A variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks, achieving more than 50 × speedup compared to state-of-the-art optimization algorithms.
Abstract
Optimization problems are critical across various domains, yet existing quantum algorithms, despite their great potential, struggle with scalability and accuracy due to excessive reliance on entanglement. To address these limitations, we propose a variational quantum optimization algorithm (VQOA), which employs an ansatz based solely on quantum superposition, where single-qubit rotation gates function analogously to neurons in classical deep neural networks. This ansatz, which can be regarded as quantum neural networks, significantly reduces circuit complexity, enhances noise robustness, mitigates Barren Plateau issues, and enables efficient partitioning for highly complex, large-scale optimization. Furthermore, we introduce distributed VQOA (DVQOA), which integrates high-performance computing with quantum computing to achieve superior performance. These features enable a significant acceleration of material optimization tasks (e.g., metamaterial design), achieving more than 50 × speedup compared to state-of-the-art optimization algorithms. Beyond material design, DVQOA efficiently solves quantum chemistry problems and
N
-ary (
N
≥2) optimization problems involving higher-order interactions, outperforming classical deep neural networks. These advantages establish DVQOA as a highly promising and versatile solver for real-world problems, demonstrating the practical benefits of the quantum-classical approach.
Quantum machine learning (QML) faces practical limitations due to noisy intermediate-scale quantum (NISQ) constraints, including noise, restricted qubit availability, and unstable optimization. This paper proposes HyQNet, a resource-aware hybrid quantum–classical framework designed to address these challenges through efficient circuit execution and adaptive optimization. The framework integrates optimized quantum circuits with classical learning strategies to improve scalability and stability under NISQ conditions. Experimental results on Iris, Wine, and Breast Cancer datasets show that HyQNet achieves an accuracy of 95.1% and F1-score of 94.8%, outperforming variational QNN (92.6%) and quantum SVM (91.2%). It also reduces runtime to 16.9 s compared to 20.5 s for VQNN, while maintaining efficient utilization of 8 qubits. Statistical analysis confirms significance (p < 0.05), and ablation studies validate the contribution of each component. The results demonstrate improved convergence stability and resource efficiency in hybrid quantum learning systems.
Sudheer Reddy K., Hastimal Jangid, Usha Desai· 2026 International Conferenc...· 0 citations
This work introduces a scalable variational framework based on Pauli correlation encoding (PCE) and applies it to electric power demand portfolio optimization, establishing PCE as a physically motivated and qubit-efficient framework for large-scale combinatorial optimization.
Takuya Yoshioka, Keita Sasada, Riku Usuki et al.· 1 citation
While combinatorial optimization problems are central to many scientific and engineering applications, their solution remains challenging due to exponentially large search spaces. Variational quantum algorithms offer a promising route for tackling such problems, yet their practical performance is limited by repeated quantum circuit evaluations and classical parameter updates. In this work, we introduce DQAOA-GPT, a hybrid framework that integrates the distributed quantum approximate optimization algorithm (DQAOA), which decomposes a large optimization problem into smaller sub-problems, with GPT-based quantum circuit generation for solving those sub-problems. Rather than relying on iterative variational optimization, the proposed approach uses a trained generative model to directly generate high-quality quantum circuits for the decomposed sub-problems. As a benchmark, we evaluate DQAOA-GPT against conventional DQAOA on dense HUBO optimization problems with up to 100 decision variables. The results demonstrate that DQAOA-GPT significantly reduces computational cost while maintaining competitive solution quality, with larger acceleration observed for larger sub-problem sizes. Although this work focuses on benchmark-scale validation, the framework provides a promising foundation for larger-scale combinatorial optimization in hybrid HPC-QC environments through increased GPU resources and parallel computing capability.
Seongmin Kim, A. Rijal, Yuri Alexeev et al.· arXiv.org· 0 citations
Hybrid Variational Quantum Algorithms (VQAs) present a highly viable pathway to near-term quantum utility; however, their performance is fundamentally bottlenecked by classical-quantum communication latency in Quantumas-a-Service (QaaS) environments.
This paper proposes an optimized classical-quantum orchestration architecture designed to minimize cloud-induced latency and maximize Quantum Processing Unit (QPU) active compute time. By implementing edge-colocated classical optimizers alongside batched parameter-shift gradient evaluations, the system circumvents stateless cloud API barriers.
Benchmarking across parameterized quantum circuits ranging from 15 to 50 qubits demonstrates an 84% reduction in network-induced QPU idle time. The framework yields a 3.2 × speedup in overall convergence time for the Quantum Approximate Optimization Algorithm (QAOA) and up to a 98% reduction in classical API call overhead compared to standard RESTful QaaS execution models.
These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.
Akshay Joseph, R. Delhibabu· Frontiers of Computer Scienc...· 0 citations
The numerical simulations demonstrate the potential of QEVO to design drug-like molecules with anticancer properties, operating in combinatorial spaces composed of up to up to [Formula: see text] solutions, while employing a shallow quantum circuit that requires only a modest number of qubits.
F. Calcagno, Delmar G. A. Cabral, Ivan Rivalta et al.· Proceedings of the National...· 0 citations
Quantum computing and artificial intelligence (AI) are converging into a distinct research frontier commonly referred to as quantum-enhanced artificial intelligence, or quantum machine learning (QML). This paper presents a conceptual and integrative review of how principles from quantum physics superposition, entanglement, and interference can be embedded into machine learning pipelines to reshape computational paradigms for classification, optimization, and representation learning. Using a structured narrative-review methodology, the study synthesizes theoretical foundations, algorithmic building blocks (quantum feature maps, variational quantum circuits, quantum kernel methods), and application domains spanning drug discovery, finance, materials science, and natural language processing. The review develops a hybrid quantum-classical architecture model and a complexity-comparison framework contrasting classical algorithms with their quantum counterparts, including Grover's search and Shor's factoring algorithm. Findings indicate that while theoretical speedups are well established, practical quantum advantage on noisy intermediate-scale quantum (NISQ) hardware remains constrained by decoherence, barren plateaus, and limited qubit connectivity. The paper contributes a synthesized taxonomy of quantum-enhanced AI methods and an evidence-based research agenda emphasizing error mitigation, hardware-aware ansatz design, and hybrid workload partitioning. The discussion further situates these developments within the broader trajectory of next-generation computing, arguing that near-term value will accrue primarily through hybrid quantum-classical systems rather than fully quantum pipelines. Implications for researchers, industry practitioners, and policymakers are discussed, alongside limitations inherent to a literature-synthesis approach.
Mohammad Wali Khurami, Musawer Hakimi· Buana Information Technology...· 0 citations
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