Probabilistic Prime Factorization based on Virtually Connected Boltzmann Machine and Probabilistic Annealing
Probabilistic computing has been introduced to operate functional networks using a probabilistic bit (p-bit), generating 0 or 1 probabilistically from its electrical input. In contrast to quantum computers, probabilistic computing enables the operation of adiabatic algorithms even at room temperatur...
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Main Authors | , , , , , , |
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Format | Journal Article |
Language | English |
Published |
26.10.2022
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Subjects | |
Online Access | Get full text |
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Summary: | Probabilistic computing has been introduced to operate functional networks
using a probabilistic bit (p-bit), generating 0 or 1 probabilistically from its
electrical input. In contrast to quantum computers, probabilistic computing
enables the operation of adiabatic algorithms even at room temperature, and is
expected to broaden computational abilities in non-deterministic polynomial
searching and learning problems. However, previous developments of
probabilistic machines have focused on emulating the operation of quantum
computers similarly, implementing every p-bit with large weight-sum matrix
multiplication blocks or requiring tens of times more p-bits than semiprime
bits. Furthermore, previous probabilistic machines adopted the graph model of
quantum computers for updating the hardware connections, which further
increased the number of sampling operations. Here we introduce a digitally
accelerated prime factorization machine with a virtually connected Boltzmann
machine and probabilistic annealing method, designed to reduce the complexity
and number of sampling operations to below those of previous probabilistic
factorization machines. In 10-bit to 64-bit factorizations were performed to
assess the effectiveness of the machine, and the machine offers 1.2 X 10^8
times improvement in the number of sampling operations compared with previous
factorization machines, with a 22-fold smaller hardware resource. This work
shows that probabilistic machines can be implemented in a cost-effective manner
using a field-programmable gate array, and hence we suggest that probabilistic
computers can be employed for solving various large NP searching problems in
the near future. |
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DOI: | 10.48550/arxiv.2210.14519 |