scholarly journals Quantum Computing and Phase Transitions in Combinatorial Search

1996 ◽  
Vol 4 ◽  
pp. 91-128 ◽  
Author(s):  
T. Hogg
Keyword(s):  
Abrupt Change ◽  
Empirical Evaluation ◽  
Quantum Algorithm ◽  
Difficult Problem ◽  
Quantum Computers ◽  
Combinatorial Search ◽  
Problem Structure ◽  
Phase Transition Behavior ◽  
Problem Instances ◽  
Direct Use

We introduce an algorithm for combinatorial search on quantum computers that is capable of significantly concentrating amplitude into solutions for some NP search problems, on average. This is done by exploiting the same aspects of problem structure as used by classical backtrack methods to avoid unproductive search choices. This quantum algorithm is much more likely to find solutions than the simple direct use of quantum parallelism. Furthermore, empirical evaluation on small problems shows this quantum algorithm displays the same phase transition behavior, and at the same location, as seen in many previously studied classical search methods. Specifically, difficult problem instances are concentrated near the abrupt change from underconstrained to overconstrained problems.

scholarly journals Finding cliques by quantum adiabatic evolution

2002 ◽  
Vol 2 (3) ◽  
pp. 181-191
Author(s):  
A.M. Childs ◽  
E. Farhi ◽  
J. Goldstone ◽  
S. Gutmann
Keyword(s):  
Random Graph ◽  
High Probability ◽  
Numerical Study ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Connected Subgraph ◽  
Combinatorial Search ◽  
Adiabatic Evolution ◽  
General Technique ◽  
Quantum Adiabatic Evolution

Quantum adiabatic evolution provides a general technique for the solution of combinatorial search problems on quantum computers. We present the results of a numerical study of a particular application of quantum adiabatic evolution, the problem of finding the largest clique in a random graph. An n-vertex random graph has each edge included with probability 1/2, and a clique is a completely connected subgraph. There is no known classical algorithm that finds the largest clique in a random graph with high probability and runs in a time polynomial in n. For the small graphs we are able to investigate ($n \le 18$), the quantum algorithm appears to require only a quadratic run time.

scholarly journals SINGLE-STEP QUANTUM SEARCH USING PROBLEM STRUCTURE

2000 ◽  
Vol 11 (04) ◽  
pp. 739-773
Author(s):  
TAD HOGG
Keyword(s):  
Quantum Algorithms ◽  
Search Algorithms ◽  
Single Step ◽  
Quantum Computers ◽  
Quantum Search ◽  
Problem Structure ◽  
Constrained Problems ◽  
Problem Instances ◽  
Amplitude Amplification

The structure of satisfiability problems is used to improve search algorithms for quantum computers and to reduce their required coherence times by using only a single coherent evaluation of problem properties. The structure of random k-SAT allows the determination of the asymptotic average behavior of these algorithms, showing they improve on the quantum algorithms, such as amplitude amplification, that ignore detailed problem structure but remain exponential for hard problem instances. Compared to good classical methods, the algorithm performs better, on average, for weakly and highly constrained problems, but worse for hard cases. The analytic techniques introduced here apply also to other quantum algorithms, supplementing the limited evaluation possible with classical simulations, and showing how quantum computing can use the ensemble properties of NP search problems.

scholarly journals Anytime Recursive Best-First Search for Bounding Marginal MAP

2019 ◽  
Vol 33 ◽  
pp. 7924-7932
Author(s):  
Radu Marinescu ◽  
Akihiro Kishimoto ◽  
Adi Botea ◽  
Rina Dechter ◽  
Alexander Ihler
Keyword(s):  
Lower Bounds ◽  
Graphical Models ◽  
Memory Management ◽  
Empirical Evaluation ◽  
Optimal Solution ◽  
Difficult Problem ◽  
Upper And Lower Bounds ◽  
Search Spaces ◽  
Best First Search ◽  
Problem Instances

Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art solvers for this task are based on a hybrid best-first and depth-first search scheme that allows them to compute upper and lower bounds on the optimal solution value in an anytime fashion. These methods however are memory intensive schemes (via the best-first component) and do not have an efficient memory management mechanism. For this reason, they are often less effective in practice, especially on difficult problem instances with very large search spaces. In this paper, we introduce a new recursive best-first search based bounding scheme that operates efficiently within limited memory and computes anytime upper and lower bounds that improve over time. An empirical evaluation demonstrates the effectiveness of our proposed approach against current solvers.

scholarly journals Asset–liability modelling in the quantum era

2021 ◽  
Vol 26 ◽  
Author(s):  
T. Berry ◽  
J. Sharpe
Keyword(s):  
Quantum Mechanics ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Capital Requirements ◽  
Quantum Computers ◽  
Investment Management ◽  
Asset Liability Management ◽  
Liability Management ◽  
Insight Into ◽  
Current Practices

Abstract This paper introduces and demonstrates the use of quantum computers for asset–liability management (ALM). A summary of historical and current practices in ALM used by actuaries is given showing how the challenges have previously been met. We give an insight into what ALM may be like in the immediate future demonstrating how quantum computers can be used for ALM. A quantum algorithm for optimising ALM calculations is presented and tested using a quantum computer. We conclude that the discovery of the strange world of quantum mechanics has the potential to create investment management efficiencies. This in turn may lead to lower capital requirements for shareholders and lower premiums and higher insured retirement incomes for policyholders.

scholarly journals Deep neural networks for quantum circuit mapping

2021 ◽  
Author(s):  
Giovanni Acampora ◽  
Roberto Schiattarella
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Machine Learning Techniques ◽  
Quantum Computers ◽  
Physical Constraints ◽  
Proper Mapping ◽  
Correct Execution

AbstractQuantum computers have become reality thanks to the effort of some majors in developing innovative technologies that enable the usage of quantum effects in computation, so as to pave the way towards the design of efficient quantum algorithms to use in different applications domains, from finance and chemistry to artificial and computational intelligence. However, there are still some technological limitations that do not allow a correct design of quantum algorithms, compromising the achievement of the so-called quantum advantage. Specifically, a major limitation in the design of a quantum algorithm is related to its proper mapping to a specific quantum processor so that the underlying physical constraints are satisfied. This hard problem, known as circuit mapping, is a critical task to face in quantum world, and it needs to be efficiently addressed to allow quantum computers to work correctly and productively. In order to bridge above gap, this paper introduces a very first circuit mapping approach based on deep neural networks, which opens a completely new scenario in which the correct execution of quantum algorithms is supported by classical machine learning techniques. As shown in experimental section, the proposed approach speeds up current state-of-the-art mapping algorithms when used on 5-qubits IBM Q processors, maintaining suitable mapping accuracy.

scholarly journals Cryptanalysis of the system based on word problems using logarithmic signatures

Radiotekhnika ◽  
2021 ◽  
pp. 106-114
Author(s):  
Y. Kotukh ◽  
T. Okhrimenko ◽  
O. Dyachenko ◽  
N. Rotaneva ◽  
L. Kozina ◽  
...  
Keyword(s):  
Quantum Algorithms ◽  
Rapid Development ◽  
Difficult Problem ◽  
Quantum Computers ◽  
Quantum Calculations ◽  
Logarithmic Signatures ◽  
Public Key Cryptosystems ◽  
Asymmetric Cryptography ◽  
Complex Problems ◽  
Factorization Of Integers

Rapid development and advances of quantum computers are contributing to the development of public key cryptosystems based on mathematically complex or difficult problems, as the threat of using quantum algorithms to hack modern traditional cryptosystems is becoming much more real every day. It should be noted that the classical mathematically complex problems of factorization of integers and discrete logarithms are no longer considered complex for quantum calculations. Dozens of cryptosystems were considered and proposed on various complex problems of group theory in the 2000s. One of such complex problems is the problem of the word. One of the first implementations of the cryptosystem based on the word problem was proposed by Magliveras using logarithmic signatures for finite permutation groups and further proposed by Lempken et al. for asymmetric cryptography with random covers. The innovation of this idea is to extend the difficult problem of the word to a large number of groups. The article summarizes the known results of cryptanalysis of the basic structures of the cryptosystem and defines recommendations for ways to improve the cryptographic properties of structures and the use of non-commutative groups as basic structures.

Phase Transitions for Quantum Search Algorithms

2005 ◽  
Author(s):  
Tad Hogg
Keyword(s):  
Phase Transitions ◽  
Computational Cost ◽  
Search Space ◽  
Search Algorithms ◽  
Quantum Search ◽  
Combinatorial Search ◽  
Problem Structure ◽  
Search Problems ◽  
Quantum Search Algorithms ◽  
The Many

Phase transitions have long been studied empirically in various combinatorial searches and theoretically in simplified models [91, 264, 301, 490]. The analogy with statistical physics [397], explored throughout this volume, shows how the many local choices made during search relate to global properties such as the resulting search cost. These studies have led to a better understanding of typical search behaviors [514] and improved search methods [195, 247, 261, 432, 433]. Among the current research questions in this field are the range of algorithms exhibiting the transition behavior and the algorithm-independent problem properties associated with the difficult instances concentrated near the transition. Towards this end, the present chapter examines quantum computer [123, 126, 158, 486] algorithms for nondeterministic polynomial (NP) combinatorial search problems [191]. As with many conventional methods, they exhibit the easy-hard-easy pattern of computational cost as the degree of constraint in the problems varies. We describe how properties of the search space affect the algorithms and identify an additional structural property, the energy gap, motivated by one quantum algorithm but applicable to a variety of techniques, both quantum and classical. Thus, the study of quantum search algorithms not only extends the range of algorithms exhibiting phase transitions, but also helps identify underlying structural properties. Specifically, the next two sections describe a class of hard search problems and the form of quantum search algorithms proposed to date. The remainder of the chapter presents algorithm behaviors, relevant problem structure, arid an approximate asymptotic analysis of their cost scaling. The final section discusses various open issues in designing and evaluating quantum algorithms, and relating their behavior to problem structure. The k-satisfiability (k -SAT) problem, as discussed earlier in this volume, consists of n Boolean variables and m clauses. A clause is a logical OR of k variables, each of which may be negated. A solution is an assignment, that is, a value for each variable, TRUE or FALSE, satisfying all the clauses. An assignment is said to conflict with any clause it does not satisfy.

LSI-BASED EFFICIENT EMULATION OVERCOMING ALGORITHMIC RESTRICTIONS INHERENT IN QUANTUM COMPUTERS

2003 ◽  
Vol 03 (04) ◽  
pp. C9-C17
Author(s):  
MINORU FUJISHIMA
Keyword(s):  
Periodic Function ◽  
Integrated Circuit ◽  
High Speed ◽  
Large Scale ◽  
Quantum Computer ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Np Problems

Quantum computers are believed to perform high-speed calculations, compared with conventional computers. However, the quantum computer solves NP (non-deterministic polynomial) problems at a high speed only when a periodic function can be used in the process of calculation. To overcome the restrictions stemming from the quantum algorithm, we are studying the emulation by a LSI (large scale integrated circuit). In this report, first, it is explained why a periodic function is required for the algorithm of a quantum computer. Then, it is shown that the LSI emulator can solve NP problems at a high speed without using a periodic function.

Quantum Chemistry on Quantum Computers: A Polynomial-Time Quantum Algorithm for Constructing the Wave Functions of Open-Shell Molecules

2016 ◽  
Vol 120 (32) ◽  
pp. 6459-6466 ◽  
Author(s):  
Kenji Sugisaki ◽  
Satoru Yamamoto ◽  
Shigeaki Nakazawa ◽  
Kazuo Toyota ◽  
Kazunobu Sato ◽  
...  
Keyword(s):  
Quantum Chemistry ◽  
Polynomial Time ◽  
Quantum Algorithm ◽  
Open Shell ◽  
Wave Functions ◽  
Quantum Computers ◽  
Time Quantum

scholarly journals Applying Quantum Optimization Algorithms for Linear Programming

2017 ◽  
Author(s):  
Mert Side ◽  
Volkan Erol
Keyword(s):  
Resource Allocation ◽  
Linear Programming ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Quantum Optimization ◽  
Special Case

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

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