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IEEE International Symposium on Circuits and Systems proceedings

On-Chit High-Speed Solver of Inverse Problems Based on Quantum-Computing Principle

Minoru Fujishima, Masahiro Shimura

Published Sep 22, 2006• 0 citations
Publisher

Abstract

A high-speed solver of an inverse problem to a oneway function becomes important although the problems become complicated according to the change in social situation. However, a chip multiprocessor dedicated to the inverse problem to the one-way function is nonexistent although a single-chip processor consuming limited power is useful in realizing a high-speed solver. In this paper, we propose a new chip multiprocessor operating with a procedure similar to that of quantum computing. The chip multiprocessor is implemented on a field programmable gate array (FPGA), and the factorization of a 64-bit integer is demonstrated. As a result, the proposed processor reduces the calculation time by 35% compared with a general-purpose processor with 3.4GHz clock frequency. As a result, it is shown that the proposed processor solves inverse problems such as factorization and discrete logarithm problems at higher speed than a general-purpose processor consuming limited power.

DOI
10.1109/iscas.2006.1693170

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A high-speed solver of an inverse problem to a oneway function becomes important although the problems become complicated according to the change in social situation. However, a chip multiprocessor dedicated to the inverse problem to the one-way function is nonexistent although a single-chip processor consuming limited power is useful in realizing a high-speed solver. In this paper, we propose a new chip multiprocessor operating with a procedure similar to that of quantum computing. The chip multiprocessor is implemented on a field programmable gate array (FPGA), and the factorization of a 64-bit integer is demonstrated. As a result, the proposed processor reduces the calculation time by 35% compared with a general-purpose processor with 3.4GHz clock frequency. As a result, it is shown that the proposed processor solves inverse problems such as factorization and discrete logarithm problems at higher speed than a general-purpose processor consuming limited power.
A high-speed and large-scale processor dedicated to quantum computing is proposed, which has the minimum operation function needed for execution of a quantum algorithm. In this processor, the probability amplitudes determined by the states of the quantum bits in the quantum computer are described using only one bit, and we named it a logic quantum processor. It is found that for this processor an execution speed 275 times higher than that of a software simulation is obtained when the processor executing the quantum algorithm for 16 qubit has been implemented in a FPGA. As an example application, it is shown that the processor can solve the satisfiability problem and that it can also simulate the decoherence in the quantum computer at high speed.
A 16-qubit quantum-computing emulator executing quantum algorithms at high speed is proposed. By limiting the command of the quantum computing emulator to the minimum function required with the quantum algorithm, probability amplitudes are expressed with one bit and the functions of many qubits are integrated on a field programmable gate array (FPGA). As a result of implementing the emulator in the FPGA, it is found that the emulator has a 250 times higher-speed as compared with a software simulation by a general-purpose processor.
Quantum computers are believed to perform high-speed calculations, compared with conventional computers. However, the quantum computer has inherent issues. Firstly, it solves NP (non-deterministic polynomial) problems at a high speed only when a periodic function can be used in the process of calculation. Secondly, it is extremely difficult to increase the problem scale to be solved, which is determined by the number of quantum bits. To overcome the restrictions stemming from the quantum algorithm, we are studying the emulation by a FPGA (field programmable gate array). In this paper, first, it is explained why a periodic function is required for the algorithm of a quantum computer. Then, it is shown that the hardware emulator can solve NP problems at a high speed without using a periodic function.
A quantum computer is highly promising and widely studied since it enables a much higher calculation speed than current computers. Due to physical limitations, however, the current quantum computer can only solve much smaller scale problems than conventional computers. In order to process a large-scale problem at high speed, we have been studying a quantum computing emulator utilizing semiconductor memories. As a result, we have realized a dedicated processor that solves search problems, such as the satisfiability problem, at much higher speed than current computers. Consequently, without using "quantum process," the possibility of a quantum computer executing a large-scale problem is shown.
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.
A quantum machine may solve some complex problems that are intractable for even the most powerful classical computers. By exploiting quantum superposition and entanglement phenomena, quantum algorithms can achieve from polynomial to exponential speed up when compared to their best classical counterparts. A quantum computer will be a part of a heterogeneous, multi-core computer in which a classical processor will interact with several accelerators such as FPGAs, GPUs and also a quantum co-processor. Figure 1 shows the different layers of the quantum computer system stack [1]. Building such a quantum system requires contributions from different fields such as physics, electronics, computer science and computer engineering for addressing the following challenges: i) build scalable quantum chips integrating qubits with long coherence times and high-fidelity operations, ii) develop classical control electronics at possibly cryogenic temperatures and iii) create the microarchitecture as well as the software for quantum computation.