad applications for topological quantum computation and topological quantum memory. We investigate the Z2 symmetry enriched generalization of the model for the Abelian group in a categorical framework and present an explicit Hamiltonian construction. This model provides a lattice realization of the Z2 symmetry of the topological phase. We discuss in detail the categorical symmetry of the phase, for which the electric-magnetic (EM) duality symmetry is a special case. The aspects of symmetry defects are investigated using the G-crossed unitary braided fusion category. By determining the corresponding anyon condensation, the gapped boundaries and boundary-bulk duality are also investigated. In the last part, an explicit lattice realization of EM duality is discussed.

Graphite/h-BN van der Waals heterostructure as a gate stack for HgTe quantum wells

Two-dimensional topological insulators have attracted much interest due to their potential applications in spintronics and quantum computing. To access the exotic physical phenomena, a gate electric field is required to tune the Fermi level into the bulk band gap. Hexagonal boron nitride (h-BN) is a promising alternative gate dielectric due to its unique advantages such as flat and charge-free surface. Here we present a h-BN/graphite van der Waals heterostructure as a top gate on HgTe heterostructure-based Hall bar devices. We compare our results to devices with h-BN/Ti/Au and HfO2/Ti/Au gates. Devices with a h-BN/graphite gate show no charge carrier density shift compared to as-grown structures, in contrast to a significant n-type carrier density increase for HfO2/Ti/Au. We attribute this observation mainly to the comparable work function of HgTe and graphite. In addition, devices with h-BN gate dielectric show slightly higher electron mobility compared to HfO2-based devices. Our results demonstrate the compatibility between layered materials transfer and wet-etched structures and provide a strategy to solve the issue of significant shifts of the carrier density in gated HgTe heterostructures.

Selecting electronic transitions between graphyne and lithium ions for entanglement generation using new organic material

This study explores the entanglement potential in a graphyne (GM) system embedded with lithium ions, employing time-dependent density functional theory (TD-DFT) calculations. A finite, non-periodic GM structure was optimized using the B3LYP functional and the 6-311G(d,p) basis set. Lithium ions were strategically positioned at various distances from the GM to investigate their interactions, focusing on charge transfer transitions to the unoccupied 2s orbitals of the lithium ions. Successful electronic transitions were identified and validated, emphasizing the role of contemporary atomic manipulation technologies such as optical tweezers. This research enhances the understanding of entanglement mechanisms in quantum systems and proposes potential applications in secure quantum communication and advanced quantum computing. The practical feasibility of precise atomic manipulation, enabled by cutting-edge technologies, underscores the applicability of the proposed configurations.

Quantum Computing Applications in Cryptography: Enhancing Security and Identifying Vulnerabilities

Quantum Computing Applications in Cryptography: Enhancing Security and Identifying Vulnerabilities

Effect of measurements on quantum speed limit

Given the initial and final states of a quantum system, the speed of transportation of state vector in the projective Hilbert space governs the quantum speed limit. Here, we ask the question: what happens to the quantum speed limit under continuous measurement process? We model the continuous measurement process by a non-Hermitian Hamiltonian which keeps the evolution of the system Schrödinger-like even under the process of measurement. Using this specific measurement model, we prove that under continuous measurement, the speed of transportation of a quantum system tends to zero. Interestingly, we also find that for small time scale, there is an enhancement of quantum speed even if the measurement strength is finite. Our findings can have applications in quantum computing and quantum control where dynamics is governed by both unitary and measurement processes.

Enhancement of quantum entanglement between twin beams via a four-wave mixing process with double feedback

Two-port feedback has been theoretically studied and proved to be effective in enhancing the degree of entanglement of the output beams for a four-wave mixing process. Here, to further consider the loss effects and phase delays, we use a model that is closer to practical implementation. By reasonably tuning the feedback coefficients, we find that a higher degree of entanglement and higher power of the output beams can be achieved, and a loose requirement for phase-locking accuracy can be obtained. This scheme may have promising applications in practical quantum computation and quantum communication.

Fewer Measurements from Shadow Tomography with N-Representability Conditions.

Classical shadow tomography provides a randomized scheme for approximating the quantum state and its properties at reduced computational cost with applications in quantum computing. In this Letter we present an algorithm for realizing fewer measurements in the shadow tomography of many-body systems. Accelerated tomography of the two-body reduced density matrix (2-RDM) is achieved by combining classical shadows with necessary constraints for the 2-RDM to represent an N-body system, known as N-representability conditions. We compute the ground-state energies and 2-RDMs of hydrogen chains and the N_{2} dissociation curve. The results demonstrate a significant reduction in the number of measurements with important applications to quantum many-body simulations on near-term quantum devices.

Quantum block coherence with respect to projective measurements

Abstract Quantum coherence serves as a defining characteristic of quantum mechanics, finding extensive applications in quantum computing and quantum communication processing. This study explores quantum block coherence in the context of projective measurements, focusing on the quantification of such coherence. Firstly, we define the correlation function between the two general projective measurements P and Q, and analyze the connection between sets of block incoherent states related to two compatible projective measurements P and Q. Secondly, we discuss the measure of quantum block coherence with respect to projective measurements. Based on a given measure of quantum block coherence, we characterize the existence of maximal block coherent states through projective measurements. This research integrates the compatibility of projective measurements with the framework of quantum block coherence, contributing to the advancement of block coherence measurement theory.

Research on the Application of Quantum Computing in Artificial Intelligence

This paper aims to explore the application of quantum computing in the field of artificial intelligence (AI). The continuous development of AI technology has brought revolutionary changes to various industries, but traditional computers may encounter bottlenecks when facing complex problems. Quantum computing, as an emerging computing paradigm, has the potential to address complex issues. This paper first introduces the fundamentals of AI and quantum computing, then discusses the application cases of quantum computing in AI, including quantum neural networks, quantum optimization algorithms, and quantum reinforcement learning. It also analyzes the current technical challenges and solutions, and prospects the future development direction of quantum computing in the field of AI. Through research in this emerging field, we can better understand and utilize quantum computing technology to promote progress and innovation in the field of AI.

Optimizing Variational Quantum Neural Networks Based on Collective Intelligence

Quantum machine learning stands out as one of the most promising applications of quantum computing, widely believed to possess potential quantum advantages. In the era of noisy intermediate-scale quantum, the scale and quality of quantum computers are limited, and quantum algorithms based on fault-tolerant quantum computing paradigms cannot be experimentally verified in the short term. The variational quantum algorithm design paradigm can better adapt to the practical characteristics of noisy quantum hardware and is currently one of the most promising solutions. However, variational quantum algorithms, due to their highly entangled nature, encounter the phenomenon known as the “barren plateau” during the optimization and training processes, making effective optimization challenging. This paper addresses this challenging issue by researching a variational quantum neural network optimization method based on collective intelligence algorithms. The aim is to overcome optimization difficulties encountered by traditional methods such as gradient descent. We study two typical applications of using quantum neural networks: random 2D Hamiltonian ground state solving and quantum phase recognition. We find that the collective intelligence algorithm shows a better optimization compared to gradient descent. The solution accuracy of ground energy and phase classification is enhanced, and the optimization iterations are also reduced. We highlight that the collective intelligence algorithm has great potential in tackling the optimization of variational quantum algorithms.

Magic of quantum hypergraph states

Magic, or nonstabilizerness, characterizes the deviation of a quantum state from the set of stabilizer states, playing a fundamental role in quantum state complexity and universal fault-tolerant quantum computing. However, analytical and numerical characterizations of magic are very challenging, especially for multi-qubit systems, even with a moderate qubit number. Here, we systemically and analytically investigate the magic resource of archetypal multipartite quantum states—quantum hypergraph states, which can be generated by multi-qubit controlled-phase gates encoded by hypergraphs. We first derive the magic formula in terms of the stabilizer Rényi-α entropies for general quantum hypergraph states. If the average degree of the corresponding hypergraph is constant, we show that magic cannot reach the maximal value, i.e., the number of qubits n. Then, we investigate the statistical behaviors of random hypergraph states' magic and prove a concentration result, indicating that random hypergraph states typically reach the maximum magic. This also suggests an efficient way to generate maximal magic states with random diagonal circuits. Finally, we study hypergraph states with permutation symmetry, such as 3-complete hypergraph states, where any three vertices are connected by a hyperedge. Counterintuitively, such states can only possess constant or even exponentially small magic for α≥2. Our study advances the understanding of multipartite quantum magic and could lead to applications in quantum computing and quantum many-body physics.

High spin axion insulator

Axion insulators possess a quantized axion field θ = π protected by combined lattice and time-reversal symmetry, holding great potential for device applications in layertronics and quantum computing. Here, we propose a high-spin axion insulator (HSAI) defined in large spin-s representation, which maintains the same inherent symmetry but possesses a notable axion field θ = (s + 1/2)2π. Such distinct axion field is confirmed independently by the direct calculation of the axion term using hybrid Wannier functions, layer-resolved Chern numbers, as well as the topological magneto-electric effect. We show that the guaranteed gapless quasi-particle excitation is absent at the boundary of the HSAI despite its integer surface Chern number, hinting an unusual quantum anomaly violating the conventional bulk-boundary correspondence. Furthermore, we ascertain that the axion field θ can be precisely tuned through an external magnetic field, enabling the manipulation of bonded transport properties. The HSAI proposed here can be experimentally verified in ultra-cold atoms by the quantized non-reciprocal conductance or topological magnetoelectric response. Our work enriches the understanding of axion insulators in condensed matter physics, paving the way for future device applications.

Excited-state downfolding using ground-state formalisms

Downfolding coupled cluster (CC) techniques are powerful tools for reducing the dimensionality of many-body quantum problems. This work investigates how ground-state downfolding formalisms can target excited states using non-Aufbau reference determinants, paving the way for applications of quantum computing in excited-state chemistry. This study focuses on doubly excited states for which canonical equation-of-motion CC approaches struggle to describe unless one includes higher-than-double excitations. The downfolding technique results in state-specific effective Hamiltonians that, when diagonalized in their respective active spaces, provide ground- and excited-state total energies (and therefore excitation energies) comparable to high-level CC methods. The performance of this procedure is examined with doubly excited states of H2, Methylene, Formaldehyde, and Nitroxyl.

Minimizing readout-induced noise for early fault-tolerant quantum computers

Quantum error correcting code can diagnose potential errors and correct them based on measured outcomes by leveraging syndrome measurement. However, mid-circuit measurement has been technically challenging for early fault-tolerant quantum computers and the readout-induced noise acts as a main contributor to the logical infidelity. We present a different method for syndrome extraction, namely , that requires only a single-shot measurement on a single ancilla, while the canonical syndrome measurement requires multiple measurements to extract the eigenvalue for each stabilizer generator. As such, we can detect the error in the logical state with minimized readout-induced noise. By adopting our method as a precheck routine for quantum error correcting cycles, we can significantly reduce the readout overhead, the idling time, and the logical error rate during syndrome measurement. We numerically analyze the performance of our protocol using Iceberg code and Steane code under realistic noise parameters based on superconducting hardware and demonstrate the advantage of our protocol in the near-term scenario. As mid-circuit measurements are still error-prone for near-term quantum hardware, our method could boost the applications of early fault-tolerant quantum computing. Published by the American Physical Society 2024