PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding

• URL: http://arxiv.org/abs/2608.02789v1
• Date: Mon, 03 Aug 2026 18:42:00 GMT
• Title: PACE-QAOA: Physics-Constrained Quantum Optimization for Qubit-Efficient Power System Islanding
• Abstract summary: Controlled islanding partitions a stressed power network to limit disrupted power transfer while preserving operational integrity in every island.<n>This paper develops a qubit-efficient hybrid quantum formulation that overcomes limited qubit capacity.<n>A qubit-efficient Lagrangian strategy combines quantum optimization with classical refinement to enforce operational constraints.
• Score: 10.677892416842944
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Controlled islanding partitions a stressed power network to limit disrupted power transfer while preserving operational integrity in every island. This NP-hard partitioning problem becomes increasingly demanding as networks grow, motivating quantum optimization as a complementary approach. However, limited qubit capacity restricts the scale at which conventional QAOA can address islanding. This paper develops a qubit-efficient hybrid quantum formulation that overcomes this barrier. A physics-informed compact encoding captures essential islanding decisions while exploiting grid structure, with formal guarantees preserving the feasible solution space and optimization objective. A qubit-efficient Lagrangian strategy combines quantum optimization with classical refinement to enforce operational constraints. Complexity analysis shows that for fixed island counts on sparse graphs, the formulation reduces phase-separator and per-layer gate complexity from quadratic to linear scaling. Evaluations on eight IEEE systems (9 to 89 buses) across multiple quantum backends produce feasible, high-quality solutions under practical circuit and sampling budgets. Factorial ablation attributes resource and runtime gains to the complementary effects of compact encoding and qubit-efficient Lagrangian constraint handling. Noise analysis demonstrates stable solution quality under device noise, and landscape diagnostics reveal smoother, more consistently scaled QAOA cost surfaces. These results provide a transferable pathway for scaling constrained quantum optimization toward larger real-world applications on near-term hardware.

Related papers

• SPLIT-Q: A Scalable Sequential Quantum Computing Framework for Coherent Controlled Islanding [10.677892416842944]
  Controlled islanding limits propagation by partitioning a compromised grid into connected, electrically sustainable islands.<n> classical methods face rapidly growing computational costs as network size and island count increase.<n>This study proposes a qubit-bounded sequential distributed quantum approximate optimization algorithm (QAOA) framework.
  arXiv  Detail & Related papers  (2026-08-13T01:47:54Z)
• REGRID-QAOA: A Resource-Efficient Graph-Reduced Hybrid QAOA Framework for Physics-Constrained Power System Islanding [13.146748002139057]
  This study develops a resource-efficient hybrid QAOA islanding framework that brings physics-constrained power-system partitioning into the quantum optimization workflow.<n>The proposed framework is validated on the standard IEEE benchmark systems (9-, 14-, 24-, 30-, 39-, and 57-bus)<n>This study establishes QAOA-based islanding as a viable quantum approach for critical infrastructure, with structured post-processing as the key enabler of quantum resource efficiency.
  arXiv  Detail & Related papers  (2026-06-13T03:30:50Z)
• Hardware-Efficient Quantum Optimization for Transportation Networks via Compressed Adiabatic Evolution [1.409283414986451]
  Transportation systems such as urban logistics, vehicle routing, and infrastructure planning require solving large-scale optimization problems under complex constraints.<n>We develop a hardware-grounded hybrid quantum optimization framework that uses Approximate Quantum Compilation (AQC) to compress early segments of digitized adiabatic evolution into shallow circuits.<n>Results show that moderate prefix compression reduces two-qubit gate depth while maintaining or improving feasible solution, particularly for routing problems.
  arXiv  Detail & Related papers  (2026-04-28T23:45:54Z)
• EQE-QAOA: An Equivalence-Preserving Qubit Efficient Framework for Combinatorial Optimization [54.05451096499336]
  Existing techniques rely on qubit reduction at the cost of information loss, leading to degraded computational performance.<n>We propose the Equivalence-preserving Qubit Efficient QAOA, which significantly reduces the required number of qubits without degrading performance.<n>We show that it is broadly applicable to large-scale optimization problems, excluding only unconstrained problems with completely independent variables.
  arXiv  Detail & Related papers  (2026-04-20T13:57:49Z)
• A Scalable Distributed Quantum Optimization Framework via Factor Graph Paradigm [46.08923284345648]
  We introduce a structure-aware framework for distributed quantum optimization.<n>We find that for a search space of size $N$, our framework achieves $O(sqrtN)$ query complexity up to processors and separator dependent factors.<n>We show that structure-aware decomposition delivers a practical path to scalable distributed quantum optimization on quantum networks.
  arXiv  Detail & Related papers  (2026-03-08T15:15:52Z)
• Tensor Network Assisted Distributed Variational Quantum Algorithm for Large Scale Combinatorial Optimization Problem [19.046113542182436]
  We propose the Distributed Variational Quantum Algorithm (DVQA) for solving Combinatorial Optimization Problems (COPs)<n>A key innovation of DVQA is its use of the truncated higher-order singular value decomposition to preserve inter-variable dependencies without relying on complex long-range entanglement.<n> Empirically, DVQA achieves state-of-the-art performance in simulations and has been experimentally validated on the Wu Kong quantum computer for portfolio optimization.
  arXiv  Detail & Related papers  (2026-01-20T13:31:02Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• Constraint-Aware Quantum Optimization via Hamming Weight Operators [1.1463843149836523]
  Constrained optimization with strict linear constraints underpins applications in drug discovery, power grids, logistics, and finance.<n>We introduce Hamming Weight Operators, a new class of constraint-aware operators that confine quantum evolution strictly within the feasible subspace.<n>We validate our approach on benchmark tasks from both finance and high-energy physics, specifically portfolio optimization and two-jet clustering with energy balance.
  arXiv  Detail & Related papers  (2026-01-04T12:58:04Z)
• Scaling Up the Quantum Divide and Conquer Algorithm for Combinatorial Optimization [0.8121127831316319]
  We propose a method for constructing quantum circuits which greatly reduces inter-device communication costs. We show that we can construct tractable circuits nearly three times the size of previous QDCA methods while retaining a similar or greater level of quality.
  arXiv  Detail & Related papers  (2024-05-01T20:49:50Z)
• 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)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• 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)

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.