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arXiv:quant-ph/9802028

Analogue Quantum Computers for Data Analysis

Alexander Yu. Vlasov

Published Feb 11, 1998• 2 citations
arXivHTMLPDF

Abstract

Analogue computers use continuous properties of physical system for modeling. In the paper is described possibility of modeling by analogue quantum computers for some model of data analysis. It is analogue associative memory and a formal neural network. A particularity of the models is combination of continuous internal processes with discrete set of output states. The modeling of the system by classical analogue computers was offered long times ago, but now it is not very effectively in comparison with modern digital computers. The application of quantum analogue modelling looks quite possible for modern level of technology and it may be more effective than digital one, because number of element may be about Avogadro number (N=6.0E23).

DOI
10.48550/arxiv.quant-ph/9802028

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Analogue computers use continuous properties of physical system for modeling. In the paper is described possibility of modeling by analogue quantum computers for some model of data analysis. It is analogue associative memory and a formal neural network. A particularity of the models is combination of continuous internal processes with discrete set of output states. The modeling of the system by classical analogue computers was offered long times ago, but now it is not very effectively in comparison with modern digital computers. The application of quantum analogue modelling looks quite possible for modern level of technology and it may be more effective than digital one, because number of element may be about Avogadro number (N=6.0E23).
"Quantum" processors' structure for simulation and verification of digital system is proposed. It is based on transactions between addressable memory components to realize any functionality. The new approach for analysis of digital systems is proposed. It is supposed to apply vector form (quantum) of combinational and sequential structures implemented into memory elements. This approach remarkably differs from the common design theory of discrete devices based on logic truth tables of components. It is based on an opportunity of applying quantum or qubit data structures in modern computers when doing calculating processes with the purpose of unary coding states of input, internal and output variables, and also on the technology of qubit-vectors implementation into memory elements FPGA, which realize combinational and sequential primitives. The use of quantum memory-based only models for describing digital components when designing computer systems would allow us to increase yield, enhance reliability of computers, make the process of design and production of devices cheaper, and also provides human-free repairing in remote & online mode.
Nature is the relations between processes and phenomena. Nothing exists in a Universe without relations. Computing is the transaction of relations between data using control and execution mechanisms. Quantum relations are a superposition of particles and their states. Quantum computing is unconditional transactions of relations between qubit data. Entanglement is a nonlocal superposition of deterministic states. A quantum computer is an analog device for solving combinatorial problems in parallel. Practically focused definitions of the concepts of quantum computing represent a path to the development of scalable quantum parallel algorithms for solving combinatorial problems. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer uses previously created primitives-constructs to build an always non-optimal calculator. The deterministic paradigm of creating quantum computing by using photon transactions on the electrons of an atom can exclude the use of quantum logic. The evolutionary path of quantum computing from the classical one: Memory-Address-Transaction (MAT) →\rightarrow→ Electron-Address-Transaction →\rightarrow→Electron-Address-Quantaction (EAQ)→\rightarrow→State-Superposition- Logic. What is calculated on a quantum computer can be calculated in parallel on a classical computer due to the redundancy of memory. MAT-computing implements any algorithms through transactions (read-write) in memory. Qubit-vector models for describing functionalities differ from the known truth tables in terms of compactness of description and technological implementation of parallel algorithms for the synthesis and analysis of digital devices and SoC-components. An example is given – a memory-driven algorithm for simulating digital devices based on qubit-vector forms of describing functionalities for a significant increase in the productivity of computing processes by parallel execution of logical operations. Comparative evaluations of the use of qubit models and methods show an increase in the efficiency of algorithms for simulating digital devices in comparison with tabular ones. The superposition of classical, quantum and analog computing is integrally presented, which makes it possible to search the best solutions for recognition and decision making.
Recently, there has been a rapid increase interest in quantum computers. Their work is based on the use of quantum-mechanical phenomena such as superposition and entanglement for computing to transform input data into outputs, which can actually provide effective performance 3-4 orders of magnitude higher than any modern computing devices. In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects for computations. Currently, there are prototypes of devices of this class, but it is not yet possible to achieve an effective solution to the planned tasks with their help due to a number of difficulties in implementation. However, there is a mathematical apparatus that describes the behavior of quantum particles using a wave function. Hence, it becomes possible to simulate quantum computations on the classical architecture and check the reliability of algorithms belonging to the NP class, which are theoretically executed on a quantum computer in polynomial time and which cannot be solved on classical computers within an acceptable time frame. Effectively simulate the operation of a quantum system on devices with classical architecture is impossible, it remains to propose methods for acceleration of quantum computing on classical systems.
The work is aimed at analyzing the potential advantages of using quantum approaches in applied problems of artificial intelligence. In this paper, the task of classifying medical images extracted from histopathological images of sections of lymph nodes is set. The theoretical basis used for the construction of quantum and hybrid-quantum computing elements used in the article will be given. Quantum analogues of classical machine learning algorithms and neural networks will be considered. The paper will give a step-by-step description of the data transformation, the construction of models and their training, followed by an analysis of the results obtained and the performance of the simulation of quantum computing.
We briefly review what a quantum computer is, what it promises to do for us and why it is so hard to build one. Among the first applications anticipated to bear fruit is the quantum simulation of quantum systems. While most quantum computation is an extension of classical digital computation, quantum simulation differs fundamentally in how the data are encoded in the quantum computer. To perform a quantum simulation, the Hilbert space of the system to be simulated is mapped directly onto the Hilbert space of the (logical) qubits in the quantum computer. This type of direct correspondence is how data are encoded in a classical analogue computer. There is no binary encoding, and increasing precision becomes exponentially costly: an extra bit of precision doubles the size of the computer. This has important consequences for both the precision and error-correction requirements of quantum simulation, and significant open questions remain about its practicality. It also means that the quantum version of analogue computers, continuous-variable quantum computers, becomes an equally efficient architecture for quantum simulation. Lessons from past use of classical analogue computers can help us to build better quantum simulators in future.
"Quantum " data structures for synthesis of digital system are proposed. They are based on transactions between addressable memory components to implement any functionality. The new approach of logic function minimization for synthesis of digital systems is proposed. It is supposed to apply vector form (quantum) of description logic and sequential structures implemented into memory elements. This approach remarkably differs from the common synthesis theory of discrete devices based on truth tables of components. It is based on an opportunity of applying quantum or qubit data structures [1-5] in modern computers when doing calculating processes with the purpose of unary coding states of input, internal and output variables, and also on the technology of qubit-vectors implementation into memory elements FPGA, which realize combinational and sequential primitives. The use of quantum memory-based only models for describing digital components when computer systems design would allow us to increase yield, enhance reliability of computers, make the process of design and production of devices cheaper, and also provides human-free repairing in remote & online mode.