scholarly journals Quantum speed-up in global optimization of binary neural nets

Author(s):  
Yidong Liao ◽  
Daniel Ebler ◽  
Feiyang Liu ◽  
Oscar Dahlsten
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentin Gebhart ◽  
Luca Pezzè ◽  
Augusto Smerzi

AbstractDespite intensive research, the physical origin of the speed-up offered by quantum algorithms remains mysterious. No general physical quantity, like, for instance, entanglement, can be singled out as the essential useful resource. Here we report a close connection between the trace speed and the quantum speed-up in Grover’s search algorithm implemented with pure and pseudo-pure states. For a noiseless algorithm, we find a one-to-one correspondence between the quantum speed-up and the polarization of the pseudo-pure state, which can be connected to a wide class of quantum statistical speeds. For time-dependent partial depolarization and for interrupted Grover searches, the speed-up is specifically bounded by the maximal trace speed that occurs during the algorithm operations. Our results quantify the quantum speed-up with a physical resource that is experimentally measurable and related to multipartite entanglement and quantum coherence.


Author(s):  
Yosi Ben-Asher ◽  
Esti Stein ◽  
Vladislav Tartakovsky

Pass transistor logic (PTL) is a circuit design technique wherein transistors are used as switches. The reconfigurable mesh (RM) is a model that exploits the power of PTLs signal switching, by enabling flexible bus connections in a grid of processing elements containing switches. RM algorithms have theoretical results proving that [Formula: see text] can speed up computations significantly. However, the RM assumes that the latency of broadcasting a signal through [Formula: see text] switches (bus length) is 1. This is an unrealistic assumption preventing physical realizations of the RM. We propose the restricted-RM (RRM) wherein the bus lengths are restricted to [Formula: see text], [Formula: see text]. We show that counting the number of 1-bits in an input of [Formula: see text] bits can be done in [Formula: see text] steps for [Formula: see text] by an [Formula: see text] RRM. An almost matching lower bound is presented, using a technique which adds to the few existing lower-bound techniques in this area. Finally, the algorithm was directly coded over an FPGA, outperforming an optimal tree of adders. This work presents an alternative way of counting, which is fundamental for summing, beating regular Boolean circuits for large numbers, where summing a vast amount of numbers is the basis of any accelerator in embedded systems such as neural-nets and streaming. a


2021 ◽  
Vol 67 (1) ◽  
pp. 241-252
Author(s):  
Wenbin Yu ◽  
Hao Feng ◽  
Yinsong Xu ◽  
Na Yin ◽  
Yadang Chen ◽  
...  

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
G. G. Guerreschi ◽  
A. Y. Matsuura
Keyword(s):  
Speed Up ◽  

2019 ◽  
Vol 18 (4) ◽  
Author(s):  
Sebastián Alberto Grillo ◽  
Franklin de Lima Marquezino
Keyword(s):  
Speed Up ◽  

1994 ◽  
Vol 10 (1) ◽  
pp. 64-95 ◽  
Author(s):  
R. Shonkwiler ◽  
Erik Van Vleck

2020 ◽  
Vol 53 (13) ◽  
pp. 135502
Author(s):  
Hong-Mei Zou ◽  
Jianhe Yang ◽  
Danping Lin ◽  
Mao-Fa Fang
Keyword(s):  
Speed Up ◽  

2021 ◽  
Vol 24 (3) ◽  
pp. 207-221
Author(s):  
Kamil Khadiev ◽  
Vladislav Remidovskii

We study algorithms for solving the problem of assembling a text (long string) from a dictionary (a sequence of small strings). The problem has an application in bioinformatics and has a connection with the sequence assembly method for reconstructing a long deoxyribonucleic-acid (DNA) sequence from small fragments. The problem is assembling a string t of length n from strings s1,...,sm. Firstly, we provide a classical (randomized) algorithm with running time Õ(nL0.5 + L) where L is the sum of lengths of s1,...,sm. Secondly, we provide a quantum algorithm with running time Õ(nL0.25 + √mL). Thirdly, we show the lower bound for a classical (randomized or deterministic) algorithm that is Ω(n+L). So, we obtain the quadratic quantum speed-up with respect to the parameter L; and our quantum algorithm have smaller running time comparing to any classical (randomized or deterministic) algorithm in the case of non-constant length of strings in the dictionary.


1991 ◽  
Vol 3 (3) ◽  
pp. 418-427 ◽  
Author(s):  
Khalid A. Al-Mashouq ◽  
Irving S. Reed

The aim of a neural net is to partition the data space into near optimal decision regions. Learning such a partitioning solely from examples has proven to be a very hard problem (Blum and Rivest 1988; Judd 1988). To remedy this, we use the idea of supplying hints to the network—as discussed by Abu-Mostafa (1990). Hints reduce the solution space, and as a consequence speed up the learning process. The minimum Hamming distance between the patterns serves as the hint. Next, it is shown how to learn such a hint and how to incorporate it into the learning algorithm. Modifications in the net structure and its operation are suggested, which allow for a better generalization. The sensitivity to errors in such a hint is studied through some simulations.


2015 ◽  
Vol 91 (3) ◽  
Author(s):  
Ying-Jie Zhang ◽  
Wei Han ◽  
Yun-Jie Xia ◽  
Jun-Peng Cao ◽  
Heng Fan
Keyword(s):  
Speed Up ◽  

Sign in / Sign up

Export Citation Format

Share Document