Mixed-precision quantization, which dynamically allocates bitwidths across layers, poses an NP-hard combinatorial optimization problem, since the exponentially growing search space makes traditional heuristic or sampling-based methods difficult to scale and prone to suboptimal solutions. Recent advances in quantum computing open up a promising new avenue, as its intrinsic parallelism enables efficient exploration of large combinatorial spaces and offers the potential to overcome the limitations of classical search. We propose Q4Q (Quantum-computing for Quantization), a quantum-based method for efficient bitwidth allocation in model quantization. Q4Q formulates accuracy loss and compression overhead as Hamiltonians, allowing Pareto-optimal solutions to be obtained from a single sampling. Guided by layer sensitivity, the method adapts bitwidth allocation to balance accuracy and compression. Experiments on several 7B models show that Q4Q achieves a 3–8 orders of magnitude speedup over traditional machine learning, improves Pareto solution quality by 3–15%, and significantly enhances energy efficiency. This work offers a new paradigm for lightweight deployment of large models in edge scenarios and underscores the promise of quantum computing in combinatorial optimization.
Mixed-precision quantization, which dynamically allocates bitwidths across layers, poses an NP-hard combinatorial optimization problem, since the exponentially growing search space makes traditional heuristic or sampling-based methods difficult to scale and prone to suboptimal solutions. Recent advances in quantum computing open up a promising new avenue, as its intrinsic parallelism enables efficient exploration of large combinatorial spaces and offers the potential to overcome the limitations of classical search. We propose Q4Q (Quantum-computing for Quantization), a quantum-based method for efficient bitwidth allocation in model quantization. Q4Q formulates accuracy loss and compression overhead as Hamiltonians, allowing Pareto-optimal solutions to be obtained from a single sampling. Guided by layer sensitivity, the method adapts bitwidth allocation to balance accuracy and compression. Experiments on several 7B models show that Q4Q achieves a 3–8 orders of magnitude speedup over traditional machine learning, improves Pareto solution quality by 3–15%, and significantly enhances energy efficiency. This work offers a new paradigm for lightweight deployment of large models in edge scenarios and underscores the promise of quantum computing in combinatorial optimization.
QuantaBit integrates a quantum-enhanced quantization solver with a mixed-precision inference engine. Starting from a pretrained FP16 Large Language Model (LLM), it analyzes layer-wise sensitivity, constructs a Hamiltonian that combines quantization-induced accuracy loss with compression loss, maps candidate bitwidths to compact binary variables, and finally solves the resulting QUBO on a Coherent Ising Machine (CIM). The decoded mixed-precision bitwidth allocation is then used to perform mixed quantization. QuantaBit invokes the inference engine for bitwidth-aware packing, heterogeneous kernel dispatch, and PagedAttention-based KV-cache management. Experiments on four LLMs show that QuantaBit decreases perplexity over uniform W4 and Cplex under the same average-bitwidth target, while deployed models reduce weight memory by over 3.4x, improve throughput by 15%, and extend context length by 53%.
Artificial Intelligence (AI) and quantum computing are converging to form a new era of ultra-fast computational problem-solving. Quantum optimization algorithms—when integrated with AI-driven heuristics, reinforcement learning, and neural-guided search models—offer unprecedented capabilities for tackling complex problems such as NP-hard optimization, logistics scheduling, cryptographic analysis, and large-scale simulation tasks. This research investigates the combined potential of AI-augmented quantum algorithms, focusing on how machine learning enhances qubit efficiency, error mitigation, quantum annealing performance, and adaptive gate-level optimization. A practical case study evaluates hybrid AI-quantum models applied to combinatorial optimization tasks, demonstrating significant reductions in computational time and improved solution accuracy. Findings highlight a future in which AI-enhanced quantum systems redefine computation, enabling breakthroughs in science, industry, and national innovation systems
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The convergence of machine learning (ML) and quantum computing is quickly becoming a game-changing new frontier in computational science. The most intriguing area of overlap may be quantum optimization, which seeks to reduce the exponentially increasing complexity and computational cost of ML algorithms. As they grow larger and more complicated, traditional optimization methods, particularly in high-dimensional and non-convex space, will inevitably hit performance limits. Quantum optimization is the whole new paradigm that leverages quantum mechanical effects like superposition, entanglement, and tunnelling to explore solution spaces more efficiently. The chapter outlines the theory, practice, and potential of hybrid quantum-classical optimization algorithms for machine learning. The most brilliant research direction in the current NISQ (noisy intermediate-scale quantum) era is mixed systems leveraging classical computing capability augmented by quantum capacity. In such designs, computationally intensive subroutines like optimization for global optima or escape from local minima are left to quantum processors and data preprocessing, iterative refinement, and model testing are performed by classical processors. We start by defining the fundamental principles of quantum computing and acquainting readers with the fundamentals like qubits, quantum gates, and quantum circuits. These are the building blocks for quantum algorithms, which hopefully can be utilized for optimization, for example, the QAOA (quantum approximate optimization algorithm), the VQE (variational quantum Eigensolver), and quantum annealing. They all build to optimize the problems in some way differently: QAOA through parameterized quantum circuits, VQE through approximations of ground states of the Hamiltonians, and quantum annealing through quantum evolution to low-energy solutions. We then form a loop between these quantum algorithms and regular machine learning tasks. Optimization impacts virtually all ML phases, from training neural networks, where one reduces loss functions; feature selection, where one aims to discover the most relevant subset of input variables; and hyperparameter optimization, which often involves expensive search procedures over extremely large combinatorial spaces. Quantum optimization techniques can therefore, in principle, accelerate such procedures through computational complexity reduction and enhancing solutions over challenging landscapes. Realistic simulations and case studies emphasize the degree to which hybrid quantum-classical algorithms have been employed in solving ML problems, that is, QSVMs, quantum-enhanced clustering, and quantum reinforcement learning. Despite their experimental nature, these applications already show encouraging results in terms of improved performance metrics as well as reduction in time-to-solution compared to classical-only solutions. Notwithstanding that, this new field is not bereft of real challenges. Quantum hardware suffers from hard limits on qubit fidelity, decoherence, and scalability in the current times. Furthermore, quantum circuit integration into broad ML pipelines requires novel architectures and software platforms. As a counter to those issues, this chapter surveys recent advances in quantum programming languages, simulators, and hybrid platforms like PennyLane, Qiskit, and TensorFlow Quantum. We conclude by looking to the future possibility that quantum optimization will revolutionize the machine learning landscape. As hardware improves and algorithmic complexity increases, the possibility of quantum-powered learning systems solving hard problems becomes increasingly accessible. From speeding up deep network training to enabling new types of unsupervised learning, quantum optimization holds the potential to recast computational tractability in machine learning. This chapter is an all-encompassing introduction and state-of-the-art work for students, researchers, and practitioners interested in using quantum optimization for practical ML applications. It sets the foundation for understanding how to pursue hybrid approaches (how they may be applied today in the real world and bracing oneself for disruption in the future).
Combinatorial optimization problems play a vital role in computer science and engineering, with applications in logistics, network design, finance, and resource allocation. However, many of these problems are NP-hard, making them computationally expensive to solve using classical optimization techniques, especially for large-scale instances. In recent years, quantum computing has emerged as a promising paradigm capable of addressing these limitations by leveraging quantum mechanical principles such as superposition and entanglement. This study investigates the application of quantum computing for solving combinatorial optimization problems, with a focus on algorithm design, complexity analysis, and real-world applicability. The research primarily employs the Quantum Approximate Optimization Algorithm (QAOA) and Grover's search algorithm to solve representative problems such as Max-Cut and the Knapsack problem. The performance of these algorithms is evaluated using key metrics including accuracy, execution time, and approximation ratio, and is compared with classical optimization techniques. The results demonstrate that QAOA is capable of generating near-optimal solutions, achieving accuracy levels of up to 93% for small problem instances. However, the study also highlights the challenges associated with current quantum systems, including noise, limited qubit availability, and increased execution time. Comparative analysis reveals that while classical methods remain more efficient for small-scale problems, quantum algorithms show significant potential for scalability and handling complex optimization tasks. The findings suggest that hybrid quantum-classical approaches offer a practical solution in the current Noisy Intermediate-Scale Quantum (NISQ) era. The study concludes that although quantum computing is still evolving, it holds strong potential for transforming combinatorial optimization in the future.
Chinese language presents unique challenges in text processing due to its complex structure, including high inter-lexical dependence, flexible word order, and rich contextual expressions. These characteristics make it particularly difficult for large models to effectively handle long Chinese texts using traditional machine learning (ML) algorithms. This paper explores the application of Quantum Machine Learning (QML) as a solution to enhance the performance of large language models (LLMs) in processing long Chinese texts. By leveraging the inherent advantages of QML, such as higher processing efficiency and deeper semantic understanding, this research demonstrates through experimental comparisons that QML models significantly outperform traditional ML models in both speed and comprehension accuracy. The paper systematically introduces text processing methods using both traditional ML and QML, and presents a simplified QML model designed to enhance text processing capabilities. The findings underscore the potential of QML in optimizing the future performance of LLMs, particularly for languages with complex linguistic structures like Chinese. This research holds significant implications for advancing LLM technologies in text-heavy applications.