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Novel Quantum Computer Emulator Chip.

John Robert Burger

Published Jan 1, 2004• 1 citation
OpenAlex

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Quantum machine learning (QML) is transforming the field of machine learning (ML), which typically utilizes a quantum computing such as gate model-based and annealing-based computing. Recently, Ising solver chips have been developed to emulate the superposition and entanglement properties of quantum physics using the current silicon manufacturing technologies. The chips have potential to solve complex combinatorial optimization problems (COP) with significantly low power consumption and price. This paper proposes quantum-inspired ML (QiML) framework that provides an approach to train the traditional ML models using a physics-based Ising solver chip to develop QiMLs. To validate the feasibility of the proposed framework, a quantum-inspired support vector machine (QiSVM) is developed and the results are compared with a quantum SVM trained by a commercial D-Wave quantum annealer and a conventional SVM to solve real-world problems. The proposed QiML framework will open new frontiers for advancing ML technologies for industry cyber-physical system applications.

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Quantum computer, when and for what?

Kristel F. L. Michielsen

•Jan 1, 2018•0 citations
A quantum computer has been the focus of considerable attention since it realizes a much higher operating speed than conventional computers. However, there are many important issues regarding algorithms as well as hardware in the realization of a quantum computer. To realize the calculation speed of a quantum computer, several quantum-computing emulators utilizing parallel operation using integrated circuits were fabricated. Consequently, an emulator of 75 quantum bits was realized, and the feasibility of a high-speed quantum-computing emulator using integrated circuits has been proved.
Scientists involved in building quantum computers are currently facing many physical difficulties. Creating qubits, manipulating them and reading their state requires a lot of experience. This paper describes an optical laser system as a test platform for quantum-optical control that enables effective manipulation of an emulated quantum bit. Importantly, it is a reproduction of the system controlling the optical path in real ion traps. This solution makes it possible to study the phenomena occurring in such systems and to learn about the wide range of problems that a designer and an operator of quantum systems may encounter, even before they start building them. The optical system presented in the article uses, among others, a 532 nm laser, acousto-optic modulators (AOMs), ultrafast light detectors, and a programmable FPGA chip. The entire optical system was then attached to the QUBIT emulator and thoroughly tested. The article describes the design and operation of the proposed optical system and shows an example of how to control it using a Python script.
Quantum machine learning (QML) is transforming the field of machine learning (ML), which typically utilizes a quantum computing such as gate model-based and annealing-based computing. Recently, Ising solver chips have been developed to emulate the superposition and entanglement properties of quantum physics using the current silicon manufacturing technologies. The chips have potential to solve complex combinatorial optimization problems (COP) with significantly low power consumption and price. This paper proposes quantum-inspired ML (QiML) framework that provides an approach to train the traditional ML models using a physics-based Ising solver chip to develop QiMLs. To validate the feasibility of the proposed framework, a quantum-inspired support vector machine (QiSVM) is developed and the results are compared with a quantum SVM trained by a commercial D-Wave quantum annealer and a conventional SVM to solve real-world problems. The proposed QiML framework will open new frontiers for advancing ML technologies for industry cyber-physical system applications.