In this paper, we aim to compare Vector Annealing (VA), a quantum-inspired metaheuristic introduced by NEC, with other metaheuristics. For this comparison, we utilize Quantum Annealing (QA), as implemented by D-Wave, and the conventional Simulated Annealing (SA) as a reference point for the capabilities of classical hardware with classical algorithms. The applications used for performance evaluation are two important benchmark programs designed to assess quantum hardware. This comparison is relevant because, for benchmarking purposes, it is valuable to have reference points between classical and quantum computing. It is widely thought that quantum computers will take some time to reach the level of relevance currently held by classical computing. This research, focusing on the Q-Score benchmark and Maximum Cardinality Matching (MCM) problems -both part of the MetriQs/BACQ benchmarking initiative- demonstrates this point. Through this work, we also trace the evolution from VA 2.0 to VA 3.0. While VA 2.0 already showed a significant advantage over both QA and SA in constrained problems, VA 3.0 introduces substantial optimizations and overcomes previous limitations in unconstrained problems. This advancement highlights the potential of VA 3.0. Furthermore, this work paves the way for the comprehensive testing of VA within the context of the MetriQs/BACQ quantum benchmark, an application-oriented French benchmarking initiative for quantum computers.
In this paper, we aim to compare Vector Annealing (VA), a quantum-inspired metaheuristic introduced by NEC, with other metaheuristics. For this comparison, we utilize Quantum Annealing (QA), as implemented by D-Wave, and the conventional Simulated Annealing (SA) as a reference point for the capabilities of classical hardware with classical algorithms. The applications used for performance evaluation are two important benchmark programs designed to assess quantum hardware. This comparison is relevant because, for benchmarking purposes, it is valuable to have reference points between classical and quantum computing. It is widely thought that quantum computers will take some time to reach the level of relevance currently held by classical computing. This research, focusing on the Q-Score benchmark and Maximum Cardinality Matching (MCM) problems -both part of the MetriQs/BACQ benchmarking initiative- demonstrates this point. Through this work, we also trace the evolution from VA 2.0 to VA 3.0. While VA 2.0 already showed a significant advantage over both QA and SA in constrained problems, VA 3.0 introduces substantial optimizations and overcomes previous limitations in unconstrained problems. This advancement highlights the potential of VA 3.0. Furthermore, this work paves the way for the comprehensive testing of VA within the context of the MetriQs/BACQ quantum benchmark, an application-oriented French benchmarking initiative for quantum computers.
We benchmark Quantum Annealing (QA) vs. Simulated Annealing (SA) with a focus on the impact of the embedding of problems onto the different topologies of the D-Wave quantum annealers. The series of problems we study are especially designed instances of the maximum cardinality matching problem that are easy to solve classically but difficult for SA and, as found experimentally, not easy for QA either. In addition to using several D-Wave processors, we simulate the QA process by numerically solving the time-dependent Schrödinger equation. We find that the embedded problems can be significantly more difficult than the unembedded problems, and some parameters, such as the chain strength, can be very impactful for finding the optimal solution. Thus, finding a good embedding and optimal parameter values can improve the results considerably. Interestingly, we find that although SA succeeds for the unembedded problems, the SA results obtained for the embedded version scale quite poorly in comparison with what we can achieve on the D-Wave quantum annealers.
Summary Oil and Gas exploration or drill sequence planning is a difficult optimization problem which infeasible to be solved by classical computers today. They are called combinatorial optimization problems (or NPHard). Solving such challenge can yield tremendous benefits in terms of improved success ratio and rescued cost and resources required. These problems are expected to be solved by Quantum Computers (QC) called Quantum Annealing (QA). The current evolution of the quantum hardware or limited availability of actual qubits. This limitation makes the pure QA solution infeasible for solving large-scale real-world problems or large number of qubits. For overcoming limitation and simulating annealing, Vector Engine Accelerator (VE) has the capability of simulating 100K fully connected qubits per PCIe based card. QA works using quantum fluctuations. Quantum Simulated Annealing (SA) or Vector Annealing (VA) works simulating the quantum effect using meta-heuristic approach on a classical computer. VA on VE solution has advantage in comparison to other SA on classical computers is large number of qubits support and capability of accelerating the same. In the presentation we will be showcasing not only the capability to solve combinatorial optimization problem but also user can accelerate the performance and making a real-world applicable solution.
Quantum computing has emerged as a promising computational paradigm with the potential to solve problems that are intractable for classical computers. This paper provides an overview of the current state of quantum computing, including its historical development, hardware advancements, applications, and challenges. Special attention is given to combinatorial optimization, which is considered one of the most promising application domains. The limitations of current quantum systems and future research directions are also discussed. This paper presents a comparative study between a conventional D-Wave-based simulated annealing approach and a proposed hybrid optimization model for solving largescale combinatorial optimization problems. The study focuses on benchmark problems, including AES, TSP gr24, and a real-world load balancing problem, all formulated as QUBO models. The D-Wave approach employs the dwave-neal simulated annealing engine with various parameter settings, while the proposed model introduces a decomposition-based framework combined with adaptive parameter control and iterative refinement. The experimental results demonstrate that the proposed model consistently outperforms the conventional approach in terms of solution quality, achieving significantly lower energy values across all problem instances. In particular, for the TSP gr24 problem, the proposed model achieves near-optimal solutions with substantially reduced computation time. For large-scale problems such as AES and load balancing, the proposed model maintains scalability and robustness, whereas the conventional approach suffers from increased computational cost and performance degradation. Although the proposed method introduces additional computational steps, its hybrid structure effectively mitigates limitations related to local minima, parameter sensitivity, and scalability. These findings indicate that integrating classical optimization techniques with quantum-inspired methods is essential for practical applications. The proposed model provides a promising framework for solving complex real-world optimization problems and highlights the importance of hybrid approaches in advancing quantum annealing technologies.
Combinatorial optimization is a foundation of numerous scientific, industrial, and social decision-making questions, but its usefulness is mostly restricted by the exponential complexity of the problem of classical computability. The quantum annealing (QA) has become one of the leading analogue quantum systems to address such problems, whereby optimization problems are converted into physical energy structures and quantum fluctuations are utilized to perform systematic exploration. It provides a critical, synthesized, and comprehensive review of quantum annealing as applied to combinatorial optimization, including the theoretical basis, hardware designs, algorithm methods, issues of embedding and encoding, benchmarking procedures, applications, and integration with quantum algorithms based on gates and solvers on a classical computer. We build a comprehensive taxonomy between adiabatic dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to state-of-the-art flux-qubit annealers, new architectures, and hybrid quantum classical pipelines. We show that the overhead of embedding and encoding and the benchmarking approach taken are the most significant factors in determining scalability and performance, but not the raw number of qubits. Transportation, energy systems, robotics, finance, pharmacological discovery, and machine learning are just some examples of the domain-specific case studies that use QA with minimal empirical utility as a hybrid refinement engine and not an independent solver. Moreover, we introduce a stringent evaluation of the existing benchmarking culture, clarify the causal links between QA and QAOA as well as between VQE and structural predicaments, and reveal the institutional roots of the existing restrictions. We conclude by identifying a research roadmap in the future, focusing on the annealing hardness characterization, Stoquastic control manipulation, embedding rules automation, architecture design, and development of principled quantum advantage. It creates a benchmark reference to those in the field and research and deployment of scalable, reliable, and application-relevant quantum optimization
Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. The Quantum Approximate Optimization Algorithm and Quantum Annealing can potentially demonstrate significant run-time performance benefits over current state-of-the-art solutions. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-cut problems using gate-model quantum devices and a quantum annealing device. This is used to guide the development of an advanced benchmarking framework for quantum computers designed to evaluate the trade-off between run-time execution performance and the solution quality for iterative hybrid quantum-classical applications. The framework generates performance profiles through compelling visualizations that show performance progression as a function of time for various problem sizes and illustrates algorithm limitations uncovered by the benchmarking approach. As an illustration, we explore the factors that influence quantum computing system throughput, using results obtained through execution on various quantum simulators and quantum hardware systems.
While we are still far from Fault Tolerant Quantum Computers (FTQCs) which are required to obtain a significant advantage in some algorithms, for benchmarking purposes, it can be interesting to try and solve problems with Noisy Intermediate Scale Quantum (NISQ) era Quantum Computers (QCs) on some of the future applications of FTQC. In this paper, our aim is to assess the performance of D-Wave Quantum Processing Units (QPUs) in solving systems of linear equations. This can also be used as a first reference point to compare NISQ era QPUs between them. To that end, we illustrate the transformation of these systems into Quadratic Unconstrained Binary Optimization (QUBO) problems, which can be tackled by these QPUs. We evaluate their performance in their default configurations and demonstrate some usual way to enhance their performance. Finally, we introduce a novel approach to solving these problems that does not result in an exponential growth of their coefficients. Data-points from these experiments are meant to be integrated in the BACQ benchmark [2].