A Novel Scheduling Framework for Multi-Programming Quantum Computing in Cloud Environment

As cloud quantum computing gains broader acceptance,a growing quantity of researchers are directing their focus towards this domain.Nevertheless,the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity,which in turn hampers users from achieving optimal satisfaction.Therefore,cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands.This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment.The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience.It introduces three innovative designs:1)Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks.2)Multi-programming mechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud.In comparison to conventional task schedulers,our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud.3)The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters.

Defining Quantum Advantage for Building a Sustainable MVP to Deliver Quantum Computing Services

Quantum Computing (QC) is hailed as the future of computers. After Google’s claim of achieving Quantum Supremacy in 2019, several groups challenged the claim. Some QC experts attribute catastrophic risks that unrestrained QC may cause in the future by collapsing the current cryptographic cybersecurity infrastructure. These predictions are relevant only if QC becomes commercially viable and sustainable in the future. No technology can be a one-way ticket to catastrophe, and neither can the definition of superiority of that technology be. If there are catastrophic risks, large-scale QC can never enter the public domain as a minimum viable product (MVP) unless there are safeguards in place. Those safeguards should obviously become an integral part of the definition of its superiority over the legacy systems. NIST (National Institute of Standards & Technology) is pursuing the standardization of Post Quantum Cryptography (PQC) as that safeguard. However, with all the 82 candidate PQCs failing and companies already offering QC as a service, there’s an urgent need for an alternate strategy to mitigate the impending Q-Day threat and render QC sustainable. Our research proposes a novel encryption-agnostic cybersecurity approach to safeguard QC. It articulates a comprehensive definition of an MVP that can potentially set a sustainable gold standard for defining commercially viable quantum advantage over classical computing.

Quantum Computing Based Neural Networks for Anomaly Classification in Real-Time Surveillance Videos

For intelligent surveillance videos,anomaly detection is extremely important.Deep learning algorithms have been popular for evaluating realtime surveillance recordings,like traffic accidents,and criminal or unlawful incidents such as suicide attempts.Nevertheless,Deep learning methods for classification,like convolutional neural networks,necessitate a lot of computing power.Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics.As a result,the focus of this research is on developing a hybrid quantum computing model which is based on deep learning.This research develops a Quantum Computing-based Convolutional Neural Network(QC-CNN)to extract features and classify anomalies from surveillance footage.A Quantum-based Circuit,such as the real amplitude circuit,is utilized to improve the performance of the model.As far as my research,this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications.There are 13 anomalies classified from the UCF-crime dataset.Based on experimental results,the proposed model is capable of efficiently classifying data concerning confusion matrix,Receiver Operating Characteristic(ROC),accuracy,Area Under Curve(AUC),precision,recall as well as F1-score.The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37%greater when compared to other existing models.To measure the efficiency of the proposed work,QC-CNN is also evaluated with classical and quantum models.

Accelerating Quantum Computing Readiness: Risk Management and Strategies for Sectors

The potential impact of quantum computing on various industries such as finance, healthcare, cryptography, and transportation is significant;therefore, sectors face challenges in understanding where to start because of the complex nature of this technology. Starting early to explore what is supposed to be done is crucial for providing sectors with the necessary knowledge, tools, and processes to keep pace with rapid advancements in quantum computing. This article emphasizes the importance of consultancy and governance solutions that aid sectors in preparing for the quantum computing revolution. The article begins by discussing the reasons why sectors need to be prepared for quantum computing and emphasizes the importance of proactive preparation. It illustrates this point by providing a real-world example of a partnership. Subsequently, the article mentioned the benefits of quantum computing readiness, including increased competitiveness, improved security, and structured data. In addition, this article discusses the steps that various sectors can take to achieve quantum readiness, considering the potential risks and opportunities in industries. The proposed solutions for achieving quantum computing readiness include establishing a quantum computing office, contracting with major quantum computing companies, and learning from quantum computing organizations. This article provides the detailed advantages and disadvantages of each of these steps and emphasizes the need to carefully evaluate their potential drawbacks to ensure that they align with the sector’s unique needs, goals, and available resources. Finally, this article proposes various solutions and recommendations for sectors to achieve quantum-computing readiness.

Leveraging Quantum Computing for the Ising Model to Simulate Two Real Systems: Magnetic Materials and Biological Neural Networks (BNNs)

Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.

Quantum Mechanical Nature in Liquid NMR Quantum Computing

The quantum nature of bulk ensemble NMR quantum computing — the center of recent heated debate, is addressed. Concepts of the mixed state and entanglement are examined, and the data in a two-qubit liquid NMR quantum computation are analyzed. The main points in this paper are: i) Density matrix describes the 'state' of an average particle in an ensemble. It does not describe the state of an individual particle in an ensemble; ii) Entanglement is a property of the wave function of a microscopic particle (such as a molecule in a liquid NMR sample), and separability of the density matrix cannot be used to measure the entanglement of mixed ensemble; iii) The state evolution in bulk-ensemble NMR quantum computation is quantum-mechanical; iv) The coefficient before the effective pure state density matrix, ?, is a measure of the simultaneity of the molecules in an ensemble. It reflects the intensity of the NMR signal and has no significance in quantifying the entanglement in the bulk ensemble NMR system. The decomposition of the density matrix into product states is only an indication that the ensemble can be prepared by an ensemble with the particles unentangled. We conclude that effective-pure-state NMR quantum computation is genuine, not just classical simulations.

Selected topics of quantum computing for nuclear physics

Nuclear physics,whose underling theory is described by quantum gauge field coupled with matter,is fundamentally important and yet is formidably challenge for simulation with classical computers.Quantum computing provides a perhaps transformative approach for studying and understanding nuclear physics.With rapid scaling-up of quantum processors as well as advances on quantum algorithms,the digital quantum simulation approach for simulating quantum gauge fields and nuclear physics has gained lots of attention.In this review,we aim to summarize recent efforts on solving nuclear physics with quantum computers.We first discuss a formulation of nuclear physics in the language of quantum computing.In particular,we review how quantum gauge fields(both Abelian and non-Abelian)and their coupling to matter field can be mapped and studied on a quantum computer.We then introduce related quantum algorithms for solving static properties and real-time evolution for quantum systems,and show their applications for a broad range of problems in nuclear physics,including simulation of lattice gauge field,solving nucleon and nuclear structures,quantum advantage for simulating scattering in quantum field theory,non-equilibrium dynamics,and so on.Finally,a short outlook on future work is given.

Near Term Hybrid Quantum Computing Solution to the Matrix Riccati Equations

The well-known Riccati differential equations play a key role in many fields,including problems in protein folding,control and stabilization,stochastic control,and cybersecurity(risk analysis and malware propaga-tion).Quantum computer algorithms have the potential to implement faster approximate solutions to the Riccati equations compared with strictly classical algorithms.While systems with many qubits are still under development,there is significant interest in developing algorithms for near-term quantum computers to determine their accuracy and limitations.In this paper,we propose a hybrid quantum-classical algorithm,the Matrix Riccati Solver(MRS).This approach uses a transformation of variables to turn a set of nonlinear differential equation into a set of approximate linear differential equations(i.e.,second order non-constant coefficients)which can in turn be solved using a version of the Harrow-Hassidim-Lloyd(HHL)quantum algorithm for the case of Hermitian matrices.We implement this approach using the Qiskit language and compute near-term results using a 4 qubit IBM Q System quantum computer.Comparisons with classical results and areas for future research are discussed.

Dissipative Quantum Computing with Majorana Fermions

We describe a scheme for universal quantum computation with Majorana fermions. We investigate two possible dissipative couplings of Majorana fermions to external systems, including metallic leads and local phonons. While the dissipation when coupling to metallic leads to uninteresting states for the Majorana fermions, we show that coupling the Majorana fermions to local phonons allows to generate arbitrary dissipations and therefore universal quantum operations on a single QuBit that can be enhanced by additional two-QuBit operations.

The Decoherence-Free in Multi-Coupled Quantum Computing System

A subdynamics theory framework for describing multi coupled quantum computing systems is presented first. A general kinetic equation for the reduced system is given then, enabling a sufficient condition to be formulated for constructing a pure coherent quantum computing system. This reveals that using multi coupled systems to perform quantum computing in Rigged Liouville Space opens the door to controlling or eliminating the intrinsic de coherence of quantum computing systems.

Efficient Scheme for One-Way Quantum Computing with Radiofrequency SQUID Qubits by Adiabatic Passage

We proposed an efficient scheme for constructing a quantum controlled phase-shift gate and generating thecluster states with rf superconducting quantum interference devices (SQUIDs)coupled to a microwave cavity throughadiabatic evolution of dark eigenstates.During the operation,the spontaneous emission is suppressed since the rf SQUIDsare always in the three lowest flux states.Considering the influence from the cavity decay with achievable experimentalparameters,we numerically analyze the success probability and the fidelity for generating the two-SQUID maximallyentangled state and the controlled phase-shift gate by adiabatic passage.

Cavity-assisted quantum computing in a silicon nanostructure

We present a scheme of quantum computing with charge qubits corresponding to one excess electron shared between dangling-bond pairs of surface silicon atoms that couple to a microwave stripline resonator on a chip. By choosing a certain evolution time, we propose the realization of a set of universal single-and two-qubit logical gates. Due to its intrinsic stability and scalability, the silicon dangling-bond charge qubit can be regarded as one of the most promising candidates for quantum computation. Compared to the previous schemes on quantum computing with silicon bulk systems, our scheme shows such advantages as a long coherent time and direct control and readout.

Intelligent Robust Control of Redundant Smart Robotic Arm Pt II: Quantum Computing KB Optimizer Supremacy

This article is a continuation of the work“Intelligent robust control of redundant smart robotic arm Pt I:Soft computing KB optimizer-deep machine learning IT”.In the first part of the paper,we examined control systems with constant coefficients of the conventional PID controller(based on genetic algorithm)and intelligent control systems based on soft computing technologies.For demonstration,MatLab/Simulink models and a test benchmark of the robot manipulator demonstrated.Advantages and limitations of intelligent control systems based on soft computing technology discussed.Intelligent main element of the control system based on soft computing is a fuzzy controller with a knowledge base in it.In the first part of the article,two ways to implement fuzzy controllers showed.First way applyied one controller for all links of the manipulator and showed the best performance.However,such an implementation is not possible in complex control objects,such as a manipulator with seven degrees of freedom(7DOF).The second way use of separated control when an independent fuzzy controller controls each link.The control decomposition due to a slight decrease in the quality of management has greatly simplified the processes of creating and placing knowledge bases.In this Pt II,to eliminate the mismatch of the work of separate independent fuzzy controllers,methods for organizing coordination control based on quantum computing technologies to create robust intelligent control systems for robotic manipulators with 3DOF and 7DOF described.Quantum supremacy of developed end-to-end IT design of robust intelligent control systems demonstrated.

A Critical Overview on Quantum Computing

Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not.Quantum computers have a computational advantage for some problems,over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm.Other areas that are expected to be benefitted from quantum computing are Machine Learning and deep learning,molecular biology,genomics and cancer research,space exploration,atomic and nuclear research and macro-economic forecasting.This paper represents a brief overview of the state of art of quantum computing and quantum information science with discussions of various theoretical and experimental aspects adopted by the researchers.

The Self-Learning Gate for Quantum Computing

Self-learning is one of the most important scientific methods that helps develop sciences, as it derives from the desire and interests of the individual. However, self-learning loses importance if it does not follow the scientific methodology for building and organizing information. The case becomes harder if the science is new and few scientific sources are available. Quantum computing is one of the new sciences in computer science and needs the support of specialists to develop it. Quantum computing overlaps with many sciences such as physics, chemistry, and mathematics, so any student in one of the previous disciplines may lose the correct self-learning path to find themselves learning the details of another discipline that does not achieve their goals. This article motivates students and those interested in computer science to begin studying the science of quantum computing and choose the same specialization that suits their interests. The article also provides a roadmap for self-learning steps to protect the learner from losing the correct learning path. I have categorized the stages of learning quantum computing into four steps through which all the essential basics can be learned, provided the goals mentioned in each stage which should be achieved. The learning strategy proposed in this article corresponds with individuals’ self-learning rules. Through my personal experience, the proposed learning strategy has proven its effectiveness in building information in an enjoyable scientific way.

Solving traffic data occlusion problems in computer vision algorithms using DeepSORT and quantum computing

Inaccuracies of traffic sensors during traffic counting and vehicle classification have persisted as transportation agencies have been prompted to calibrate sensors periodically.Detection of multiple objects,heavy occlusions,and similar appearances in congested places are some causes of computer vision model inaccuracies.This paper used the YOLOv5 model for detection and the DeepSORT model for tracking objects.Due to the nature of the reported problem caused by many misses and mismatches,the power of quantum computing with the alternating direction method of multipliers(ADMM)optimizer was leveraged.A basic Kalman filter and the Hungarian algorithm features were used in combination with a quantum optimizer to present robust multiple object tracking(MOT)algorithms.This hybrid combination of the classical and quantum model has fastened learning the occludes during frame matching of tracks and detections by generating minimum quantum cost function value.Comparisons with the existing models indicated a significant increase in the primary MOT metric multiple object tracking accuracy(MOTA)by 16%more than the regular YOLOv5-DeepSORT model when using a quantum optimizer.Also,a 6%multiple object tracking precision(MOTP)increases and a 6%identification metrics(F_(1))score increase were observed using the quantum optimizer with identity switching reduced from 6 to 4.This model is expected to assist transportation officials in improving the accuracy of traffic counts and vehicle classification and reduce the need for regular computer vision software calibration.

Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

Quantum computing is a game-changing technology for global academia,research centers and industries including computational science,mathematics,finance,pharmaceutical,materials science,chemistry and cryptography.Although it has seen a major boost in the last decade,we are still a long way from reaching the maturity of a full-fledged quantum computer.That said,we will be in the noisy-intermediate scale quantum(NISQ)era for a long time,working on dozens or even thousands of qubits quantum computing systems.An outstanding challenge,then,is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise.To address this challenge,several near-term quantum computing techniques,including variational quantum algorithms,error mitigation,quantum circuit compilation and benchmarking protocols,have been proposed to characterize and mitigate errors,and to implement algorithms with a certain resistance to noise,so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications.Besides,the development of near-term quantum devices is inseparable from the efficient classical sim-ulation,which plays a vital role in quantum algorithm design and verification,error-tolerant verification and other applications.This review will provide a thorough introduction of these near-term quantum computing techniques,report on their progress,and finally discuss the future prospect of these techniques,which we hope will motivate researchers to undertake additional studies in this field.

Cryo-CMOS modeling and a 600 MHz cryogenic clock generator for quantum computing applications

The development of large-scale quantum computing has boosted anurgent desire for the advancement of cryogenic CMOS(cryo-CMOS),which is a promising scalable solution for the control and read-out interface of quantum bits.In the current work,180 nm CMOS transistors were characterized and modeled down to 4 K,and the impact oflow-temperature transistor performance variations on circuit designwas also analyzed.Based on the proposed cryogenic model,a 180 nmCMOS-based 450 to 850 MHz clock generator operating at 4 K forquantum computing applications was presented.At the output frequency of 600 MHz,it achieved<4.8 ps RMS jitter with 30 mWpower consumption(with test buffer),corresponding to a−211.6 dBjitter-power FOM,which is suitable for providing a stable clock signalfor the control and readout electronics of scalable quantum computers.

Design of a novel hybrid quantum deep neural network in INEQR images classification

We redesign the parameterized quantum circuit in the quantum deep neural network, construct a three-layer structure as the hidden layer, and then use classical optimization algorithms to train the parameterized quantum circuit, thereby propose a novel hybrid quantum deep neural network(HQDNN) used for image classification. After bilinear interpolation reduces the original image to a suitable size, an improved novel enhanced quantum representation(INEQR) is used to encode it into quantum states as the input of the HQDNN. Multi-layer parameterized quantum circuits are used as the main structure to implement feature extraction and classification. The output results of parameterized quantum circuits are converted into classical data through quantum measurements and then optimized on a classical computer. To verify the performance of the HQDNN, we conduct binary classification and three classification experiments on the MNIST(Modified National Institute of Standards and Technology) data set. In the first binary classification, the accuracy of 0 and 4 exceeds98%. Then we compare the performance of three classification with other algorithms, the results on two datasets show that the classification accuracy is higher than that of quantum deep neural network and general quantum convolutional neural network.

Development of 400-μW cryogen-free dilution refrigerators for quantum experiments

We have successfully developed cryogen-free dilution refrigerators with medium cooling power that can be applied to quantum experiments. Breakthroughs have been made in some key technologies and components of heat switches and dilution units. Our prototype has been running continuously and stably for more than 100 hours below 10 m K, with a minimum temperature of 7.6 m K and a cooling power of 450 μW at 100 m K. At the same time, we have also made progress in the application of dilution refrigerators, such as quantum computing, low-temperature detector, and magnet integration. These indicators and test results indicate good prospects for application in physics, astronomy, and quantum information.