Quantum Annealing Research Articles (Page 1)

Abstract The formation of energy communities is pivotal for advancing decentralized and sustainable energy management. Within this context, Coalition Structure Generation (CSG) emerges as a promising framework. The complexity of CSG grows rapidly with the number of agents, making classical solvers impractical for even moderate sizes. This suggests CSG as an ideal candidate for benchmarking quantum algorithms against classical ones. Facing ongoing challenges in attaining computational quantum advantage for exact optimization, we pivot our focus to benchmarking quantum and classical solvers for approximate optimization. Approximate optimization is particularly critical for industrial use cases requiring real-time optimization, where finding high-quality solutions quickly is often more valuable than achieving exact solutions more slowly. Our findings indicate that quantum annealing (QA) on DWave can achieve solutions of comparable quality to our best classical solver, but with more favorable runtime scaling, showcasing an advantage. This advantage is observed when compared to solvers, such as Tabu search, simulated annealing, and the state-of-the-art solver Gurobi in finding approximate solutions for energy community formation involving over 100 agents. DWave also surpasses 1-round QAOA on IBM hardware. 
Our findings represent the largest benchmark of quantum approximate optimizations for a real-world dense model beyond the hardware's native topology, where D-Wave demonstrates a scaling advantage.

Quantum Annealing Based Power Grid Partitioning for Parallel Simulation

This investigates Quantum Annealing as a game-changing strategy for dealing with complicated combinatorial optimization issues, with the goal of achieving sustainable supply chains and decreased carbon footprints. The purpose of the research is to harness the potential of Quantum Computing, notably focused on Quadratic Unconstrained Binary Optimization formulations, Ising Model, Maximum Cut Problem, and the Traveling Salesman Problem. The goal of this is to expand the use of Quantum Annealing methods in the logistics, supply chain, and environmental fields. The goal of this study is to use Quantum Annealing's powerful capability to effectively explore large solution spaces as a springboard for developing novel approaches. A quantum-powered paradigm change in supply chain optimization is made possible by the new application of Quantum Annealing to real-world sustainability concerns. This is groundbreaking since it is the first to include quantum algorithms into eco-friendly supply chain planning by going into the details of QUBO, the Ising Model, MAX-CUT, and TSP. It offers a fresh viewpoint on reducing the carbon footprint of supply chains, which in turn encourages more environmentally friendly corporate procedures. It lays the groundwork for future developments in quantum-enabled sustainability and highlights the critical role that cutting-edge technologies play in driving global supply chain environmental conservation initiatives.

The object of this study is multi-aircraft landing scheduling on single and multiple runways, which is an important aspect of modern air traffic management systems. The main problems solved in this research are the complexity of scheduling optimization due to limited runway capacity, the need to maintain a safe distance between aircraft, and the uncertainty of estimated time of arrival (ETA) which is often influenced by external factors such as weather and air traffic density. To overcome these challenges, this research proposes a hybrid approach between Long short-term memory-gradient boosting with the quantum annealing method. the results show that this approach is able to significantly improve the performance of the scheduling system, with an accuracy of 0.93, a precision of 0.91, a recall of 0.90, and an F1 score of 0.91. These values are higher than the model without quantum annealing, which only achieved an accuracy of 0.87, a precision of 0.85, a recall of 0.83, and an F1 score of 0.84. This improvement can be explained by the ability of LSTM-gradient boosting to predict ETA deviation more accurately, as well as the effectiveness of quantum annealing in solving the quadratic unconstrained binary optimization (QUBO) formulation efficiently. The unique feature of this research lies in the application of a hybrid model that combines the power of machine learning and quantum computing, achieving a balance between predictive accuracy and optimization efficiency. These research findings can be applied to air traffic scheduling systems at airports with single or multiple runways. Their implementation has the potential to improve operational efficiency, reduce delays, and enhance flight safety through more precise and adaptive landing time management

This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postprocessing and quantum annealing. Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization, necessitating robust and efficient noise-removal strategies. The proposed method evaluates filtered training subsets based on validation loss, iteratively refines loss estimates through surrogate model-based BBO with postprocessing, and leverages quantum annealing to efficiently sample diverse training subsets with low validation error. Experiments on a noisy majority bit task demonstrate the method's ability to prioritize the removal of high-risk mislabeled instances. Integrating D-Wave's clique sampler running on a physical quantum annealer achieves faster optimization and higher-quality training subsets compared to OpenJij's simulated quantum annealing sampler or Neal's simulated annealing sampler, offering a scalable framework for enhancing dataset quality. This work highlights the effectiveness of the proposed method for supervised learning tasks, with future directions including its application to unsupervised learning, real-world datasets, and large-scale implementations.

Abstract Wireless sensor networks (WSNs) are increasingly being targeted by malicious users who abuse resource scarcity to mask attack origins, rendering traditional IP traceback methods inappropriate due to excessive latency, low accuracy, and unacceptable overhead. IP traceback in this paper is formulated as a quadratic unconstrained binary optimization (QUBO) problem, and quantum annealing is used via D-Wave hardware and hybrid solvers, and is integrated into an NS3 simulation environment. Comparative study with classical packet marking and probabilistic sampling regimes indicates that the quantum-enhanced model achieves 90% traceback success rates and 5–10 percentage point false positive reduction with comparable latency and energy expenses. These results affirm that cyberphysical and IoT domains can be significantly benefited by quantum annealing for attacker localization in WSNs, resulting in practical, low-overhead security solutions.

Recent demonstrations on specialized benchmarks have reignited excitement for quantum computers, yet their advantage for real-world problems remains an open question. Here, we show that probabilistic computers, co-designed with hardware to implement Monte Carlo algorithms, provide a scalable classical pathway for solving hard optimization problems. We focus on two algorithms applied to three-dimensional spin glasses: discrete-time simulated quantum annealing and adaptive parallel tempering. We benchmark these methods against a leading quantum annealer. For simulated quantum annealing, increasing replicas improves residual energy scaling, consistent with extreme value theory. Adaptive parallel tempering, supported by non-local isoenergetic cluster moves, scales more favorably and outperforms simulated quantum annealing. Field Programmable Gate Arrays or specialized chips can implement these algorithms in modern hardware, leveraging massive parallelism to accelerate them while improving energy efficiency. Our results establish a rigorous classical baseline for assessing practical quantum advantage and present probabilistic computers as a scalable platform for real-world optimization challenges.

We study the application of emerging photonic and quantum computing architectures to solving the Traveling Salesman Problem (TSP), a well-known NP-hard optimization problem. We investigate several approaches: Simulated Annealing (SA), Quadratic Unconstrained Binary Optimization (QUBO-Ising) methods implemented on quantum annealers and Optical Coherent Ising Machines, as well as the Quantum Approximate Optimization Algorithm (QAOA) and the Quantum Phase Estimation (QPE) algorithm on gate-based quantum computers. QAOA and QPE were tested on the IBM Quantum platform. The QUBO-Ising method was explored using the D-Wave quantum annealer, which operates on superconducting Josephson junctions, and the Quantum Computing Inc (QCi) Dirac-1 entropy quantum optimization machine. Gate-based quantum computers demonstrated accurate results for small TSP instances in simulation. However, real quantum devices are hindered by noise and limited scalability. Circuit complexity grows with problem size, restricting performance to TSP instances with a maximum of 6 nodes. In contrast, Ising-based architectures show improved scalability for larger problem sizes. SQUID-based Ising machines can handle TSP instances with up to 12 nodes, while entropy computing implemented in hybrid optoelectronic components extend this capability to 18 nodes. Nevertheless, the solutions tend to be suboptimal due to hardware limitations and challenges in achieving ground state convergence as the problem size increases. Despite these limitations, Ising machines demonstrate significant time advantages over classical methods, making them a promising candidate for solving larger-scale TSPs efficiently.

Join order optimization is among the most crucial query optimization problems, and its central position is also evident in the new research field where quantum computing is applied to database optimization and data management. In this field, join order optimization is the most studied database problem, typically tackled with a quadratic unconstrained binary optimization model, which is solved using various meta-heuristics, such as quantum and digital annealing, the quantum approximate optimization algorithm, or the variational quantum eigensolver. In this study, we continue developing quantum computing techniques for left-deep join order optimization by presenting three novel quantum optimization algorithms. These algorithms are based on a higher-order unconstrained binary optimization model, which is a generalization of the quadratic model and has not previously been applied to database problems. Theoretically, these optimization problems naturally map to universal quantum computers and quantum annealers. Compared to previous studies, two of our algorithms are the first quantum algorithms to model the join order cost function precisely. We prove theoretical bounds by showing that these two methods encode the same plans as the dynamic programming algorithm with respect to the query graph, which provides the optimal result up to cross products. The third algorithm achieves plans at least as good as those of the greedy algorithm with respect to the query graph. These results establish a meaningful theoretical connection between classical and quantum algorithms for selecting left-deep join orders. To demonstrate the practical usability of our algorithms, we have conducted an extensive experimental evaluation on thousands of clique, cycle, star, tree, and chain query graphs using both quantum and classical solvers.

We experimentally demonstrate the fast generation of thermal states of a transmon using a single-junction quantum-circuit refrigerator (QCR) as an tunable environment. Through single-shot readout, we monitor the transmon up to its third excited state, assessing population distributions controlled by QCR drive pulses. Whereas cooling can be achieved in the weak-drive regime, high-amplitude pulses can generate Boltzmann-distributed populations from a temperature of 110 mK up to 500 mK within 100 ns. As we propose in our work, this fast and efficient temperature control provides an appealing opportunity to demonstrate a quantum heat engine. Our results also pave the way for efficient dissipative state preparation and for reducing the circuit depth in thermally assisted quantum algorithms and quantum annealing.

ABSTRACTApproximate block diagonalization is a problem of transforming a given symmetric matrix as close to block diagonal as possible by symmetric permutations of its rows and columns. This problem arises as a preprocessing stage of various scientific calculations and has been shown to be NP‐complete. In this paper, we consider solving this problem approximately using the D‐Wave Advantage quantum annealer. For this purpose, several steps are needed. First, we have to reformulate the problem as a quadratic unconstrained binary optimization (QUBO) problem. Second, the QUBO has to be embedded into the physical qubit network of the quantum annealer. Third, and optionally, reverse annealing for improving the solution can be applied. We propose two QUBO formulations and four embedding strategies for the problem and discuss their advantages and disadvantages. Through numerical experiments, it is shown that the combination of domain‐wall encoding and D‐Wave's automatic embedding is the most efficient in terms of usage of physical qubits, while the combination of one‐hot encoding and automatic embedding is superior in terms of the probability of obtaining a feasible solution. It is also shown that reverse annealing is effective in improving the solution for medium‐sized problems.

Probabilistic computing with p-bits is emerging as a computational paradigm for machine learning and for facing combinatorial optimization problems (COPs) with the so-called probabilistic Ising machines (PIMs). From a hardware point of view, the key elements that characterize a PIM are the random number generation, the nonlinearity, the network of coupled probabilistic bits, and the energy-minimization algorithm. Regarding the energy-minimization algorithm in this work we show that PIMs using the simulated quantum annealing (SQA) schedule exhibit better performance as compared to simulated annealing and parallel tempering in solving a number of COPs, such as maximum satisfiability problems, the planted Ising problem, and the traveling salesman problem. Additionally, we design and simulate the architecture of a fully connected CMOS-based PIM that is able to run the SQA algorithm having a spin-update time of 8 ns with a power consumption of 0.22 mW. Our results also show that SQA increases the reliability and the scalability of PIMs by compensating for device variability at an algorithmic level enabling the development of their implementation combining CMOS with different technologies such as spintronics. This work shows that the characteristics of the SQA are hardware agnostic and can be applied in the codesign of any hybrid analog-digital Ising machine implementation. Our results open a promising direction for the implementation of a new generation of reliable and scalable PIMs.

Abstract Layout optimization problems involve finding the optimal arrangement of elements in order to maximize efficiency. For instance, the wind farm layout optimization (WFLO) problem consists of the best turbine placement to maximize energy production while minimizing wake losses. As its nonlinear and combinatorial nature makes it challenging for traditional optimization methods, alternative approaches such as quantum annealing and quantum‐classical hybrid methods offer a promising alternative for tackling such complex problems. Here, WFLO is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem using the Jensen wake model. A quantum annealer is compared, the Gurobi solver, and the Quantum Approximate Optimization Algorithm (QAOA). The quantum annealer provides solutions one order of magnitude faster than Gurobi with at most 3% lower power output, making it suitable for rapid suboptimal approximations. These findings highlight the trade‐off between the quality of the solution and the computational time and demonstrate how quantum methods, especially when combined with classical solvers, can contribute to efficient renewable energy optimization.

Physics-based approaches to de novo drugdesigninvolve the simultaneous exploration of vast chemical and conformationalspaces. The rapid development of quantum computing technologies offersa promising perspective to efficiently tackle this challenge. In thiswork, we focus on peptide design and present a multiscale frameworkthat combines classical and quantum computing to optimize amino acidsequences and predict binding poses at atomic resolution. We illustrateour scheme by designing binders for several protein targets, and wecontrast the performance of the D-Wave quantum annealer with thatof an industry-grade classical optimizer. To assess our results, wecompare the designed sequences and the corresponding poses with thoseavailable in a data set of experimentally characterized peptide binders.

ABSTRACT Spectral unmixing is a popular technique in remote sensing, in which the measured spectrum of light is decomposed to determine which materials have produced the reflected signal. The technique gives insight into which materials are present on the target surface, making it useful in a wide variety of geoscience applications. However, the spectral unmixing problem tends to be computationally taxing to solve, limiting the extent to which the technique can be applied. This work serves as a proof-of-concept for the potential application of a particular type of quantum computing, quantum annealing, on the spectral unmixing problem. To do this, we devised the first Quadratic Unconstrained Binary Optimisation (QUBO) formulation for linear spectral unmixing. This formulation of the problem can be solved using simulated or quantum annealing. Using artificial spectral mixtures, the QUBO formulation and both annealing methods were tested on accuracy and required computation time. The results were compared to the popular Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm. The MESMA algorithm and simulated annealing achieve comparable accuracy at equal computation time, indicating some potential of annealing in spectral unmixing applications. Solving the QUBO formulation by means of quantum annealing was possible only on simplified versions of the unmixing problem due to current hardware restrictions. However, quantum annealing requires a fraction of the computation time of the MESMA algorithm or simulated annealing, indicating the potential of quantum annealing for these types of problems. If quantum computing devices continue to improve in the following years, practical applications of spectral unmixing may become more efficiently solvable using quantum annealing.

Finding a Hadamard matrix of a specific order using a quantum computer can lead to a demonstration of practical quantum advantage. Earlier efforts using a quantum annealer were impeded by the limitations of the present quantum resource and its capability to implement high order interaction terms, which for an M-order matrix will grow by O(M^2). In this paper, we propose a novel qubit-efficient method by implementing the Hadamard matrix searching algorithm on a gate-based quantum computer. We achieve this by employing the Quantum Approximate Optimization Algorithm (QAOA). Since high order interaction terms that are implemented on a gate-based quantum computer do not need ancillary qubits, the proposed method reduces the required number of qubits into O(M). We present the formulation of the method, construction of corresponding quantum circuits, and experiment results in both a quantum simulator and a real gate-based quantum computer.

Residual Errors after Quantum Annealing in the Axial Next Nearest Neighbor Ising Model: Impact of Critical Points and Modulated Correlation

ABSTRACT Innovative optimization methods that balance power efficiency, computational complexity, and performance are needed to meet IoT device demand for low-power, high-performance VLSI circuits. Combining quantum computing with traditional optimization methods is necessary due to the exponential development of circuit complexity. This study optimizes low-power VLSI circuits using a hybrid quantum-classical framework that uses quantum circuit optimization, quantum annealing, and hybrid variational methods. The framework reduces gate count, power consumption, and latency by combining quantum-assisted logic optimization with classical heuristics. Simulations and experiments prove the framework can improve energy-efficient hardware designs. Quantum gate reduction (QGR) and quantum state encoding (QSE) optimize Boolean logic, while quantum annealing refines transistor location and reduces leakage power. Classical post-processing methods like heuristic refinement and logic gate remapping fine-tune quantum-optimized circuits for practical application. Compared to classical approaches, the hybrid architecture reduces gate count by 35%, saves 28% power, and improves latency by 25%. Performance validation using IBM Quantum Experience and D-Wave devices shows that quantum-assisted VLSI optimization techniques are possible. Further research on fault-tolerant quantum computing, hybrid co-design, and real-world CMOS integration for next-generation semiconductor fabrication are planned. This study shows how quantum-classical hybrid techniques can alter electronic design automation.

Quantum ergodicity and scrambling in quantum annealers

Quantum Approaches for Vehicle Routing Optimization on Noisy Intermediate Scale Quantum Platforms: Applications of QAOA and Quantum Annealing for Vehicle Routing Problems [Focus: Quantum Software and its Engineering