Applications In Quantum Computing Research Articles

Realization of fractional quantum Hall state with interacting photons.

Fractional quantum Hall (FQH) states are known for their robust topological order and possess properties that are appealing for applications in fault-tolerant quantum computing. An engineered quantum platform would provide opportunities to operate FQH states without an external magnetic field and enhance local and coherent manipulation of these exotic states. We demonstrate a lattice version of photon FQH states using a programmable on-chip platform based on photon blockade and engineering gauge fields on a two-dimensional circuit quantum electrodynamics system. We observe the effective photon Lorentz force and butterfly spectrum in the artificial gauge field, a prerequisite for FQH states. After adiabatic assembly of Laughlin FQH wave function of 1/2 filling factor from localized photons, we observe strong density correlation and chiral topological flow among the FQH photons. We then verify the unique features of FQH states in response to external fields, including the incompressibility of generating quasiparticles and the smoking-gun signature of fractional quantum Hall conductivity. Our work illustrates a route to the creation and manipulation of novel strongly correlated topological quantum matter composed of photons and opens up possibilities for fault-tolerant quantum information devices.

Quantum Equation of Motion with Orbital Optimization for Computing Molecular Properties in Near-Term Quantum Computing.

Determining the properties of molecules and materials is one of the premier applications of quantum computing. A major question in the field is how to use imperfect near-term quantum computers to solve problems of practical value. Inspired by the recently developed variants of the quantum counterpart of the equation-of-motion (qEOM) approach and the orbital-optimized variational quantum eigensolver (oo-VQE), we present a quantum algorithm (oo-VQE-qEOM) for the calculation of molecular properties by computing expectation values on a quantum computer. We perform noise-free quantum simulations of BeH2 in the series of STO-3G/6-31G/6-31G* basis sets and of H4 and H2O in 6-31G using an active space of four electrons and four spatial orbitals (8 qubits) to evaluate excitation energies, electronic absorption, and, for twisted H4, circular dichroism spectra. We demonstrate that the proposed algorithm can reproduce the results of conventional classical CASSCF calculations for these molecular systems.

Nanoscale spin ordering and spin screening effects in tunnel ferromagnetic Josephson junctions

Magnetic Josephson junctions (MJJs) have emerged as a prominent playground to explore the interplay between superconductivity and ferromagnetism. A series of fascinating experiments have revealed striking phenomena at the superconductor/ferromagnet (S/F) interface, pointing to tunable phase transitions and to the generation of unconventional spin-triplet correlations. Here, we show that the Josephson effect, being sensitive to phase space variation on the nanoscale, allows a direct observation of the spin polarization of the S/F interface. By measuring the temperature dependence of the Josephson magnetic field patterns of tunnel MJJs with strong and thin F-layer, we demonstrate an induced nanoscale spin order in S along the superconducting coherence length at S/F interface, i.e., the inverse proximity effect, with the first evidence of full spin screening at very low temperatures, as expected by the theory. A comprehensive phase diagram for spin nanoscale ordering regimes at S/F interfaces in MJJs has been derived in terms of the magnetic moment induced in the S-layer. Our findings contribute to drive the design and the tailoring of S/F interfaces also in view of potential applications in quantum computing.

Excited-State Downfolding Using Ground-State Formalisms

Abstract 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 H$_{2}$, Methylene, Formaldehyde, and Nitroxyl.

Topologically protected subradiant cavity polaritons through linewidth narrowing enabled by dissipationless edge states

Cavity polaritons derived from strong light–matter interaction provide a basis for efficient manipulation of quantum states via cavity field. Polaritons with narrow linewidth and long lifetime are appealing in applications, such as quantum sensing and storage. Here, we propose a prototypical arrangement to implement a whispering-gallery-mode resonator with one-dimensional topological atom mirror, which allows to boost the lifetime of cavity polaritons over an order of magnitude. This considerable enhancement attributes to the coupling of polaritonic states to dissipationless edge states protected by the topological bandgap of atom mirror that suppresses the leakage of cavity modes. When exceeding the width of Rabi splitting, topological bandgap can further reduce the dissipation from polaritonic states to bulk states, giving arise to subradiant cavity polaritons with extremely sharp linewidth. The resultant Rabi oscillation experiences decay rate lower than the free-space decay of a single quantum emitter. Inheriting from the topologically protected properties of edge states, the subradiance of cavity polaritons can be preserved in disordered atom mirror with moderate perturbations involving the atomic frequency, interaction strengths and location fluctuations. Our work opens up a new paradigm of topology-engineered quantum states with robust quantum coherence for future applications in quantum computing and network.

Confining single Er3+ ions in sub-3 nm NaYF4 nanoparticles to induce slow relaxation of the magnetisation

Molecular systems known as single-molecule magnets (SMMs) exhibit magnet-like behaviour of slow relaxation of the magnetisation and magnetic hysteresis and have potential application in high-density memory storage or quantum computing. Often, their intrinsic magnetic properties are plagued by low-energy molecular vibrations that lead to phonon-induced relaxation processes, however, there is no straightforward synthetic approach for molecular systems that would lead to a small amount of low-energy vibrations and low phonon density of states at the spin-resonance energies. In this work, we apply knowledge accumulated over the last decade in molecular magnetism to nanoparticles, incorporating Er3+ ions in an ultrasmall sub-3 nm diamagnetic NaYF4 nanoparticle (NP) and probing the slow relaxation dynamics intrinsic to the Er3+ ion. Furthermore, by increasing the doping concentration, we also investigate the role of intraparticle interactions within the NP. The knowledge gained from this study is anticipated to enable better design of magnetically high-performance molecular and bulk magnets for a wide variety of applications, such as molecular electronics.

Quantum optimization algorithms: Energetic implications

SummarySince the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. In this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least to more energy efficient than their classical counterparts; why feedback‐based quantum optimization is energy‐inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of 20. Finally, we use the NchooseK high‐level programming model to run optimization problems on both gate‐based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate‐based counterparts.

A Mathematical Framework for Quantum Hamiltonian Simulation and Duality

AbstractAnalogue Hamiltonian simulation is a promising near-term application of quantum computing and has recently been put on a theoretical footing alongside experiencing wide-ranging experimental success. These ideas are closely related to the notion of duality in physics, whereby two superficially different theories are mathematically equivalent in some precise sense. However, existing characterisations of Hamiltonian simulations are not sufficiently general to extend to all dualities in physics. We give a generalised duality definition encompassing dualities transforming a strongly interacting system into a weak one and vice versa. We characterise the dual map on operators and states and prove equivalence ofduality formulated in terms of observables, partition functions and entropies. A building block is a strengthening of earlier results on entropy preserving maps—extensions of Wigner’s celebrated theorem- –to maps that are entropy preserving up to an additive constant. We show such maps decompose as a direct sum of unitary and antiunitary components conjugated by a further unitary, a result that may be of independent mathematical interest.

Coupling Nanowire Quantum Dots to Optical Waveguides by Microsphere-Induced Photonic Nanojet

Silica on silicon is a major optical integration platform, while the emergent class of the integrated laser-written circuits’ platform offers additionally high customizability and flexibility for rapid prototyping. However, the inherent waveguides’ low core/cladding refractive index contrast characteristic, compared to other photonic platforms in silicon or silicon nitride, sets serious limitations for on-chip efficient coupling with single photon emitters, like semiconductor nanowires with quantum dots, limiting the applications in quantum computing. A new light coupling scheme proposed here overcomes this limitation, providing means for light coupling >50%. The scheme is based on the incorporation of an optical microsphere between the nanowire and the waveguide, which is properly optimized and arranged in terms of size, refractive index, and the distance of the microsphere between the nanowire and waveguide. Upon suitable design of the optical arrangement, the photonic nanojet emitted by the illuminated microsphere excites efficiently the guided eigenmodes of the input channel waveguide, thus launching light with high-coupling efficiency. The method is tolerant in displacements, misalignments, and imperfections and is fabricationally feasible by the current state of art techniques. The proposed method enables the on-chip multiple single photon emitters’ integration, thus allowing for the development of highly customizable and scalable quantum photonic-integrated circuits for quantum computing and communications.

Advancements and Applications of Quantum Computing in Robotics

Advancements and Applications of Quantum Computing in Robotics

Quadripartite quantum steering generated by single-pass cascaded nonlinear processes

Quantum steering has become an important resource for quantum information due to its unique asymmetry. In this paper, quadripartite quantum steering generated by single-pass nondegenerate optical parametric amplification cascaded with a sum-frequency process is investigated. Firstly, an idler beam is produced by a different-frequency process between pump and signal. Secondly, a sum-frequency beam is generated by a cascaded sum-frequency process between idler beam and pump in the same optical superlattice. Quadripartite quantum steering among pump, signal, idler and sum-frequency beams is demonstrated based on the criterion for multipartite quantum steering. The influences of nonlinear coupling coefficients κ, dimensionless propagation length ξ, and the initial values of pump and signal on the variation characteristic of the quadripartite steering are also investigated. The results indicate that slightly larger nonlinear coupling coefficients of cascaded nonlinear processes and weaker signals can achieve better quadripartite quantum steering. This simple quadripartite quantum steering preparation scheme does not require a resonant cavity and is easier to implement experimentally, with potential applications in quantum communication and computing.

Unbalanced penalization: a new approach to encode inequality constraints of combinatorial problems for quantum optimization algorithms

Solving combinatorial optimization problems of the kind that can be codified by quadratic unconstrained binary optimization (QUBO) is a promising application of quantum computation. Some problems of this class suitable for practical applications such as the traveling salesman problem (TSP), the bin packing problem (BPP), or the knapsack problem (KP) have inequality constraints that require a particular cost function encoding. The common approach is the use of slack variables to represent the inequality constraints in the cost function. However, the use of slack variables considerably increases the number of qubits and operations required to solve these problems using quantum devices. In this work, we present an alternative method that does not require extra slack variables and consists of using an unbalanced penalization function to represent the inequality constraints in the QUBO. This function is characterized by larger penalization when the inequality constraint is not achieved than when it is. We evaluate our approach on the TSP, BPP, and KP, successfully encoding the optimal solution of the original optimization problem near the ground state cost Hamiltonian. Additionally, we employ D-Wave Advantage and D-Wave hybrid solvers to solve the BPP, surpassing the performance of the slack variables approach by achieving solutions for up to 29 items, whereas the slack variables approach only handles up to 11 items. This new approach can be used to solve combinatorial problems with inequality constraints with a reduced number of resources compared to the slack variables approach using quantum annealing or variational quantum algorithms.

Effective versus Floquet theory for the Kerr parametric oscillator

Parametric gates and processes engineered from the perspective of the static effective Hamiltonian of a driven system are central to quantum technology. However, the perturbative expansions used to derive static effective models may not be able to efficiently capture all the relevant physics of the original system. In this work, we investigate the conditions for the validity of the usual low-order static effective Hamiltonian used to describe a Kerr oscillator under a squeezing drive. This system is of fundamental and technological interest. In particular, it has been used to stabilize Schrödinger cat states, which have applications for quantum computing. We compare the states and energies of the effective static Hamiltonian with the exact Floquet states and quasi-energies of the driven system and determine the parameter regime where the two descriptions agree. Our work brings to light the physics that is left out by ordinary static effective treatments and that can be explored by state-of-the-art experiments.

Role of quantum computing in shaping the future of 6 G technology

ContextThe emergence of 6 G technology heralds a groundbreaking era in digital connectivity, envisaging universal and seamless links. To address the intricate computational and security requirements of this revolution, the integration of quantum computing (QC) into these networks is perceived as a promising solution. ObjectiveThe objective this study presents a comprehensive investigation into the potential roles and implications of QC within the context of 6 G technology. MethodologyTo address the objectives of this study, firstly, we have conducted literature survey to identify the key applications of using QC in 6 G technology. Secondly, we performed interview study with industry experts to identify the best practices related to the key application of QC in 6 G technology. ResultsOur study unfolds in two distinct stages: firstly, we identify 15 key applications of QC in 6 G technology and segmented into 4 core areas. Secondly, the literature findings were empirically validated by conducting interview study and identified 49 best practices related to one of the identified key applications of QC in 6 G technology. ConclusionThe outcomes of this research lay a solid foundation for understanding both the pivotal applications of QC in 6 G technology and the effective practices for its implementation, thus providing valuable insights to both academics and industry practitioners.

Generating scalable graph states in an atom-nanophotonic interface

Scalable graph states are essential for measurement-based quantum computation and many entanglement-assisted applications in quantum technologies. Generation of these multipartite entangled states requires a controllable and efficient quantum device with delicate design of generation protocol. Here we propose to prepare high-fidelity and scalable graph states in one and two dimensions, which can be tailored in an atom-nanophotonic cavity via state carving technique. We propose a systematic protocol to carve out unwanted state components, which facilitates scalable graph states generations via adiabatic transport of a definite number of atoms in optical tweezers. An analysis of state fidelity is also presented, and the state preparation probability can be optimized via multiqubit state carvings and sequential single-photon probes. Our results showcase the capability of an atom-nanophotonic interface for creating graph states and pave the way toward novel problem-specific applications using scalable high-dimensional graph states with stationary qubits.

Riemannian quantum circuit optimization for Hamiltonian simulation

Hamiltonian simulation, i.e. simulating the real time evolution of a target quantum system, is a natural application of quantum computing. Trotter-Suzuki splitting methods can generate corresponding quantum circuits; however, a faithful approximation can lead to relatively deep circuits. Here we start from the insight that for translation invariant systems, the gates in such circuit topologies can be further optimized on classical computers to decrease the circuit depth and/or increase the accuracy. We employ tensor network techniques and devise a method based on the Riemannian trust-region algorithm on the unitary matrix manifold for this purpose. For the Ising and Heisenberg models on a one-dimensional lattice, we achieve orders of magnitude accuracy improvements compared to fourth-order splitting methods. The optimized circuits could also be of practical use for the time-evolving block decimation algorithm.

Nonadiabatic Holonomic Quantum Computation Based on Rydberg Ground State Blockade

Quantum gates are crucial for quantum computation and quantum information processing. However, their effectiveness is often hindered by systematic errors and decoherence. Therefore, achieving resilient quantum gates to these factors is of great significance. We present a method to construct nonadiabatic holonomic single- and two-qubit gates in a Rydberg ground-state-blockade regime. Our approach utilizes a far-off-resonant technique for the single-qubit gate and a modified Rydberg antiblockade for the two-qubit gate. The reduction of the population of single- and two-excitation Rydberg states and the nonadiabatic holonomic process during the construction of the gates ensure robustness to decoherence and systematic errors, respectively. Numerical results demonstrate the fidelity and robustness of our scheme. The proposed scheme holds promise for future applications in quantum computation and quantum information processing tasks.

Detection of malicious URLs using machine learning

AbstractThe detection of fraudulent URLs that lead to malicious websites using addresses similar to those of legitimate websites is a key form of defense against phishing attacks. Currently, in the case of Internet of Things devices is especially relevant, because they usually have access to the Internet, although in many cases they are vulnerable to these phishing attacks. This paper offers an overview of the most relevant techniques for the accurate detection of fraudulent URLs, from the most widely used machine learning and deep learning algorithms, to the application, as a proof of concept, of classification models based on quantum machine learning. Starting from an essential data preparation phase, special attention is paid to the initial comparison of several traditional machine learning models, evaluating them with different datasets and obtaining interesting results that achieve true positive rates greater than 90%. After that first approach, the study moves on to the application of quantum machine learning, analysing the specificities of this recent field and assessing the possibilities it offers for the detection of malicious URLs. Given the limited available literature specifically on the detection of malicious URLs and other cybersecurity issues through quantum machine learning, the research presented here represents a relevant novelty on the combination of both concepts in the form of quantum machine learning algorithms for cybersecurity. Indeed, after the analysis of several algorithms, encouraging results have been obtained that open the door to further research on the application of quantum computing in the field of cybersecurity.

Quantum α-fidelity of diagonalizable second-order Hamiltonians in multi-dimensional systems

How to characterize the similarity between two quantum states has received much attention recently. Motivated by the continued push in this direction, we propose a computable analytical formula for the quantum α-fidelity between diagonalizable second-order Hamiltonians, which is simply expressed in terms of the matrix representation of operators. Further, using this analytical formula, we calculate the α-fidelity of the one-dimensional and two-dimensional displaced-squeezed thermal states, the Liu-Tombesi oscillators, and twin-beam amplitude-squeezed states. Since α-fidelity can precisely characterize the approximation accuracy between two quantum states by adjusting the value of the parameter α, our results offer a richer fidelity family to quantify the similarity between diagonalizable second-order Hamiltonians in multi-dimensional systems, and hence may have potential applications in quantum information and quantum computing.

Distributed quantum neural networks via partitioned features encoding

Quantum neural networks are expected to be a promising application in near-term quantum computing, but face challenges such as vanishing gradients during optimization and limited expressibility by a limited number of qubits and shallow circuits. To mitigate these challenges, an approach using distributed quantum neural networks has been proposed to make a prediction by approximating outputs of a large circuit using multiple small circuits. However, the approximation of a large circuit requires an exponential number of small circuit evaluations. Here, we instead propose to distribute partitioned features over multiple small quantum neural networks and use the ensemble of their expectation values to generate predictions. To verify our distributed approach, we demonstrate ten class classification of the Semeion and MNIST handwritten digit datasets. The results of the Semeion dataset imply that while our distributed approach may outperform a single quantum neural network in classification performance, excessive partitioning reduces performance. Nevertheless, for the MNIST dataset, we succeeded in ten class classification with exceeding 96% accuracy. Our proposed method not only achieved highly accurate predictions for a large dataset but also reduced the hardware requirements for each quantum neural network compared to a large single quantum neural network. Our results highlight distributed quantum neural networks as a promising direction for practical quantum machine learning algorithms compatible with near-term quantum devices. We hope that our approach is useful for exploring quantum machine learning applications.