LASOLV TM is a computing machine developed by NTT based on photonics technologies.While we can efficiently solve combinatorial optimization problems by using LASOLV, there are technical challenges in its application to real-world problems.For example, we need to mathematically convert problems to a specific format for solving them using LASOLV.Moreover, LASOLV requires cooperation with conventional digital computers to solve complex problems.In this article, we introduce LASOLV Computing System, which is a computing platform that helps users overcome these challenges.
LASOLV TM is a computing machine developed by NTT based on photonics technologies.While we can efficiently solve combinatorial optimization problems by using LASOLV, there are technical challenges in its application to real-world problems.For example, we need to mathematically convert problems to a specific format for solving them using LASOLV.Moreover, LASOLV requires cooperation with conventional digital computers to solve complex problems.In this article, we introduce LASOLV Computing System, which is a computing platform that helps users overcome these challenges.
One of the problems that conventional computers, such as those based on central processing units, cannot solve is the combinatorial optimization problem, the solution to which is a pattern from among a huge number of combinations of patterns that satisfies a certain condition.Operating on the basis of a new principle, an Ising machine is a computer that specializes in solving this problem.However, there are many challenges to using an Ising machine, such as converting real-world problems into a format that the Ising machine can handle.Research and development of a software development kit and computing system that overcome these challenges are introduced in this article.
Abstract Ising machines can efficiently solve real-world combinatorial optimization problems. Among them, photonic Ising machines (PIMs) exploit the parallelism and broadband bandwidth of optical systems to outperform digital computers in certain metrics. However, the bulky footprint of conventional discrete components imposes significant limitations on the miniaturization of current PIMs. Meanwhile, the scalability of problem size and spin connectivity density is constrained by limited computational resources. To address these challenges, we proposed and demonstrated an integrated PIM (IPIM) based on a hybrid architecture on a silicon-based photonic platform with an optical component footprint of 0.065 mm², achieved success rates exceeding 90% in finding global optimal solutions across all three benchmark tests. To tackle the common challenge in PIMs, where large-scale and high-connectivity-density problems naturally incur high computational complexity, we developed a joint algorithm with sparsity, inverse sparsity, and decomposition operations (JA-SID). Using this approach, we successfully solved Ising problems with sparse and dense spin connectivity, involving 102,400 spins. In addition, we introduced an optimized parameter configuration scheme (OPCS), which reduced the iteration count by over 50% for slowly converging problems, thereby lowering time complexity and power consumption. In practice, we used the IPIM to successfully partition a Facebook social network with over 60,000 users, demonstrating its capability to handle ultra-large-scale real-world problems. Our research substantially extended the applicability of IPIM, offering a promising solution to the increasing global demand for computational resources.
Quantum computers are increasingly being integrated into classical computing environments, particularly within supercomputing and data centers, where they serve as accelerators for solving a wide range of specific problems. Previous research has indicated that quantum computers have the potential to resolve optimization problems with theoretically lower computational complexity than classical algorithms. However, in practical scenarios, even minor instances of these problems often require substantial computational resources, enhanced quality quantum hardware, and the results may not always be optimal. In this paper, we highlight the importance of hybrid systems, combining quantum and classical computations to address growing problem sizes and computational demands. We focus on optimization problems using hardware and software-enhanced quantum computers operational within a novel quantum–classical hybrid setup, incorporating GPUs and photonic quantum computers. The study specifically addresses combinatorial optimization problems, such as the Max-Cut and the Job Shop Scheduling Problem, in addition to selected machine learning classification use cases. Notably, the quantum algorithm in Max-Cut optimization outperformed complete solution searches, especially for larger problem instances. For the Job-Shop Scheduling Problem, we made a significant advancement by successfully solving substantially larger instances compared to our previous previous work. Furthermore, selected hybrid neural networks incorporating quantum layers showed improved stability, though without a clear quality advantage over classical models. The paper also highlights the rapid progress and technological achievements in both hardware and software used in near-term photonic quantum computers, suggesting a promising future for quantum–classical hybrid systems in useful applications.
Combinatorial optimization problems are central to many challenges in logistics, finance, engineering, and the life sciences, yet they remain among the most computationally demanding. Many of these problems can be mapped onto the Ising model, in which binary spins interact through a network of couplings, and solutions correspond to low-energy, ideally ground-state, spin configurations. Photonic Ising machines have the potential to be fast and energy-efficient heuristic solvers of optimization problems by leveraging the low latency, high bandwidth, and inherent parallelism of optics. However, current photonic implementations remain limited in scalability, connectivity, reconfigurability, and time-to-solution, preventing their use in many practical applications. In this perspective, we examine the current landscape of photonic Ising machines, discuss the challenges and limitations of existing platforms, and identify the scientific and technological advances needed to realize large-scale systems. These developments could establish photonic Ising machines as useful hardware platforms for practical optimization.
Abstract Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimization paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems.
The Ising machine (IM) has emerged as a promising tool for tackling nondeterministic polynomial-time hard combinatorial optimization problems in real-world applications. Among various types of IMs, optoelectronic IMs based on electro-optical (EO) modulators stand out as an impressive platform for Ising computations. They offer a simple and stable architecture, with the EO modulator providing a natural inline nonlinear transfer function for the Ising model. However, integrated optoelectronic IMs have not been demonstrated until now, and exploring large-scale computations within the constraints of digital hardware resources remains an open challenge for these systems. In this paper, an integrated optoelectronic IM based on a thin-film lithium niobate (TFLN) photonic chip is presented, in conjunction with a sparse matrix–vector multiplication algorithm embedded in a field-programmable gate array that optimizes hardware resource utilization and minimizes computational latency. This setup allows us to solve multiple types of MAX-CUT problems with up to 2048 spins and achieve a remarkably low iteration latency of 1.78 μs. To further address the constraints posed by digital devices when tackling larger-scale Ising problems, we extend the application of the TFLN chip to yet another new scheme in which the single, compact on-chip modulator concurrently performs operations of linear multiplication and nonlinear transformation. This scheme demonstrates the capability to address large-scale MAX-CUT problems involving up to 16,384 spins, which, to the best of our knowledge, are the largest-scale problems solved on an on-chip IM, highlighting its potential to overcome digital limitations. The TFLN-based optoelectronic IMs provide a compact solution with high scalability for potentially practical applications in addressing complex combinatorial optimization problems.