Efficient mapping of multi-constraint satisfaction problems to Rydberg platforms

• URL: http://arxiv.org/abs/2604.27030v1
• Date: Wed, 29 Apr 2026 15:01:02 GMT
• Title: Efficient mapping of multi-constraint satisfaction problems to Rydberg platforms
• Abstract summary: We present a hardware-native gadget framework for solving constraint satisfaction problems on Rydberg quantum computing architectures.<n>Our approach introduces a compact $xor_1$ gadget that enforces exactly-one constraints, ubiquitous in optimization.<n>We observe reductions in detuning range of up to $99%$ and savings in atom count and connectivity overhead.
• Score: 0.040150524643488644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a hardware-native gadget framework for solving constraint satisfaction problems on Rydberg quantum computing architectures. Our approach introduces a compact $xor_1$ gadget that enforces exactly-one constraints, ubiquitous in combinatorial optimization, directly through geometric embedding and blockade interactions. A key advantage of the $xor_1$ gadget is its fixed, problem-size-independent detuning requirements: enforcing constraints through blockade interactions eliminates the need for large penalty terms, thereby substantially reducing the detuning range compared to Quadratic Unconstrained Binary Optimization (QUBO) formulations and improving experimental feasibility. By tailoring the construction to the geometric connectivity of Rydberg atom arrays, the framework bypasses the all-to-all physical couplings often assumed in logical encodings. This enables embeddings compatible with planar layouts and avoids highly connected arrangements. We develop scalable implementations that reduce atom count and connectivity overhead while avoiding extensive classical preprocessing, making them compatible with near-term neutral-atom hardware. As illustrations, we apply our framework to the gate-assignment and $N$-queens problems, highlighting its practicality, resource efficiency, and hardware compatibility. In these examples, we observe reductions in detuning range of up to $99\%$ and savings in atom count and connectivity overhead of up to $54\%$ compared to the QUBO method. These results establish a route toward implementing large-scale combinatorial optimization on Rydberg platforms beyond the limits of existing encodings.

Related papers

• Sparse QUBO Formulation for Efficient Embedding via Network-Based Decomposition of Equality and Inequality Constraints [0.35331503620315147]
  We propose a method to construct a significantly sparser logical QUBO model for equality and inequality constraints.<n>By adding auxiliary variables based on specific network structures, our approach decomposes the original constraint into smaller, more manageable constraints.<n> Experimental results on D-Wave's hardware show that our formulation leads to substantial reductions in the number of qubits required for embedding.
  arXiv  Detail & Related papers  (2026-01-26T03:40:06Z)
• Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation [49.865020394064096]
  We propose textbfLighter-X, an efficient and modular framework that can be seamlessly integrated with existing GNN-based recommender architectures.<n>Our approach substantially reduces both parameter size and computational complexity while preserving the theoretical guarantees and empirical performance of the base models.<n>Experiments demonstrate that Lighter-X achieves comparable performance to baseline models with significantly fewer parameters.
  arXiv  Detail & Related papers  (2025-10-11T08:33:08Z)
• Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach [0.5497663232622965]
  We discuss the complexity-theoretic limits of reductions from arbitrary graphs to unit-disk instances.<n>We prove any such reduction incurs a blow-up in geometric count and degrades solution approximation guarantees.<n>As a practical alternative, we propose a divide-and-conquer with only linear overhead which leverages pred atomic layouts.
  arXiv  Detail & Related papers  (2025-08-08T08:50:29Z)
• A quantum wire approach to weighted combinatorial graph optimisation problems [0.0]
  We present and experimentally demonstrate an efficient encoding scheme based on chains of Rydberg-blockaded atoms.<n>We embed maximum weighted independent set (MWIS) and quadratic unconstrained binary optimization (QUBO) problems on a neutral atom architecture.
  arXiv  Detail & Related papers  (2025-03-21T13:00:51Z)
• Quantum Gate Optimization for Rydberg Architectures in the Weak-Coupling Limit [55.05109484230879]
  We demonstrate machine learning assisted design of a two-qubit gate in a Rydberg tweezer system. We generate optimal pulse sequences that implement a CNOT gate with high fidelity. We show that local control of single qubit operations is sufficient for performing quantum computation on a large array of atoms.
  arXiv  Detail & Related papers  (2023-06-14T18:24:51Z)
• Symmetric Tensor Networks for Generative Modeling and Constrained Combinatorial Optimization [72.41480594026815]
  Constrained optimization problems abound in industry, from portfolio optimization to logistics. One of the major roadblocks in solving these problems is the presence of non-trivial hard constraints which limit the valid search space. In this work, we encode arbitrary integer-valued equality constraints of the form Ax=b, directly into U(1) symmetric networks (TNs) and leverage their applicability as quantum-inspired generative models.
  arXiv  Detail & Related papers  (2022-11-16T18:59:54Z)
• Rydberg blockade based parity quantum optimization [0.0]
  We present a scalable architecture for solving higher-order constrained binary optimization problems on neutral-atom hardware. We formulate the recently developed parity encoding of arbitrary connected higher-order optimization problems as a maximum-weight independent set (textsfMWIS) Our architecture builds from small textsfMWIS modules in a problem-independent way, crucial for practical scalability.
  arXiv  Detail & Related papers  (2022-10-11T16:45:09Z)
• Quantum optimization with arbitrary connectivity using Rydberg atom arrays [0.0]
  We extend the classes of problems that can be efficiently encoded in Rydberg arrays by constructing explicit mappings from the original problems. We analyze several examples, including: maximum weighted independent set on graphs with arbitrary connectivity, quadratic unconstrained binary optimization problems with arbitrary or restricted connectivity, and integer factorization.
  arXiv  Detail & Related papers  (2022-09-08T18:00:00Z)
• Constrained mixers for the quantum approximate optimization algorithm [55.41644538483948]
  We present a framework for constructing mixing operators that restrict the evolution to a subspace of the full Hilbert space. We generalize the "XY"-mixer designed to preserve the subspace of "one-hot" states to the general case of subspaces given by a number of computational basis states. Our analysis also leads to valid Trotterizations for "XY"-mixer with fewer CX gates than is known to date.
  arXiv  Detail & Related papers  (2022-03-11T17:19:26Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• An Integer Linear Programming Framework for Mining Constraints from Data [81.60135973848125]
  We present a general framework for mining constraints from data. In particular, we consider the inference in structured output prediction as an integer linear programming (ILP) problem. We show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules.
  arXiv  Detail & Related papers  (2020-06-18T20:09:53Z)

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.