Matrix multiplication (MatMul) is the computational backbone of modern machine learning, yet its classical complexity remains a bottleneck for large scale data processing. We propose a hybrid quantum classical algorithm for matrix multiplication based on an adaptive configuration of Hadamard tests. By introducing classical memoization that caches state preparation blocks outside the main compilation loop, we reduce the total classical pre processing overhead for all N^2 element circuits to O(N^2). This decouples the heavy gate synthesis overhead from the core quantum processing loop, enabling execution complexities that strictly match classical input/output boundaries.
We introduce an "Adaptive Stacking" framework that allows the algorithm to dynamically reconfigure its execution pattern - from sequential horizontal stacking to massive vertical parallelism - based on available qubit resources. This flexibility enables a tunable time complexity range, theoretically reaching O(N^2) on fault tolerant systems while maintaining compatibility with near term hardware.
Our core theoretical contribution is the formalization of the "Entropy Dividend": an information theoretic concentration bound proving that the effective measurement variance sigma^2_eff <= (1/S) (1 - (1/2N) e^[H_max - H(psi)]) approaches its maximum. This makes AQ-Stacker uniquely suited for stabilizing the stochastic weight distributions of deep neural networks. We validate the numerical stability of our approach through Quantum Machine Learning (QML) Statevector simulations, achieving 96% accuracy on the MNIST handwritten digit dataset. Our results suggest that entropic noise suppression and parallel Hadamard stacking provide a scalable path toward super-classical efficiency in next-generation quantum-enhanced AI.
Matrix multiplication (MatMul) is the computational backbone of modern machine learning, yet its classical complexity remains a bottleneck for large scale data processing. We propose a hybrid quantum classical algorithm for matrix multiplication based on an adaptive configuration of Hadamard tests. By introducing classical memoization that caches state preparation blocks outside the main compilation loop, we reduce the total classical pre processing overhead for all N^2 element circuits to O(N^2). This decouples the heavy gate synthesis overhead from the core quantum processing loop, enabling execution complexities that strictly match classical input/output boundaries.
We introduce an "Adaptive Stacking" framework that allows the algorithm to dynamically reconfigure its execution pattern - from sequential horizontal stacking to massive vertical parallelism - based on available qubit resources. This flexibility enables a tunable time complexity range, theoretically reaching O(N^2) on fault tolerant systems while maintaining compatibility with near term hardware.
Our core theoretical contribution is the formalization of the "Entropy Dividend": an information theoretic concentration bound proving that the effective measurement variance sigma^2_eff <= (1/S) (1 - (1/2N) e^[H_max - H(psi)]) approaches its maximum. This makes AQ-Stacker uniquely suited for stabilizing the stochastic weight distributions of deep neural networks. We validate the numerical stability of our approach through Quantum Machine Learning (QML) Statevector simulations, achieving 96% accuracy on the MNIST handwritten digit dataset. Our results suggest that entropic noise suppression and parallel Hadamard stacking provide a scalable path toward super-classical efficiency in next-generation quantum-enhanced AI.
Matrix multiplication is a fundamental operation in compute-intensive tasks and a key component of modern quantum acceleration frameworks. Here we present a quantum matrix multiplication algorithm based on quantum kernels (QKMM), achieving an elementary gate complexity of O(N2log2N), with amplitude encoding overhead explicitly included and without assuming a QRAM oracle. This scaling is asymptotically lower than that of the best-known classical matrix multiplication algorithm O(N2.371339). Building upon QKMM, we establish a family of quantum linear algebra operators, including Quantum Vector Inner Product (V2V), Quantum Vector-Matrix Multiplication (V2M), QKMM (M2M), Quantum One-to-Many Matrix Multiplication(O2M) and Quantum Sequential Matrix Multiplication (SMM), providing a unified framework from vector operations to parallel and sequential matrix transformations. Through noiseless simulations, realistic noise modelling and experiments on a superconducting quantum processor, we systematically characterize the numerical accuracy, resource requirements and hardware execution limits of this operator framework. Furthermore, we integrate SMM into deep neural-network inference, enabling intermediate features to propagate coherently across layers without repeated measurement and re-encoding. These results establish a pathway from quantum circuit-level algorithm design to end-to-end coherent computation, providing a quantum computing framework for matrix-centric compute-intensive applications.
Image classification plays a vital role in large-scale data analysis, especially in object recognition tasks using advanced Deep Learning (DL) frameworks. However, the growing complexity and computational demands of modern DL models have introduced challenges related to scalability and efficiency. Quantum Computing (QC) has emerged as a promising alternative, capable of addressing these limitations by leveraging the principles of Quantum Machine Learning (QML). However, many existing QML models require a large number of qubits, which poses limitations within the current Noisy Intermediate-Scale Quantum (NISQ) era. This work introduces a novel Adaptive Quantum Convolutional Neural Network (AQCNN) designed for efficient and scalable image classification within the constraints of NISQ devices. Addressing the limitations of existing QML approaches, particularly the high qubit requirements, AQCNN incorporates a resource-efficient quantum convolutional layer that performs localized quantum filtering using parameterized quantum circuits. A classical preprocessing layer encodes input features to reduce qubit load, followed by quantum embedding and hybrid quantum-classical layers that optimize feature extraction and classification performance. The model leverages an adaptive quantum convolution strategy, minimizing quantum gate depth and circuit complexity while preserving critical spatial hierarchies in image data. Evaluated on benchmark datasets, AQCNN achieved 95.88% accuracy on MNIST and 95.68% on FMNIST, outperforming comparable QML architectures. Additionally, the model supports scalable execution through parallel quantum circuit arrays, enabling practical deployment on current quantum hardware. This architecture demonstrates a significant advance in quantum-assisted image classification, balancing performance with qubit and gate efficiency. The integration of adaptive quantum convolution and hybrid processing not only enhances classification accuracy but also provides a viable path forward for deploying QML solutions under realistic hardware constraints.
This paper addresses the critical limitations of existing quantum machine learning (QML) frameworks concerning scalability, specifically their inability to efficiently handle large-scale quantum datasets. We propose a novel, scalable QML framework designed to effectively process these datasets and accelerate the training and inference of quantum machine learning algorithms. The core mechanism leverages quantum parallelism, constructing a framework capable of handling increased complexity. This work represents a significant advancement by directly tackling the scalability bottleneck in QML, establishing a foundational element for future developments in the field. The framework utilizes a modular architecture incorporating optimized quantum circuits and classical processing units for data preparation, model training, and result evaluation. The key innovation lies in the dynamic partitioning of quantum computations across multiple quantum processors, coupled with sophisticated data encoding strategies to mitigate the effects of qubit decoherence and limited qubit counts. This approach allows for the parallel execution of complex quantum algorithms, drastically reducing computational overhead and enabling the training of larger and more sophisticated models. We demonstrate the potential of this framework through theoretical analysis and algorithmic design, highlighting its advantages over traditional, monolithic QML implementations.
The core step in quantum simulations is typically matrix vector multiplication $φ= \Hmat ψ$. Executing this step is limited by memory requirement to store the Hamiltonian. We present a memory-scalable, hardware-adaptive matrix-free framework for applying large operators on vectors without materializing the full matrix on a single accelerator.
The operator is represented through a block-procedural interface: blocks may be generated, loaded, cached, distributed, or applied directly only when their action is needed. For quantum simulation, it provides the core kernel for quantum operations. An adaptive planner selects block size, cache strategy, GPU grouping, row distribution, and task parallelization from memory and workload estimates. We describe analytic, measured, and learned planning strategies that choose between procedural generation, partial caching, full caching, and row-distributed caching. The method removes the requirement that the full dense matrix fit in the accelerator memory. This shifts large simulations from a fixed memory barrier to a tunable balance between block generation, cache reuse, data movement, parallel scheduling, and numerical accuracy.
Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.
While real quantum devices have been increasingly used to conduct research focused on achieving quantum advantage or quantum utility in recent years, executing deep quantum circuits or performing quantum machine learning with large-scale data on current noisy intermediate-scale quantum devices remains challenging, making classical simulation essential for quantum machine learning research. However, such classical simulation often suffers from the cost of gradient calculations, requiring enormous memory or computational time. To address these problems, we propose a method to fuse multiple consecutive gates in each of the forward and backward paths to improve throughput by minimizing global memory accesses. As a result, we achieved approximately 20 times throughput improvement for a Hardware-Efficient Ansatz with 12 or more qubits, reaching over 30 times improvement on a mid-range consumer GPU with limited memory bandwidth. By combining our proposed method with gradient checkpointing, we drastically reduced memory usage, making it possible to train a large-scale quantum machine learning model, a 20-qubit, 1,000-layer model with 60,000 parameters, using 1,000 samples in approximately 20 minutes per epoch. This implies that we can train the model on large datasets, comprising tens of thousands of samples, like MNIST or CIFAR-10, within a realistic time frame (e.g., 20 hours per epoch). Thus, our proposed method significantly accelerates such classical simulations, making a significant contribution to advancing research in quantum machine learning and variational quantum algorithms, such as verifying algorithms on large datasets or investigating learning theories of deep quantum circuits like barren plateaus.