As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inference performance of models. Harnessing the parallel processing capabilities afforded by quantum superposition and entanglement to reshape matrix multiplication implementations has become a promising entry point for optimising underlying quantum arithmetic logic and improving the operational efficiency of quantum circuits. This paper proposes a universal quantum matrix multiplication (QMM) framework designed to achieve substantial computational acceleration through an optimised quantum arithmetic logic unit. To circumvent the limitations of multi-register and multi-control gates in conventional quantum arithmetic circuits, we encode classical data directly into parameterised \(R_z\) rotation gates using the quantum Fourier transform (QFT), thereby reducing the base gate complexity of the quantum adder to \(O(n)\). In addition, by adopting the column-wise multiplication principle from classical arithmetic, we optimize the gate complexity of the quantum multiplier to \(O(n^2)\). We further extend this approach to a quantum version of the Strassen algorithm, and experimentally quantify the trade-off between reduced multiplication time and increased overhead in addition resources. This work establishes a reliable technical pathway for constructing general-purpose quantum matrix operations, with the potential to unlock substantial computational power for training modern machine learning models.
As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inference performance of models. Harnessing the parallel processing capabilities afforded by quantum superposition and entanglement to reshape matrix multiplication implementations has become a promising entry point for optimising underlying quantum arithmetic logic and improving the operational efficiency of quantum circuits. This paper proposes a universal quantum matrix multiplication (QMM) framework designed to achieve substantial computational acceleration through an optimised quantum arithmetic logic unit. To circumvent the limitations of multi-register and multi-control gates in conventional quantum arithmetic circuits, we encode classical data directly into parameterised (R_z) rotation gates using the quantum Fourier transform (QFT), thereby reducing the base gate complexity of the quantum adder to (O(n)). In addition, by adopting the column-wise multiplication principle from classical arithmetic, we optimize the gate complexity of the quantum multiplier to (O(n^2)). We further extend this approach to a quantum version of the Strassen algorithm, and experimentally quantify the trade-off between reduced multiplication time and increased overhead in addition resources. This work establishes a reliable technical pathway for constructing general-purpose quantum matrix operations, with the potential to unlock substantial computational power for training modern machine learning models.
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.
Quantum computing (Q-computing) is a revolutionary technology that applies quantum mechanics (QM) to implement calculations significantly quicker than traditional computers. Q-computing employs Q-bits (qubits) rather than classical bits, which can take on a superposition state, making it possible to represent both 0 and 1 at once. This distinctive property allows qubits to perform multiple calculations simultaneously, making Q computers exponentially more powerful as more qubits are added, compared to the linear scaling of classical computers. However, challenges such as qubit instability and error rates remain significant obstacles to fully realizing this potential. A vital aspect of this technology is the development of Q arithmetic circuits, which act as the foundational blocks for efficiently executing Q algorithms. Q half-adder and full-adder circuits are integral to performing fundamental arithmetic operations within Q computers. In contrast to classical adders, Q adders rely on Q gates such as Controlled NOT (CNOT) and Toffoli gates. These gates exploit Q phenomena like entanglement and superposition, allowing for more efficient computations. The Q half-adder consists of two qubits and adds two single bits, producing a carry and sum output. It utilizes the CNOT gate to compute the sum and the Toffoli gate (controlled-controlled-NOT [CCNOT]) to compute the carry. The Q full-adder builds on this, adding three qubits to produce a sum and a carry output, further increasing computational capacity. Through the entanglement of qubits, these circuits allow for more parallelism and speed compared to classical adders. Q carry-look-ahead adders (QCLAs) and Q ripple-carry adders (QRCAs) are used to perform more complex arithmetic operations based on these half-adder and full-adder structures. Q arithmetic circuits have broad applications, boosting secure key generation in cryptography, speeding up matrix operations for faster model training in machine learning, and enabling more precise, efficient image transformations in Q image processing, benefiting fields like medical imaging and computer vision.
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.
This paper explores a novel approach to accelerating matrix multiplication by leveraging the principles of quantum state superposition. Traditional matrix multiplication is computationally intensive, particularly for large matrices, and is a core component of many scientific and engineering applications. We propose a hardware acceleration strategy that maps the matrix multiplication process onto a quantum circuit exploiting the inherent parallelism offered by quantum superposition. The core idea involves representing matrix elements as quantum states and performing the multiplication operations using controlled quantum gates. This approach aims to potentially achieve exponential speedups compared to classical algorithms. We detail the theoretical framework, outlining the quantum circuit design, and discuss the key challenges and potential improvements associated with this methodology. The primary focus is on the algorithmic design and conceptual framework rather than experimental validation, offering a foundational exploration of this concept.
It has been proven that a quantum computer is superior to an electronic com puter in solving some NP problems. Based on quantum operations, this article proposes a new quantum sum and quantum multiplication, and then the floating point quantum multiplier and quantum sum are created on the basis of fixed point number operations. These studies lay the foundation for the quantum implementation of digital filters. This article provides a new way to calculate the summator on a quantum computer. This method uses the quantum Fourier transform (QFT) and reduces the number of qubits needed to be added by eliminating the need to temporarily transfer bits. This approach also allows you to add a classical number to a quantum superposition without encoding a classical number in a quantum register. This method also allows for mass parallelization during its execution. Adding and multiplying capabilities based on QFT are improved with some changes. The proposed operations are compared with the operations of close quantum arithmetic.
Abstract - The exponential growth of large-scale data has created significant challenges in storage, processing, and optimization that classical computing systems struggle to address efficiently. Quantum Machine Learning (QML) has emerged as a promising paradigm that integrates quantum computing concepts with machine learning techniques to tackle complex big data optimization problems. By utilizing quantum features such as superposition, entanglement, and parallelism, QML algorithms can accelerate tasks like clustering, classification, and dimensionality reduction, which are traditionally computationally demanding. Recent advancements indicate strong potential for quantum-inspired optimization in fields including cybersecurity, financial portfolio management, Industry 4.0, and healthcare analytics. This paper provides a comprehensive survey of QML algorithms designed for big data optimization, examining their advantages, limitations, and application domains. Additionally, it discusses the performance trade-offs associated with hybrid quantum–classical frameworks and highlights future research directions aimed at creating secure, scalable, and resource-efficient quantum solutions for large-scale, data-driven decision-making. As data volumes continue to expand at extremely rapid rates, processing big data with traditional machine learning (ML) techniques has become increasingly challenging. ML models often struggle with issues related to computational power, efficient parameter tuning, accurate model selection, and maintaining high performance when dealing with massive datasets. Deep learning approaches—such as convolutional neural networks (CNNs)—demand substantial computational resources to train on large-scale supervised learning tasks. Moreover, the difficulty of training these networks grows significantly as the size and complexity of the datasets increase. In response to these limitations, Quantum Machine Learning (QML) has emerged as a promising research domain at the intersection of quantum computing and machine learning. Quantum computers operate fundamentally differently from classical systems, using the principles of quantum mechanics to encode and process information. As a result, quantum computational methods have the potential to address certain problems that are computationally intensive or infeasible for classical machines. Index Terms— Quantum Machine Learning (QML); Big Data Optimization; Quantum Algorithms; Hybrid Quantum-Classical Models; Quantum Computing; Data Analytics