An efficient quantum proactive incremental learning algorithm

The demand for efficient solvers of complicated combinatorial optimization problems, especially those classified as NP-complete or NP-hard, has recently led to increased exploration of novel computing architectures. One prominent collective state computing paradigm embodied in the so-called Ising machines has recently attracted considerable research attention due to its ability to optimize complex problems with large numbers of interacting variables. Ising model-inspired solvers, thus named due to mathematical similarities to the well-known model from solid-state physics, represent a promising alternative to traditional von Neumann computer architectures due to their high degree of inherent parallelism. While there are many possible physical realizations of Ising solvers, just as there are many possible implementations of any binary computer, photonic Ising machines (PIMs) use primarily optical components for computation, taking advantage of features like lower power consumption, fast calculation speeds, the leveraging of physical optics to perform the calculations themselves, possessing decent scalability and noise tolerance. Photonic computing in the form of PIMs may offer certain computational advantages that are not easily achieved with non-photonic approaches and is nonetheless an altogether fascinating application of photonics to computing. In this review, we provide an overview of Ising machines generally, introducing why they are useful, what types of problems they can tackle, and how different Ising solvers can be compared and benchmarked. We delineate their various operational mechanisms, advantages, and limitations vis-à-vis non-photonic Ising machines. We describe their scalability, interconnectivity, performance, and physical dimensions. As research in PIMs continues to progress, there is a potential that photonic computing could well emerge as a way to handle large and challenging optimization problems across diverse domains. This review serves as a comprehensive resource for researchers and practitioners interested in understanding capabilities and potential of PIMs in addressing such complex optimization problems. 

Photonic Ising machines for combinatorial optimization problems

Gate-based and Annealing Quantum Computing backend systems purposely built and deployed based on Quantum Integrated Cloud Architecture (QICA) design patterns, provide enhanced quantum computational capabilities that consist of a suite of hybrid quantum-classical cloud-based software and specialized hybrid quantum-classical machine learning algorithms, tools, solvers, and simulators using physics-inspired models and High-Performance Computing circuits simulators. By harnessing the unique properties of quantum mechanics and building on decades of computer science and quantum physics research, our motivation in this paper consists of two folds: (1) to provide an overview of our PATENT-PENDING Quantum Integrated Cloud Architecture (QICA) implemented in support of aerospace programs, fielded and deployed to accelerate the pace of research and commercial enterprise adoption of low-bandwidth satellite networks using nanosatellites in a low orbit. (2) Demonstrate QICA-based computational advantages of Hybrid Quantum-Classical machine learning architecture using IBM Quantum, exploring the base generic quantum neural network (QNN) interfaces provided in Qiskit Machine Learning based on which many agnostic variations of both hybrid classical-quantum and quantum neural networks (QNN) are based on hybrid quantum machine learning.

Hybrid Quantum Machine learning using Quantum Integrated Cloud Architecture (QICA)

Quantum Computing for MIMO Beam Selection Problem: Model and Optical Experimental Solution

Computing today requires a lot of processing power, an immersive environment, and excellent precision. In this paper, we propose a hybrid multiprocessor architecture with quantum integration for data-intensive applications like a metaverse. Regular non-computationally intensive applications are handled by the central processing unit (CPU) and video rendering, graphically intensive programs by the graphics processing unit (GPU), and enormous, parallel, and computationally intensive applications by the quantum processing unit (QPU). To achieve higher permanence computing, we provide combined CPU, GPU, and QPU architecture in this work. This paper provides a roadmap for truly high-performance computing for massive real-time systems. In addition, we present an onsite and cloud-based architecture for scalability. Also, we present multimodality security protection systems for internal and external communication based on the split computing paradigm.

Quantum Integrated (C+G+Q)PU Split Architecture

Molecular docking is important in drug discovery but is burdensome for classical computers. Here, we introduce Grid Point Matching (GPM) and Feature Atom Matching (FAM) to accelerate pose sampling in molecular docking by encoding the problem into quadratic unconstrained binary optimization (QUBO) models so that it could be solved by quantum computers like the coherent Ising machine (CIM). As a result, GPM shows a sampling power close to that of Glide SP, a method performing an extensive search. Moreover, it is estimated to be 1000 times faster on the CIM than on classical computers. Our methods could boost virtual drug screening of small molecules and peptides in future.

Encoding Molecular Docking for Quantum Computers.

With the deeply studied of quantum computing by research fellows, the computational complexity and time complexity of quantum algorithms gradually increase. The quantum computing simulator provides an effective platform for the development and verification of quantum algorithms. However, the running time and memory overhead during calculation will increase exponentially with the number of qubits. Therefore, the difficulty of high-speed quantum computing on classical computers will gradually increase. At present, a single node has been unable to meet the resource requirements of the algorithm in the quantum computing process, and it is necessary to expand the memory resources in the multi-node computing cluster. Message passing interface has high performance inter-process communication capabilities, which can balance the data transmission between different hosts. Quantum computing tasks can be distributed on several servers to work together to solve the same problem. Therefore, this paper designs an advanced scheme of deploying quantum computing programs on cloud computing platform to support message exchanging between servers using message passing interface to accelerate quantum computing.

A quantum computing simulator scheme using MPI technology on cloud platform

This paper synthesizes the findings of application carried out in recent years and looks at the influence of automatic robots on financial scenario. Robot autonomous sorting can greatly improve the efficiency of perform tasks that require manual labor and free financial staffs from simple and single repetition work. It is estimated that robots are 15 times more efficient than human and the comprehensive cost is only 8% based on three-year workload. Starts from this background, a systematic description was carried out to analyze the use of dynamic cooperative robots in financial bill recognition and classification, while the opportunities, potential benefits and challenges to implementing dynamic cooperative robots in financial work was also discussed. The dynamic cooperative robot is consisted of mechanical arm, bill recognition system, cloud processing server and support multiple areas financial work. This work is the first to identify and discuss how financial work processed with all automatic systems and serves as a catalyst to spur future research in financial bill processing area.

Design of Dynamic Cooperative Robot System for Financial Bill Recognition

Abstract Solving linear differential equations is a common problem in almost all fields of science and engineering. Here, we present a variational algorithm with shallow circuits for solving such a problem: given an $$N \times N$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>N</mml:mi> <mml:mo>×</mml:mo> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> matrix $${\varvec{A}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>A</mml:mi> </mml:mrow> </mml:math> , an N -dimensional vector $$\varvec{b}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> </mml:math> , and an initial vector $$\varvec{x}(0)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mn>0</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> , how to obtain the solution vector $$\varvec{x}(T)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mi>T</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> at time T according to the constraint $$\textrm{d}\varvec{x}(t)/\textrm{d} t = {\varvec{A}}\varvec{x}(t) + \varvec{b}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mtext>d</mml:mtext> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> <mml:mo>/</mml:mo> <mml:mtext>d</mml:mtext> <mml:mi>t</mml:mi> <mml:mo>=</mml:mo> <mml:mrow> <mml:mi>A</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>x</mml:mi> </mml:mrow> <mml:mo>(</mml:mo> <mml:mi>t</mml:mi> <mml:mo>)</mml:mo> <mml:mo>+</mml:mo> <mml:mrow> <mml:mi>b</mml:mi> </mml:mrow> </mml:mrow> </mml:math> . The core idea of the algorithm is to encode the equations into a ground state problem of the Hamiltonian, which is solved via hybrid quantum-classical methods with high fidelities. Compared with the previous works, our algorithm requires the least qubit resources and can restore the entire evolutionary process. In particular, we show its application in simulating the evolution of harmonic oscillators and dynamics of non-Hermitian systems with $$\mathcal{P}\mathcal{T}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>P</mml:mi> <mml:mi>T</mml:mi> </mml:mrow> </mml:math> -symmetry. Our algorithm framework provides a key technique for solving so many important problems whose essence is the solution of linear differential equations. 

A quantum algorithm for linear differential equations with layerwise parameterized quantum circuits

Named entity recognition (NER) in natural language processing (NLP) considers the problem of identifying a sequence of words in a sentence text that mentions a predefined type of object (entity), e.g., person, organization, location, or time. NER methods are keys in extracting knowledge from texts as entities are fundamental for attaching entity properties or entity relations. However, NER for texts in Chinese is trickier due to that some auxiliary words maybe dropped in a sentence, which is a common phenomenon in Chinese writing for brevity. A usually dropped Chinese word is ‘的’ (often functions as the word ‘of’ in English). One obvious effect of this kind of omitting is bring difficulty in identifying the sub-entities (or nested named entities) contained in a named entity. Previous works considers the effected of recovering dropped pronouns in the Chinese translation task. Here we proposed a rule-based method to rover the auxiliary word ‘的’ for Chinese text, and study the effect of this recovery on the performance of a state-of-the-art Chinese NER method FLAT. Experimental results on Weibo-NER and MSRA-NER datasets shows that our method improves on FLAT. This study thus highlights the promising of recovering more types of dropped words for Chinese NER problem.

On the Utility of Recovering Dropped Words in Chinese Named Entity Recognition

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