Dust Theory Vol. 8: Gradient Quantum Computing: A Native Quantum Paradigm and Hardware Architecture Based on Gradient-Relational Ontology (Gradient Cosmos Research Series)
All existing quantum computing paradigms take substantial qubits as basic units and realize state evolution through quantum gate operations. Essentially, they simulate quantum effects within the framework of classical architecture, and persistently face three core bottlenecks: extreme decoherence sensitivity, huge error correction overhead, and extremely high scaling difficulty. The root cause lies in the failure to grasp the relational essence of quantum phenomena at the ontological level. Based on the path-rule duality principle of Gradient-Relational Ontology and the path-state essence of quantum states, this paper proposes gradient quantum computing as a native quantum computing paradigm. The basic unit of computation is not binary qubits, but multi-dimensional parallel conductive gradient paths; the computing process is not the sequential operation of quantum gates, but the self-organized evolution of path superposition, interference and condensation; measurement is not a special operation triggering collapse, but the spontaneous condensation of paths into rule states. The study demonstrates that this paradigm possesses endogenous decoherence suppression and native error correction capabilities. It can maintain computational stability through structural steady states without external quantum error correction codes. Its parallelism grows exponentially with path scale, and the scaling bottleneck is far lower than that of traditional qubit architectures. This paper further presents a four-layer hardware system architecture for gradient quantum computers, clarifies the core technical path of physical implementation, and proves its natural computational advantages in combinatorial optimization, quantum simulation and other problems. This study extends relational ontology to the field of quantum computing, providing a brand-new ontological path and architectural scheme for breaking through the engineering bottlenecks of existing quantum computing and realizing large-scale general-purpose quantum computing.
All existing quantum computing paradigms take substantial qubits as basic units and realize state evolution through quantum gate operations. Essentially, they simulate quantum effects within the framework of classical architecture, and persistently face three core bottlenecks: extreme decoherence sensitivity, huge error correction overhead, and extremely high scaling difficulty. The root cause lies in the failure to grasp the relational essence of quantum phenomena at the ontological level. Based on the path-rule duality principle of Gradient-Relational Ontology and the path-state essence of quantum states, this paper proposes gradient quantum computing as a native quantum computing paradigm. The basic unit of computation is not binary qubits, but multi-dimensional parallel conductive gradient paths; the computing process is not the sequential operation of quantum gates, but the self-organized evolution of path superposition, interference and condensation; measurement is not a special operation triggering collapse, but the spontaneous condensation of paths into rule states. The study demonstrates that this paradigm possesses endogenous decoherence suppression and native error correction capabilities. It can maintain computational stability through structural steady states without external quantum error correction codes. Its parallelism grows exponentially with path scale, and the scaling bottleneck is far lower than that of traditional qubit architectures. This paper further presents a four-layer hardware system architecture for gradient quantum computers, clarifies the core technical path of physical implementation, and proves its natural computational advantages in combinatorial optimization, quantum simulation and other problems. This study extends relational ontology to the field of quantum computing, providing a brand-new ontological path and architectural scheme for breaking through the engineering bottlenecks of existing quantum computing and realizing large-scale general-purpose quantum computing.
This paper presents a comprehensive review of quantum computing and the technological foundations of quantum processors. Quantum computing represents a transformative computational paradigm that leverages the fundamental principles of quantum mechanics—such as superposition, entanglement, and quantum interference—to solve complex problems that are intractable for classical computers. At the core of this paradigm lies the quantum processor, a specialized hardware system designed to initialize, control, and measure quantum bits (qubits) through precise gate operations and coherent interactions. The survey examines the essential theoretical concepts underlying quantum computation and provides a structured comparison of leading quantum processor platforms. Prominent architectures, including superconducting circuits, trapped-ion systems, and photonic implementations, are analyzed in terms of their physical mechanisms, scalability, coherence properties, and engineering challenges. Each platform offers distinct advantages and faces specific technical limitations related to noise, error correction, and fabrication complexity. In addition to reviewing hardware developments, this study highlights key obstacles in advancing quantum processors toward large-scale, fault-tolerant systems. Issues such as qubit stability, error mitigation, thermal management, and system integration are discussed to present a realistic perspective on current progress. Performance indicators including gate fidelity, coherence time, connectivity, and scalability are used to compare architectural approaches. The objective of this paper is to provide a clear and organized synthesis of recent advancements in quantum computing hardware, thereby offering researchers and practitioners a foundational reference to support continued innovation and development in this rapidly evolving field.
Quantum computing may solve selected classes of problems more efficiently than classical computers, with applications in areas such as cryptography, chemistry, and optimization. However, current quantum systems remain limited in scale and reliability and depend on substantial classical-computing support. This thesis investigates this challenge in two complementary contexts: classical simulation, where efficient computation enables the exploration of larger quantum systems; and quantum error correction, where efficient real-time decoding enables more reliable quantum operation.The first part examines the classical simulation of quantum computers, focusing on the computational and memory challenges that limit its scalability. It organizes the field across device, gate, and algorithmic levels, with particular attention to state-vector, path-based, hybrid, and tensor-based methods. The survey relates their computation, memory, data-movement, and communication bottlenecks to acceleration approaches for CPUs, GPUs, FPGAs, and distributed systems. It further highlights recurring optimization strategies, including reduced precision, data compression, parallel execution, improved memory locality, specialized matrix units, and reconfigurable hardware, providing a common framework for current techniques and future acceleration opportunities.The second part addresses the real-time classical processing required for quantum error correction. Starting from a high-accuracy Graph Neural Network surface-code decoder, it applies algorithm-hardware co-design through hardware-guided pruning and retraining, input-graph filtering, quantization, and a custom FPGA architecture. For code distances up to d=7, the resulting designs satisfy both max-latency and average-latency decoding requirements while reducing the logical error rate by 13% and 40%, respectively, compared with the evaluated Minimum Weight Perfect Matching baseline.Together, these works show how classical computer-architecture techniques can support quantum computing from scalable exploration through simulation to reliable operation through real-time error correction.
Quantum computing has emerged as a paradigm-shifting computational model capable of addressing problems intractable for classical systems. This chapter explores the foundational principles, hardware implementations, and algorithmic approaches that define the field of quantum computing. Key quantum mechanical phenomena – such as superposition, entanglement, and interference – are examined in relation to their computational applications. The discussion extends to qubit realization technologies including superconducting circuits, trapped ions, photonic systems, and emerging platforms like topological qubits. Core quantum algorithms, including Shor’s and Grover’s, are analyzed to demonstrate their operational mechanics and performance advantages. The chapter also presents current challenges in quantum error correction, coherence preservation, and hardware scalability, particularly in the context of noisy intermediate-scale quantum devices. Further, it highlights applications across domains such as cryptography, optimization, quantum chemistry, and machine learning. The content is supported by illustrative diagrams, comparative tables, and up-to-date research references, providing a comprehensive overview of the state of quantum computing and its future directions.
The rapid development of computing hardware has been driven by an ever-emerging need for high throughput, scalable performance, and computation capabilities to be able to address increasingly complex problems. The paradigm of classical computing, centered on deterministic binary logic and the von Neumann architecture, has long favored modern information processing and still supports a wide range of applications. However, physical limits in scaling transistors, power dissipation, and slowing down Moore's Law have stimulated the consideration of alternative computing paradigms. Quantum computing is now an emerging means that exploits the basic principles of quantum mechanics-such as superposition, entanglement, and quantum interference-to enable new forms of computation. This review study discusses a comparison between the classical and quantum computing systems in operational paradigms, architecture structures, performance characteristics, and application domains. This work is supported by a systematic review of the established theories, currently realized hardware implementations, and representative algorithms. The analysis underlines that classical systems remain very reliable, scalable, and efficient in general-purpose and deterministic workloads, while quantum systems ensure essential advantages in specified problem classes, such as cryptography, quantum chemistry, combinatorial optimization, and selected machine learning tasks. The study concludes that classical and quantum computing are best viewed as complementary technologies. Future high-performance computing platforms will most likely be based on hybrid-classical–quantum architectures in which quantum processors serve as specialized accelerators to help classical systems solve new computational challenges.
Quantum computing represents a fundamental shift in computational paradigms, moving beyond classical de terministic architectures toward probabilistic systems governed by the principles of quantum mechanics. This paper presents a comprehensive investigation into the theoretical foundations, mathematical frameworks, hard ware architectures, and global strategic developments shaping the field of quantum information science. Core quantum phenomena—including superposition, entanglement, interference, and quantum tunneling—are exam ined as the foundational mechanisms enabling exponential computational capabilities within high-dimensional Hilbert spaces. The study further explores the mathematical formalism underlying quantum computation, including Dirac no tation, tensor products, and unitary transformations, alongside the operational role of quantum gates and circuit models. Breakthrough algorithms such as Shor’s and Grover’s are analyzed to highlight their disruptive implica tions for cryptography and computational complexity. In parallel, emerging hybrid algorithms suitable for Noisy Intermediate-Scale Quantum (NISQ) devices are discussed. A critical focus is placed on the engineering challenges associated with building scalable and fault-tolerant quantum systems, including decoherence, error rates, and quantum error correction techniques. The paper also surveys leading hardware implementations—superconducting circuits, trapped ions, neutral atoms, and photonic systems—evaluating their advantages and limitations. Finally, the work contextualizes quantum computing within a global strategic landscape, emphasizing national initiatives and regional innovation ecosystems driving techno logical advancement. This study concludes that quantum computing is rapidly transitioning from theoretical exploration to practical realization, with the potential to revolutionize domains such as cryptography, materials science, medicine, and op timization. As the field enters its engineering era, achieving reliable quantum advantage will mark a transformative milestone in the evolution of computation and scientific discovery.
ABSTRACT Quantum computing represents a fundamental shift in the way computational problems are approached and solved. Unlike classical computing systems that rely on binary bits to process information, quantum computing utilizes quantum bits, or qubits, which exploit principles of quantum mechanics such as superposition and entanglement. This transition from classical bits to qubits has opened new possibilities for solving complex problems that are computationally infeasible for traditional systems. This paper explores the evolution of computing paradigms from classical architectures to quantum-based models, highlighting the theoretical foundations and technological advancements that have driven this transformation. It discusses the core concepts of quantum computing, including qubit representation, quantum gates, and computational models, along with an overview of current quantum hardware architectures. Furthermore, the paper examines key application areas such as cryptography, optimization, artificial intelligence, and scientific simulations, where quantum computing demonstrates significant potential. Despite its promise, quantum computing faces substantial challenges related to hardware stability, error correction, scalability, and resource requirements, which are also analyzed. Finally, the paper outlines future research directions and the potential impact of quantum computing on next-generation information processing systems, emphasizing its role in redefining the boundaries of modern computation. Keywords : Quantum Computing, Qubits, Quantum Algorithms, Quantum Applications, Future Computing Technologies