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.
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.
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.
Quantum annealing, a maturing technology, offers a potential solution to large scale combinatorial optimization problems by leveraging quantum bits (qubits) in superposition to explore vast solution spaces simultaneously, significantly reducing computation time.Currently, quantum annealers can handle up to 11,000 binary variables, with hybrid solvers capable of addressing problems with up to 1 million binary variables.Though not yet consistently optimal, these approaches provide high-quality solutions without the need for complex heuristics.This paper explores the potential application of quantum annealing to optimize supply chain operations at Dell, specifically within their Excess & Obsolete (E&O) inventory process.The focus is on identifying a proactive re-balancing strategy for inventory management, minimizing costs, and improving parts distribution across Dell's global locations.By utilizing simulated annealing accelerated by high-performance computing (HPC) resources, including Nvidia GPUs and NEC's vector engine, we aim to achieve near-optimal solutions for inventory rebalancing.Some models will also be evaluated on quantum hardware to explore the feasibility of quantum acceleration in real-world supply chain optimization.Preliminary results suggest that while quantum annealing is still in its early stages, it holds significant promise for cost savings and operational efficiency in largescale supply chains.
Quantum annealing (QA) has the potential to significantly improve solution quality and reduce time complexity in solving combinatorial optimization problems compared to classical optimization methods. However, due to the limited number of qubits and their connectivity, the QA hardware did not show such an advantage over classical methods in past benchmarking studies. Recent advancements in QA with more than 5000 qubits, enhanced qubit connectivity, and the hybrid architecture promise to realize the quantum advantage. Here, we use a quantum annealer with state-of-the-art techniques and benchmark its performance against classical solvers. To compare their performance, we solve over 50 optimization problem instances represented by large and dense Hamiltonian matrices using quantum and classical solvers. The results demonstrate that a state-of-the-art quantum solver has higher accuracy (~0.013%) and a significantly faster problem-solving time (~6561×) than the best classical solver. Our results highlight the advantages of leveraging QA over classical counterparts, particularly in hybrid configurations, for achieving high accuracy and substantially reduced problem solving time in large-scale real-world optimization problems.
Quantum computing has emerged as a transformative paradigm, leveraging principles of quantum mechanics to tackle computationally intractable problems. This manuscript explores the potential of quantum computing for solving complex engineering problems—specifically optimization, simulation, and data analysis tasks—using technologies and methodologies available up to 2022. We examine both gate-based and quantum annealing approaches, outline experimental frameworks, and compare performance against classical counterparts. Results from simulated benchmark problems demonstrate promising speedups in select cases, though practical adoption is constrained by hardware limitations such as qubit coherence times and noise. The study concludes by highlighting avenues for integrating hybrid quantum-classical workflows in engineering, along with a discussion of current constraints and future directions. Over the past decade, research efforts have focused on understanding how quantum phenomena like superposition and entanglement can be harnessed to realize computational advantages for specific problem classes. In particular, combinatorial optimization problems—ubiquitous in engineering design and logistics—offer fertile ground for quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing. Additionally, quantum simulation techniques, including the Variational Quantum Eigensolver (VQE), promise to accelerate the modeling of physical systems at the molecular and materials level, potentially informing the design of next-generation structural and functional materials. Beyond raw algorithmic performance, this paper carefully assesses the end‑to‑end workflow, accounting for overheads in problem encoding, error mitigation, and classical post‑processing. We also present a detailed analysis of resource utilization—quantified by qubit counts, circuit depths, and annealing schedules—highlighting trade‑offs between solution quality and execution time. Ultimately, our findings underscore that, while pure quantum speedups remain rare in 2022, hybrid approaches harnessing both quantum and classical resources can yield practical benefits for small‑to‑medium scale engineering tasks. This expanded evaluation provides a roadmap for researchers and practitioners interested in applying quantum computing to real‑world engineering challenges.
Critical decision-making issues in science, engineering, and industry are based on combinatorial optimization; however, its application is inherently limited by the NP-hard nature of the problem. A specialized paradigm of analogue quantum computing, quantum annealing (QA), has been proposed to solve these problems by encoding optimization problems into physical energy landscapes and solving them by quantum tunnelling systematically through exploration of solution space. This is a critical review that summarizes the current applications of quantum annealing to combinatorial optimization and includes a theoretical background, hardware designs, algorithm implementation strategies, encoding and embedding schemes, protocols to benchmark quantum annealing, areas of implementation, and links with the quantum algorithms implementation with gate-based hardware and classical solvers. We develop a unified framework, relating adiabatic quantum dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to modern flux-qubit annealers (Chimera, Pegasus, Zephyr topologies), and emergent architectures (Lechner-Hauke-Zoller systems, Rydberg atom platforms), and hybrids of quantum and classical computation. Through our analysis, we find that overhead in embedding and encoding is the largest determinant of the scalability and performance (this is not just the number of qubits). Minor embeddings also usually have a physical qubit count per logical variable of between 5 and 12 qubits, which limits effective problem capacity by 80-92% and, due to chain-breaking errors, compromises the quality of solutions.
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.