Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy

• URL: http://arxiv.org/abs/2407.13616v1
• Date: Thu, 18 Jul 2024 15:55:01 GMT
• Title: Quantum Local Search for Traveling Salesman Problem with Path-Slicing Strategy
• Authors: Chen-Yu Liu, Hiromichi Matsuyama, Wei-hao Huang, Yu Yamashiro,
• Abstract summary: We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP) We explore various path slicing methods, including k-means and anti-k-means clustering, to divide the TSP into manageable subproblems. These are then solved using quantum or classical solvers.
• Score: 1.8186826508785554
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present novel path-slicing strategies integrated with quantum local search to optimize solutions for the Traveling Salesman Problem (TSP), addressing the limitations of current Noisy Intermediate-Scale Quantum (NISQ) technologies. Our hybrid quantum-classical approach leverages classical path initialization and quantum optimization to effectively manage the computational challenges posed by the TSP. We explore various path slicing methods, including k-means and anti-k-means clustering, to divide the TSP into manageable subproblems. These are then solved using quantum or classical solvers. Our analysis, performed on multiple TSP instances from the TSPlib, demonstrates the ability of our strategies to achieve near-optimal solutions efficiently, highlighting significant improvements in solving efficiency and resource utilization. This approach paves the way for future applications in larger combinatorial optimization scenarios, advancing the field of quantum optimization.

Related papers

• A Monte Carlo Tree Search approach to QAOA: finding a needle in the haystack [0.0]
  variational quantum algorithms (VQAs) are a promising family of hybrid quantum-classical methods tailored to cope with the limited capability of near-term quantum hardware. We show that leveraging regular parameter patterns deeply affects the decision-tree structure and allows for a flexible and noise-resilient optimization strategy.
  arXiv  Detail & Related papers  (2024-08-22T18:00:02Z)
• Parallel Quantum Local Search via Evolutionary Mechanism [0.9208007322096533]
  We propose an innovative Parallel Quantum Local Search (PQLS) methodology that leverages the capabilities of small-scale quantum computers. Our approach transcends this constraint by simultaneously executing multiple QLS pathways and aggregating their most effective outcomes at certain intervals to establish a generation'' Our findings demonstrate the profound impact of parallel quantum computing in enhancing the resolution of Ising problems.
  arXiv  Detail & Related papers  (2024-06-10T16:35:52Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Measurement-Based Quantum Approximate Optimization [0.24861619769660645]
  We focus on measurement-based quantum computing protocols for approximate optimization. We derive measurement patterns for applying QAOA to the broad and important class of QUBO problems. We discuss the resource requirements and tradeoffs of our approach to that of more traditional quantum circuits.
  arXiv  Detail & Related papers  (2024-03-18T06:59:23Z)
• Multi-Objective Optimization and Network Routing with Near-Term Quantum Computers [0.2150989251218736]
  We develop a scheme with which near-term quantum computers can be applied to solve multi-objective optimization problems. We focus on an implementation based on the quantum approximate optimization algorithm (QAOA)
  arXiv  Detail & Related papers  (2023-08-16T09:22:01Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Lyapunov control-inspired strategies for quantum combinatorial optimization [0.0]
  We provide an expanded description of Lyapunov control-inspired strategies for quantum optimization. Instead, these strategies utilize feedback from qubit measurements to assign values to the quantum circuit parameters in a deterministic manner.
  arXiv  Detail & Related papers  (2021-08-12T19:47:59Z)
• Purification and Entanglement Routing on Quantum Networks [55.41644538483948]
  A quantum network equipped with imperfect channel fidelities and limited memory storage time can distribute entanglement between users. We introduce effectives enabling fast path-finding algorithms for maximizing entanglement shared between two nodes on a quantum network.
  arXiv  Detail & Related papers  (2020-11-23T19:00:01Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)
• Cross Entropy Hyperparameter Optimization for Constrained Problem Hamiltonians Applied to QAOA [68.11912614360878]
  Hybrid quantum-classical algorithms such as Quantum Approximate Optimization Algorithm (QAOA) are considered as one of the most encouraging approaches for taking advantage of near-term quantum computers in practical applications. Such algorithms are usually implemented in a variational form, combining a classical optimization method with a quantum machine to find good solutions to an optimization problem. In this study we apply a Cross-Entropy method to shape this landscape, which allows the classical parameter to find better parameters more easily and hence results in an improved performance.
  arXiv  Detail & Related papers  (2020-03-11T13:52:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.