Continuous black-box optimization with an Ising machine and random subspace coding

Izawa, Syun; Kitai, Koki; Tanaka, Shu; Tsuda, Koji

doi:10.1103/physrevresearch.4.023062

Cited by 18 publications

(4 citation statements)

References 26 publications

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“…Ising machines specialize in searching for better solutions to combinatorial optimization problems formulated by an Ising model or a QUBO model. Studies have applied Ising machines to various combinatorial optimization problems, including machine learning [13]- [16], material design [17]- [19], portfolio optimization [20], [21], protein folding [22], traffic optimization [23]- [27], quantum compiler [28], and black-box optimization [17], [29], [30].…”

Section: B Ising Machinementioning

confidence: 99%

Dynamical Process of a Bit-Width Reduced Ising Model With Simulated Annealing

Kikuchi,

Togawa,

Tanaka

2023

IEEE Access

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Ising machines have attracted attention as fast and high-precision solvers for combinatorial optimization problems, which are often formulated as ground-state (lowest-energy) search problems of the Ising model. Since the bit-width of coefficients on Ising machines is limited, the Ising model must be transformed into a bit-width reduced (BWR) Ising model which reduces the coefficients. In a bit-width reduction method, which adds auxiliary spins, the ground state of the BWR Ising model is theoretically the same as the Ising model before bit-width reduction (original Ising model). However, the dynamical process of the BWR Ising model towards the ground state remains to be elucidated. Therefore, using simulated annealing (SA), we compared the dynamical processes of these models. The result shows that the dynamical process differs significantly by model. Analysis from the viewpoint of statistical mechanics found that the BWR Ising model has two characteristic properties: an effective temperature and a slow relaxation. It is suggested that these properties alter the effective temperature and spin flip probability in the BWR Ising model, contributing to the dynamical differences. Therefore, to obtain the same dynamical process as the original Ising model, we proposed SA parameters for the BWR Ising model. We demonstrated the proposed SA parameter using a square lattice Ising model, in which the interaction and magnetic field were set uniformly to the same positive values or randomly. As experimental evaluations, the dynamical process of the BWR Ising model became closer to that of the original Ising model.

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Section: B Ising Machinementioning

confidence: 99%

Dynamical Process of a Bit-Width Reduced Ising Model With Simulated Annealing

Kikuchi,

Togawa,

Tanaka

2023

IEEE Access

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“…For the standard discretization, no such term is necessary and for the random subspace coding, we add Eq. 122. e. References This problem can be found in Izawa et al (2022).…”

Section: Constraintsmentioning

confidence: 99%

Encoding-independent optimization problem formulation for quantum computing

Dominguez,

Unger,

Traube

et al. 2023

Front. Quantum Sci. Technol.

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We review encoding and hardware-independent formulations of optimization problems for quantum computing. Using this generalized approach, an extensive library of optimization problems from the literature and their various derived spin encodings are discussed. Common building blocks that serve as a construction kit for formulating these spin Hamiltonians are provided. This previously introduced approach paves the way toward a fully automatic construction of Hamiltonians for arbitrary discrete optimization problems and this freedom in the problem formulation is a key step for tailoring optimal spin Hamiltonians for different hardware platforms.

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“…5). We note the use of the method of receptive fields for continuous optimization [55,56] and the method based on random projection in problems of decentralized flows [57].…”

Section: Input Data Transformationsmentioning

confidence: 99%

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

Rachkovskij¹,

Gritsenko²,

Volkov³

et al. 2022

Kibern. vyčisl. teh.

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coming these shortcomings is a necessary condition for advancing from specialized Artificial Intelligence to general one, which requires the development of alternative approaches.The purpose of the paper is to present an overview of research in this direction, which has been carried out at the International Center for 25 years. The approach being developed stems from the ideas of N. M. Amosov and his scientific school. Connections to the Hyperdimensional Computing (HDC) and Vector Symbolic Architectures (VSA) field as well as to current brain research are also provided.Results. The concept of distributed data representation is outlined, including HDC/VSA that are capable of representing various data structures. The developed paradigm of Associative-Projective Neural Networks is considered: codevector representation of data, superposition and binding operations, general architecture, transformation of data of various types into codevectors, methods for solving problems and applications. Conclusion. An adequate representation of data is one of the key issues within the Artificial Intelligence. The main area of research reviewed in this article is the problem of representing heterogeneous data in Artificial Intelligence systems in a unified format based on modeling the neural organization of the brain and the mechanisms of thinking. The approach under development is based on the hypothesis of distributed representation of information in the brain and allows representing various types of data, from numeric values to graphs, as vectors of large but fixed dimensionality.The most important advantages of the developed approach are the possibility of natural integration and efficient processing of various types of data and knowledge, a high degree of parallel computing, reliability and resistance to noise, the possibility of hardware implementation with high performance and energy efficiency, data processing based on associative similarity search -similar to how human memory works. This allows one to unify the methods, algorithms, and software and hardware for Artificial Intelligence systems, increase their scalability in terms of speed and memory with an increase in data volume and complexity.The research creates the basis for overcoming the shortcomings of current approaches to the specialized Artificial Intelligence based on Deep Neural Networks and paves the way for the creation of Artificial General Intelligence.

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Continuous black-box optimization with an Ising machine and random subspace coding

Cited by 18 publications

References 26 publications

Dynamical Process of a Bit-Width Reduced Ising Model With Simulated Annealing

Dynamical Process of a Bit-Width Reduced Ising Model With Simulated Annealing

Encoding-independent optimization problem formulation for quantum computing

Neural Distributed Representations for Artificial Intelligence and Modeling of Thinking

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