Applications In Quantum Computing Research Articles

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.

Quantum simulation of excited states from parallel contracted quantum eigensolvers

Computing excited-state properties of molecules and solids is considered one of the most important near-term applications of quantum computers. While many of the current excited-state quantum algorithms differ in circuit architecture, specific exploitation of quantum advantage, or result quality, one common feature is their rooting in the Schrödinger equation. However, through contracting (or projecting) the eigenvalue equation, more efficient strategies can be designed for near-term quantum devices. Here we demonstrate that when combined with the Rayleigh–Ritz variational principle for mixed quantum states, the ground-state contracted quantum eigensolver (CQE) can be generalized to compute any number of quantum eigenstates simultaneously. We introduce two excited-state (anti-Hermitian) CQEs that perform the excited-state calculation while inheriting many of the remarkable features of the original ground-state version of the algorithm, such as its scalability. To showcase our approach, we study several model and chemical Hamiltonians and investigate the performance of different implementations.

Study of dielectric polarization and electrical transport in Bi1·2Sb0·8Te0·4Se2.6 nanofilms

Studying the dielectric response of topological insulators (TIs) can unveil their unique physical mechanisms such as charge transport and spin-orbit coupling effects. However, due to the manifestation of material's topological nature and band structure primarily in nanofilm, such thickness poses challenges for dielectric testing. To date, research on TI dielectric aspects remains relatively unexplored. Therefore, this paper successfully synthesizes nanofilm of quaternary topological insulator Bi1·2Sb0·8Te0·4Se2.6 (BSTS) using laser molecular beam epitaxy (LMBE) technique. Utilizing a wide-frequency dielectric spectrometer and a comprehensive physical properties measurement system (PPMS), we measured and thoroughly analyzed the dielectric polarization and charge transport characteristics of BSTS. We observed various polarization responses in the frequency range of 101–103 Hz, with the dipole orientation gradually failing to keep pace with the frequency increase in the range of 103–105 Hz, and the relaxation polarization unable to establish itself in the range of 105–107 Hz, with polarization primarily contributed by displacement polarization. Subsequently, we further analyzed the dependence of BSTS dielectric polarization response on temperature and film thickness, which will help reveal the influence of external factors on TI dielectric response, providing crucial insights for controlling TI materials' dielectric response. This not only deepens our understanding of the fundamental physical properties of this novel material but also offers important scientific basis and technological support for its applications in quantum computing, photonics, spintronics, and other fields.

Enhanced magnon blockade in a magnomechanical system

Magnon blockade is one of the effective methods for realizing single magnon sources which have great potential application in quantum information processing and quantum computing. To enhance single-magnon blockade effect, we introduce a two-magnon driving to the magnomechanical system, which is used to form the multipath destructive interference. Our result shows that both the conventional magnon blockade (CMB) and unconventional magnon blockade (UMB) can be achieved due to nonlinear term of the magnon-mechanical oscillator and magnetic parametric amplification term(MPA) induced by two-magnon driving. By setting certain parameters of MPA, we combine the effect of CMB and UMB. As a result, the single-magnon blockade effect is enhanced, and the disadvantage of rapid oscillations of the time-delay second-order correlation function g (2)(τ) with UMB is overcome, which makes high time resolution not necessary in the detection of second-order correlation function.

Sparse Random Hamiltonians Are Quantumly Easy

A candidate application for quantum computers is to simulate the low-temperature properties of quantum systems. For this task, there is a well-studied quantum algorithm that performs quantum phase estimation on an initial trial state that has a non-negligible overlap with a low-energy state. However, it is notoriously hard to give theoretical guarantees that such a trial state can be prepared efficiently. Moreover, the heuristic proposals that are currently available, such as with adiabatic state preparation, appear insufficient in practical cases. This paper shows that, for most random sparse Hamiltonians, the maximally mixed state is a sufficiently good trial state, and phase estimation efficiently prepares states with energy arbitrarily close to the ground energy. Furthermore, any low-energy state must have non-negligible quantum circuit complexity, suggesting that low-energy states are classically nontrivial and phase estimation is the optimal method for preparing such states (up to polynomial factors). These statements hold for two models of random Hamiltonians: (i) a sum of random signed Pauli strings and (ii) a random signed d-sparse Hamiltonian. The main technical argument is based on some new results in nonasymptotic random matrix theory. In particular, a refined concentration bound for the spectral density is required to obtain complexity guarantees for these random Hamiltonians. Published by the American Physical Society 2024

Quantum Error Mitigated Classical Shadows

Classical shadows enable us to learn many properties of a quantum state ρ with very few measurements. However, near-term and early fault-tolerant quantum computers will only be able to prepare noisy quantum states ρ and it is thus a considerable challenge to efficiently learn properties of an ideal, noise-free state ρid. We consider error mitigation techniques, such as probabilistic error cancelation (PEC), zero noise extrapolation (ZNE), and symmetry verification (SV), which have been developed for mitigating errors in single expected value measurements and generalize them for mitigating errors in classical shadows. We find that PEC is the most natural candidate and thus develop a thorough theoretical framework for PEC shadows with the following rigorous theoretical guarantees: PEC shadows are an unbiased estimator for the ideal quantum state ρid; the sample complexity for simultaneously predicting many linear properties of ρid is identical to that of the conventional shadows approach up to a multiplicative factor, which is the sample overhead due to error mitigation. Due to efficient postprocessing of shadows, this overhead does not depend directly on the number of qubits but rather grows exponentially with the number of noisy gates. The broad set of tools introduced in this work may be instrumental in exploiting near-term and early fault-tolerant quantum computers: we demonstrate in detailed numerical simulations a range of practical applications of quantum computers that will significantly benefit from our techniques. Published by the American Physical Society 2024

Enhancement of Entanglement via Incoherent Collisions.

In contrast to the general thought that the collisions are intrinsically dephasing in nature and detrimental to quantum entanglement at room or higher temperatures, here, we show that in the conventional ladder-type electromagnetically induced transparency (EIT) configuration, when the probe field intensity is not very weak as compared to the pump field, the entanglement between the bright pump and probe fields can be remarkably enhanced with the increase of the collisional decay rates in a moderate range in an inhomogeneously broadened atomic system. The strengthened entanglement results from the enhancement of constructive interference and suppression of destructive interference between one-photon and multiphoton transition pathways. Our results clearly indicate that the collisions offer a promising alternative to enhance entanglement at room or higher temperatures despite of the dephasing nature, which provides great convenience for experimental implementation, and opens new prospects and applications in realistic quantum computation and quantum information processing.

Microwave Photon-Number Amplification

We experimentally demonstrate a microwave photon-multiplication scheme which combines the advantages of a single-photon detector and a power meter by multiplying the incoming photon number by an integer factor. Our first experimental implementation achieves an n=3-fold multiplication with 0.69 efficiency in a 116 MHz bandwidth up to an input photon rate of 400 MHz. It loses phase information but does not require any dead time or time binning. We expect an optimized device cascading such multipliers to achieve number-resolving measurement of itinerant photons with low dark count, which would offer new possibilities in a wide range of quantum-sensing and quantum-computing applications. Published by the American Physical Society 2024

Theoretical foundations for creating a quantum photon computer

Currently, significant results have emerged in the field of creation and application of quantum computers. We are talking about the Chinese quantum photonic computers Jiuzhang, Jiuzhang-2 (Jiuzhang 2.0), Jiuzhang-3 (Jiuzhang 3.0), which appeared respectively in 2020, 2021 and 2023, as well as the Canadian quantum photonic computer Borealis, which appeared in 2022. All these computers belong to the class of so-called quantum simulators – variants of quantum computers that solve one problem or a narrow class of problems. In addition to narrow specialization, quantum simulators have another important feature. In quantum simulators, controllable quantum objects simulate and effectively predict the behavior of real quantum systems. For example, in the quantum photonic computers Jiuzhang, Jiuzhang-2, Jiuzhang-3 and Borealis, the photons supplied to the inputs of these computers imitate the behavior of a “system of non-interacting identical bosons.” The Jiuzhang, Jiuzhang-2, Jiuzhang-3, and Borealis quantum simulators solve the problem of sampling bosons from a given distribution. Together they represent a significant scientific, technical and technological breakthrough in the development and creation of quantum computers. However, beyond what has been achieved, a number of important issues that are of interest both theoretically and for the field of practical applications remain unexamined. Among them are the following: about the architecture and composition of the elements of a quantum photonic computer, their number and connection diagram, as well as setting parameters that determine the required probability distribution from which boson samples are taken. The scientific problem addressed in the article is to obtain answers to the above questions by developing the theoretical foundations for creating a quantum photonic computer that solves the problem of sampling bosons from a given distribution in the most general case without restrictions on the distribution. The following results were obtained: the theoretical basis for the creation of a quantum photon computer (called in the article the Fuzuli boson sampler, briefly bsF) was developed that solves the problem of sampling bosons from a given distribution, for use as a quantum computing subsystem of a hybrid computing system to solve the problem of estimating matrix permanents; its constituent elements have been identified and studied, their numbers have been determined, and inter-element connection diagrams have been developed; an algorithm has been developed for tuning the bsF quantum photon computer to solve the problem of estimating matrix permanents; The functioning process of bsF is described. The article assumes that physically, in each cycle of operation of the bsF computer, the process of supplying photons to the inputs of the computer, their “movement” to the detectors, as well as the registration of photons by detectors is organized in such a way that the set of these photons imitates the behavior of a system of non-interacting identical bosons. Physical and engineering issues of ensuring such behavior of photons are not considered in this work. At the same time, the feasibility of such a possibility in practice (experimentally) is assumed to be undeniable. Examples confirming this position are the already created prototypes of quantum photonic computers in China (Jiuzhang, Jiuzhang-2, Jiuzhang-3) and in Canada (Borealis).

Average-Case Speedup for Product Formulas

Quantum simulation is a promising application of future quantum computers. Product formulas, or Trotterization, are the oldest and still remain an appealing method to simulate quantum systems. For an accurate product formula approximation, the state-of-the-art gate complexity depends on the number of terms in the Hamiltonian and a local energy estimate. In this work, we give evidence that product formulas, in practice, may work much better than expected. We prove that the Trotter error exhibits a qualitatively better scaling for the vast majority of input states, while the existing estimate is for the worst states. For general k-local Hamiltonians and higher-order product formulas, we obtain gate count estimates for input states drawn from any orthogonal basis. The gate complexity significantly improves over the worst case for systems with large connectivity. Our typical-case results generalize to Hamiltonians with Fermionic terms, with input states drawn from a fixed-particle number subspace, and with Gaussian coefficients (e.g., the SYK models). Technically, we employ a family of simple but versatile inequalities from non-commutative martingales called uniform smoothness, which leads to Hypercontractivity, namely p-norm estimates for k-local operators. This delivers concentration bounds via Markov’s inequality. For optimality, we give analytic and numerical examples that simultaneously match our typical-case estimates and the existing worst-case estimates. Therefore, our improvement is due to asking a qualitatively different question, and our results open doors to the study of quantum algorithms in the average case.

Review of Applications of Quantum Computing in Power Flow Calculation

Review of Applications of Quantum Computing in Power Flow Calculation

Quantum and quantum-inspired optimization for an in-core fuel management problem

Operation management of nuclear power plants consists of several computationally hard problems. Searching for an in-core fuel loading pattern is among them. The main challenge of this combinatorial optimization problem is the exponential growth of the search space with a number of loading elements. Here we study a reloading problem in a Quadratic Unconstrained Binary Optimization (QUBO) form. Such a form allows us to apply various techniques, including quantum annealing, classical simulated annealing, and quantum-inspired algorithm in order to find fuel reloading patterns for several realistic configurations of nuclear reactors. We present the results of benchmarking the in-core fuel management problem in the QUBO form using the aforementioned computational techniques. This work demonstrates potential applications of quantum computers and quantum-inspired algorithms in the energy industry.

Chirality manipulation of ultrafast phase switches in a correlated CDW-Weyl semimetal

Light engineering of correlated states in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods. Here we demonstrate a light control of correlation gaps in a model charge-density-wave (CDW) and polaron insulator (TaSe4)2I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via the fluence dependence of a longitudinal circular photogalvanic current. This helicity-dependent photocurrent reveals continuous ultrafast phase switches from the polaronic state to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Additional distinctive attributes aligning with the light-induced switches include: the mode-selective coupling of coherent phonons to the polaron and CDW modulation, and the emergence of a non-thermal chiral photocurrent above the pump threshold of CDW-related phonons. The demonstrated ultrafast chirality control of correlated topological states here holds large potentials for realizing axion electrodynamics and advancing quantum-computing applications.

Process Parameter Optimisation for Endohedral Metallofullerene Synthesis via the Arc-Discharge Method

Fullerenes have a unique structure, capable of both encapsulating other molecules and reacting with those on the exterior surface. Fullerene derivatives have also been found to have enormous potential to address the challenges of the renewable energy sector and current environmental issues, such as in the production of n-type materials in bulk heterojunction solar cells, as antimicrobial agents, in photocatalytic water treatment processes, and in sensor technologies. Endohedral metallofullerenes, in particular, can possess unpaired electron spins, driven by the enclosed metal atom or cluster, which yield valuable magnetic properties. These properties have significant potential for applications in molecular magnets, spin probes, quantum computing, and devices such as quantum information processing,, atomic clocks, and molecular magnets. However, the intrinsically low yield of endohedral fullerenes remains a huge obstacle, impeding not only their industrial utilization but also the synthesis and characterization essential for exploring novel applications. The low yield and difficulty in separation of different types of endohedral fullerenes results in the usage of a large amount of solvents and energy, which is detrimental to the environment. In this paper, we analyse the methodologies proposed by various researchers and identify the critical synthesis parameters that play a role in increasing the yields of fullerenes.

Towards near-term quantum simulation of materials

Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3 compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system’s size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.

Longitudinal Coupling Synthesis in Superconducting Qubits

Longitudinal coupling, which generates entanglement without energy exchange, has extensive applications in quantum computing and quantum simulation. However, achieving available direct and flexible longitudinal couplings between highly coherent superconducting qubits is challenging. In this study, a method is developed to achieve direct and flexible longitudinal couplings between superconducting qubits, including the direct longitudinal coupling between capacitively shunted flux qubits (C‐shunt flux qubits) and that between transmon qubits. Herein, first, a variant of the prototype C‐shunt flux qubit, the concentric C‐shunt flux qubit, is introduced. It is demonstrated that the large mutual inductance between concentric C‐shunt flux qubits produces a longitudinal coupling strength up to 21 MHz. It is also demonstrated that the method can be used to realize a strong longitudinal coupling between two transmon qubits. In the findings, it is demonstrated that it is possible to achieve a flexible and efficient direct longitudinal coupling between superconducting qubits, and open up new possibilities for the development of quantum gate operation methods as well as quantum simulation methods.

SimuQ: A Framework for Programming Quantum Hamiltonian Simulation with Analog Compilation

Quantum Hamiltonian simulation, which simulates the evolution of quantum systems and probes quantum phenomena, is one of the most promising applications of quantum computing. Recent experimental results suggest that Hamiltonian-oriented analog quantum simulation would be advantageous over circuit-oriented digital quantum simulation in the Noisy Intermediate-Scale Quantum (NISQ) machine era. However, programming analog quantum simulators is much more challenging due to the lack of a unified interface between hardware and software. In this paper, we design and implement SimuQ, the first framework for quantum Hamiltonian simulation that supports Hamiltonian programming and pulse-level compilation to heterogeneous analog quantum simulators. Specifically, in SimuQ, front-end users specify the target quantum system with Hamiltonian Modeling Language, and the Hamiltonian-level programmability of analog quantum simulators is specified through a new abstraction called the abstract analog instruction set (AAIS) and programmed in AAIS Specification Language by hardware providers. Through a solver-based compilation, SimuQ generates executable pulse schedules for real devices to simulate the evolution of desired quantum systems, which is demonstrated on superconducting (IBM), neutral-atom (QuEra), and trapped-ion (IonQ) quantum devices. Moreover, we demonstrate the advantages of exposing the Hamiltonian-level programmability of devices with native operations or interaction-based gates and establish a small benchmark of quantum simulation to evaluate SimuQ's compiler with the above analog quantum simulators.