scholarly journals The Hidden Subgroup Problem for Universal Algebras

2020 ◽  
Author(s):  
Matthew Moore ◽  
Taylor Walenczyk
Keyword(s):  
Hidden Subgroup Problem ◽  
Universal Algebras ◽  
Subgroup Problem

Quantum solution to the hidden subgroup problem for Poly-Near-Hamiltonian groups

2004 ◽  
Vol 4 (3) ◽  
pp. 229-235
Author(s):  
D. Gavinsky
Keyword(s):  
Quantum Computing ◽  
Efficient Algorithm ◽  
Abelian Groups ◽  
Hidden Subgroup Problem ◽  
Subgroup Problem ◽  
Hamiltonian Groups ◽  
Quantum Solution

The Hidden Subgroup Problem (HSP) has been widely studied in the context of quantum computing and is known to be efficiently solvable for Abelian groups, yet appears to be difficult for many non-Abelian ones. An efficient algorithm for the HSP over a group \f G\ runs in time polynomial in \f{n\deq\log|G|.} For any subgroup \f H\ of \f G, let \f{N(H)} denote the normalizer of \f H. Let \MG\ denote the intersection of all normalizers in \f G (i.e., \f{\MG=\cap_{H\leq G}N(H)}). \MG\ is always a subgroup of \f G and the index \f{[G:\MG]} can be taken as a measure of ``how non-Abelian'' \f G is (\f{[G:\MG] = 1} for Abelian groups). This measure was considered by Grigni, Schulman, Vazirani and Vazirani, who showed that whenever \f{[G:\MG]\in\exp(O(\log^{1/2}n))} the corresponding HSP can be solved efficiently (under certain assumptions). We show that whenever \f{[G:\MG]\in\poly(n)} the corresponding HSP can be solved efficiently, under the same assumptions (actually, we solve a slightly more general case of the HSP and also show that some assumptions may be relaxed).

An improved query for the hidden subbroup problem

2014 ◽  
Vol 14 (5&6) ◽  
pp. 467-492
Author(s):  
Asif Shakeel
Keyword(s):  
Abelian Group ◽  
Standard Method ◽  
Finite Abelian Group ◽  
Quantum Algorithms ◽  
Success Probability ◽  
Hidden Subgroup Problem ◽  
Subgroup Identification ◽  
Subgroup Problem ◽  
Query Algorithms

The Hidden Subgroup Problem (HSP) is at the forefront of problems in quantum algorithms. In this paper, we introduce a new query, the \textit{character} query, generalizing the well-known phase kickback trick that was first used successfully to efficiently solve Deutsch's problem. An equal superposition query with $\vert 0 \rangle$ in the response register is typically used in the ``standard method" of single-query algorithms for the HSP. The proposed character query improves over this query by maximizing the success probability of subgroup identification under a uniform prior, for the HSP in which the oracle functions take values in a finite abelian group. We apply our results to the case when the subgroups are drawn from a set of conjugate subgroups and obtain a success probability greater than that found by Moore and Russell.

Quantum measurements for hidden subgroup problems with optimal sample complexity

2008 ◽  
Vol 8 (3&4) ◽  
pp. 345-358
Author(s):  
M. Hayashi ◽  
A. Kawachi ◽  
H. Kobayashi
Keyword(s):  
Log P ◽  
Sample Complexity ◽  
Arbitrary Group ◽  
Hidden Subgroup Problem ◽  
Optimal Sample ◽  
Subgroup Problem ◽  
Good Measurement ◽  
Hidden Subgroups ◽  
Decision Version ◽  
Candidate Set

One of the central issues in the hidden subgroup problem is to bound the sample complexity, i.e., the number of identical samples of coset states sufficient and necessary to solve the problem. In this paper, we present general bounds for the sample complexity of the identification and decision versions of the hidden subgroup problem. As a consequence of the bounds, we show that the sample complexity for both of the decision and identification versions is $\Theta(\log|\HH|/\log p)$ for a candidate set $\HH$ of hidden subgroups in the case \REVISE{where the candidate nontrivial subgroups} have the same prime order $p$, which implies that the decision version is at least as hard as the identification version in this case. In particular, it does so for the important \REVISE{cases} such as the dihedral and the symmetric hidden subgroup problems. Moreover, the upper bound of the identification is attained \REVISE{by a variant of the pretty good measurement}. \REVISE{This implies that the concept of the pretty good measurement is quite useful for identification of hidden subgroups over an arbitrary group with optimal sample complexity}.

scholarly journals On solving systems of random linear disequations

2008 ◽  
Vol 8 (6&7) ◽  
pp. 579-594
Author(s):  
G. Ivanyos
Keyword(s):  
Abelian Groups ◽  
Main Idea ◽  
Constant Factor ◽  
Linear Functions ◽  
Important Special Case ◽  
Hidden Subgroup Problem ◽  
Left Hand ◽  
Shift Problem ◽  
Group A ◽  
Subgroup Problem

An important special case of the hidden subgroup problem is equivalent to the hidden shift problem over abelian groups. An efficient solution to the latter problem could serve as a building block of quantum hidden subgroup algorithms over solvable groups. The main idea of a promising approach to the hidden shift problem is a reduction to solving systems of certain random disequations in finite abelian groups. By a disequation we mean a constraint of the form $f(x)\neq 0$. In our case, the functions on the left hand side are generalizations of linear functions. The input is a random sample of functions according to a distribution which is up to a constant factor uniform over the "linear" functions $f$ such that $f(u)\neq 0$ for a fixed, although unknown element $u\in A$. The goal is to find $u$, or, more precisely, all the elements $u'\in A$ satisfying the same disequations as $u$. In this paper we give a classical probabilistic algorithm which solves the problem in an abelian $p$-group $A$ in time polynomial in the sample size $N$, where $N=(\log\size{A})^{O(q^2)}$, and $q$ is the exponent of $A$.

scholarly journals Random Measurement Bases, Quantum State Distinction and Applications to the Hidden Subgroup Problem

Algorithmica ◽  
2008 ◽  
Vol 55 (3) ◽  
pp. 490-516 ◽  
Author(s):  
Jaikumar Radhakrishnan ◽  
Martin Rötteler ◽  
Pranab Sen
Keyword(s):  
Quantum State ◽  
Hidden Subgroup Problem ◽  
Random Measurement ◽  
Subgroup Problem ◽  
Measurement Bases

scholarly journals For distinguishing conjugate Hidden subgroups, the pretty good measurement is as good as it gets

2007 ◽  
Vol 7 (8) ◽  
pp. 752-765
Author(s):  
C. Moore ◽  
A. Russell
Keyword(s):  
Dihedral Group ◽  
Hidden Subgroup Problem ◽  
Parent Group ◽  
Optimal Measurement ◽  
Subgroup Problem ◽  
Good Measurement ◽  
Hidden Subgroups

Recently Bacon, Childs and van Dam showed that the ``pretty good measurement'' (PGM) is optimal for the Hidden Subgroup Problem on the dihedral group $D_n$ in the case where the hidden subgroup is chosen uniformly from the $n$ involutions. We show that, for any group and any subgroup $H$, the PGM is the optimal one-register experiment in the case where the hidden subgroup is a uniformly random conjugate of $H$. We go on to show that when $H$ forms a Gel'fand pair with its parent group, the PGM is the optimal measurement for any number of registers. In both cases we bound the probability that the optimal measurement succeeds. This generalizes the case of the dihedral group, and includes a number of other examples of interest.

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