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IEEE Military Communications Conference (MILCOM)

A Quantum Approximate Optimization Algorithm on Conflict Graphs for Radio Resource Allocation

Marina Gavrilovskaia, Kwang‐Cheng Chen

Published Oct 6, 2025• 1 citation
Publisher

Abstract

Radio resource allocation is always a central and complex computational task in state-of-the-art wireless networks. Computationally efficient implementation of radio resource allocation is certainly desirable in wireless networks, and can be generalized to many resource allocation/management problems in C4ISR systems. Quantum optimization, an emerging quantum computation technology, suggests a new avenue toward (radio) resource allocation particularly potential to reduce computing latency in systems. Quantum approximation optimization algorithm (QAOA) was recently shown to possibly achieve resource allocation (RRA) in limited scope, while the generalization of QAOA for RRA remains an active research subject. Considering OFDMA resource allocation, this paper extends QAOA to multiple radio blocks by quantum state preparation, and takes the interference between adjacent radio resource units into account by introducing the conflict graphs to formulate quantum optimization. Computational experiments successfully verify proposed quantum optimization to RRA.

DOI
10.1109/milcom64451.2025.11310580

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Radio resource allocation is always a central and complex computational task in state-of-the-art wireless networks. Computationally efficient implementation of radio resource allocation is certainly desirable in wireless networks, and can be generalized to many resource allocation/management problems in C4ISR systems. Quantum optimization, an emerging quantum computation technology, suggests a new avenue toward (radio) resource allocation particularly potential to reduce computing latency in systems. Quantum approximation optimization algorithm (QAOA) was recently shown to possibly achieve resource allocation (RRA) in limited scope, while the generalization of QAOA for RRA remains an active research subject. Considering OFDMA resource allocation, this paper extends QAOA to multiple radio blocks by quantum state preparation, and takes the interference between adjacent radio resource units into account by introducing the conflict graphs to formulate quantum optimization. Computational experiments successfully verify proposed quantum optimization to RRA.
Quantum optimization is expected to resolve many computationally hard problems, such as Radio Resource Allocation (RRA) in wireless communications and networking. However, given noisy intermediate-scale quantum (NISQ) computers that supply a very limited number of effective logical qubits after quantum error correction, computationally efficient algorithms are highly desirable to facilitate quantum advantages, particularly the required number of qubits. To achieve quantum advantageous optimization by small number of qubits, we formulate RRA as a Maximum Independent Set (MIS) on dense conflict graphs, which is challenging for the Quantum Approximate Optimization Algorithm (QAOA) using NISQ computing machines. We further propose a modified Quantum Local Search (QLS) framework that decomposes the global MIS into overlapping subgraphs defined by a tunable neighborhood radius. Hard (feasibility) constraints are enforced via partial mixers, while soft (adjacency) constraints are resolved in a classical post-processing step. Considering practical interference issues that are typically ignored in quantum computational research, we uniquely extend the method to accommodate minimal-adjacency scenarios by specifying shared boundaries causing limited interference. While complexity analysis shows polynomial scaling in the number of feasible placements, computational experiments demonstrate substantial savings on required qubits and simultaneously achieving optimal solutions across multiple adjacency configurations. Efficient quantum optimization of RRA by NISQ computers of significantly fewer qubits is therefore accomplished.
Quantum technologies have recently scaled up from laboratories into commercial applications thanks to the rapid technical developments and the growing investments in quantum computing. These developments open up the way for the emergence of the so-called noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithms (QAOAs) represent a class of algorithms tailored for the NISQera computing for provisioning tangible quantum advantages. Meanwhile, wireless communications networks have become more complex over time and the pressure to conquer communications complexity is intense for both researchers and system designers. Specifically, a major optimization problem in this context is the resource allocation in modern communications where typically appears as an intricate 0/1 knapsack (0/1-KP) problem and finding its optimal solution using classical computers is prohibitively difficult. Thus, a parallel QAOA framework for optimizing the 0/1- KP problems is proposed in this paper. The proposal has the space complexity ofO(n)\mathcal{O} (n)O(n)and pseudopolynomial time complexity ofO(nW)\mathcal{O} (nW)O(nW), whereWWWis the knapsack's total capacity andnnnis the total number of items. However, the proposed QAOA solution is highly parallel and can be implemented onMMMNISQ devices of n-qubits each to obtainO(nW/M)\mathcal{O}(n W / M)O(nW/M)time complexity andO(nM)\mathcal{O} (nM)O(nM)space complexity. Numerical experiments show high approximation ratios even for shallow depth QAOA instances.
This paper presents a novel approach for partitioning database sessions into groups using the quantum MaxCut problem solved by the Quantum Approximate Optimization Algorithm (QAOA). Each session is represented as a node in a graph, with edges indicating the degree of resource contention between sessions. By defining a comprehensive resource demand function that accounts for CPU time, memory usage, and input/output operations, the problem is formulated as a graph partitioning task aimed at optimally distributing sessions to minimize overlap and enhance resource utilization. The paper outlines the implementation of the QAOA algorithm using Cirq, including the construction of the quantum circuit, the optimization of parameters, and the measurement of results. The results are visualized through graphs and heatmaps that illustrate the division of sessions and the reduction in resource contention. As its main contribution, the paper demonstrates the practical application of quantum algorithms to resource allocation in database systems, indicating a promising approach for improving system performance and mitigating resource conflicts.
Discrete radio resource management problems in dense wireless networks are naturally cast as quadratic unconstrained binary optimization (QUBO) programs but are difficult to solve at scale. We investigate a quantum-classical approach based on the Recursive Quantum Approximate Optimization Algorithm (RQAOA), which interleaves shallow QAOA layers with variable elimination guided by measured single- and two-qubit correlators. For interference-aware channel assignment, we give a compact QUBO/Ising formulation in which pairwise interference induces same-channel couplings and one-hot constraints are enforced via quadratic penalties (or, optionally, constraint-preserving mixers). Within RQAOA, fixing high-confidence variables or relations reduces the problem dimension, stabilizes training, and concentrates measurement effort on a shrinking instance that is solved exactly once below a cutoff. On simulated instances of modest size, including a four-user, four-channel example, the method consistently returns feasible assignments and, for the demonstrated case, attains the global optimum. These results indicate that recursion can mitigate parameter growth and feasibility issues that affect plain QAOA, and suggest a viable pathway for near-term quantum heuristics in wireless resource allocation.
Open-Loop wireless communication enables minimal latency communication with the assistance of machine learning in various aspects. A typical predictive downlink radio resource access for such minimal latency communications requires real-time optimization, which is not possible for standard classical machine learning and particularly deep learning using artificial neural networks, due to computational complexity. However, quantum optimization introduces a new technology opportunity to achieve such a purpose. Turning an allocation or knapsack problem into a quadratic unconstrainted binary optimization problem, given a quantum edge processor, we employ quantum approximation optimization algorithm to accomplish with polynomial computational complexity, to successfully realize radio resource allocation and pave a new approach to 6G mobile communications of minimal latency communication.
Efficient spectrum sharing in the Citizens Broadband Radio Service (CBRS) band is essential for maximizing 5G network capacity, particularly when high-traffic base stations require simultaneous access to multiple channels. Standard formulations of the Quantum Approximate Optimization Algorithm (QAOA) impose such multi-channel constraints using penalty terms, so most of the explored Hilbert space corresponds to invalid assignments. We propose a subspace-confined QAOA tailored to CBRS multi-channel allocation, in which each node-wise channel register is initialized in a Generalized Dicke state and evolved under an intra-register XY mixer. This ansatz confines the dynamics to a tensor product of Johnson graphs that exactly encode per-node Hamming-weight constraints. For an 8-node CBRS interference graph with 24 qubits, the effective search space is reduced from the full Hilbert space of size 2242^{24}224 to 2916 feasible configurations. Within this subspace, the algorithm converges rapidly to low-conflict assignments without large penalty coefficients. Simulations on instances with up to eight nodes show that the proposed ansatz achieves near-optimal conflict levels and consistently outperforms standard penalty-based QAOA and a greedy classical heuristic in terms of feasibility. Noise simulations with depolarizing channels further indicate that the constraint-preserving structure maintains a high feasibility ratio in NISQ-relevant error regimes.