Quantum-based algorithm and circuit design for bounded Knapsack optimization problem

2020 ◽  
Vol 20 (9&10) ◽  
pp. 766-786
Author(s):  
Wenjun Hou ◽  
Marek Perkowski
Keyword(s):  
Circuit Design ◽  
Knapsack Problem ◽  
Decision Problem ◽  
Optimization Problem ◽  
Quantum Algorithm ◽  
Quantum Circuits ◽  
Local Quantum ◽  
Speed Up ◽  
Grover Search ◽  
Quantum Simulator

The Knapsack Problem is a prominent problem that is used in resource allocation and cryptography. This paper presents an oracle and a circuit design that verifies solutions to the decision problem form of the Bounded Knapsack Problem. This oracle can be used by Grover Search to solve the optimization problem form of the Bounded Knapsack Problem. This algorithm leverages the quadratic speed-up offered by Grover Search to achieve a quantum algorithm for the Knapsack Problem that shows improvement with regard to classical algorithms. The quantum circuits were designed using the Microsoft Q# Programming Language and verified on its local quantum simulator. The paper also provides analyses of the complexity and gate cost of the proposed oracle. The work in this paper is the first such proposed method for the Knapsack Optimization Problem.

scholarly journals Qulacs: a fast and versatile quantum circuit simulator for research purpose

Quantum ◽  
2021 ◽  
Vol 5 ◽  
pp. 559
Author(s):  
Yasunari Suzuki ◽  
Yoshiaki Kawase ◽  
Yuya Masumura ◽  
Yuria Hiraga ◽  
Masahiro Nakadai ◽  
...  
Keyword(s):  
Fault Tolerant ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Research Purpose ◽  
Numerical Techniques ◽  
Circuit Simulator ◽  
Speed Up ◽  
Near Term

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via examples, describe numerical techniques to speed-up simulation, and demonstrate its performance with numerical benchmarks.

scholarly journals Grover on PIPO

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1194
Author(s):  
Kyungbae Jang ◽  
Gyeongju Song ◽  
Hyeokdong Kwon ◽  
Siwoo Uhm ◽  
Hyunji Kim ◽  
...  
Keyword(s):  
Block Cipher ◽  
Search Algorithm ◽  
Quantum Algorithms ◽  
Optimization Techniques ◽  
Quantum Circuits ◽  
Brute Force ◽  
Grover Search ◽  
Quantum Simulator ◽  
Compact Design ◽  
Grover Search Algorithm

The emergence of quantum computers is threatening the security of cryptography through various quantum algorithms. Among them, the Grover search algorithm is known to be efficient in accelerating brute force attacks on block cipher algorithms. To utilize the Grover’s algorithm for brute force attacks, block ciphers must be implemented in quantum circuits. In this paper, we present optimized quantum circuits of the SPN (Substitution Permutation Network) structured lightweight block cipher, namely the PIPO block cipher. In particular, the compact design of quantum circuits for the 8-bit Sbox is investigated. These optimization techniques are used to implement other cryptographic operations as quantum circuits. Finally, we evaluate quantum resources of Grover search algorithm for the PIPO block cipher in ProejctQ, a quantum simulator provided by IBM.

scholarly journals Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Nikolai Krivulin
Keyword(s):  
Decision Problem ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Sufficient Conditions ◽  
Approximation Error ◽  
Optimal Solution ◽  
Algebraic Solution ◽  
Pairwise Comparisons ◽  
Logarithmic Scale ◽  
Idempotent Algebra

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.

scholarly journals Efficient Implementation of PRESENT and GIFT on Quantum Computers

2021 ◽  
Vol 11 (11) ◽  
pp. 4776
Author(s):  
Kyungbae Jang ◽  
Gyeongju Song ◽  
Hyunjun Kim ◽  
Hyeokdong Kwon ◽  
Hyunji Kim ◽  
...  
Keyword(s):  
Block Cipher ◽  
Search Algorithm ◽  
Block Ciphers ◽  
Quantum Circuits ◽  
Security Level ◽  
Symmetric Key ◽  
Grover Search ◽  
Target Block ◽  
Symmetric Key Cryptography ◽  
Grover Search Algorithm

Grover search algorithm is the most representative quantum attack method that threatens the security of symmetric key cryptography. If the Grover search algorithm is applied to symmetric key cryptography, the security level of target symmetric key cryptography can be lowered from n-bit to n2-bit. When applying Grover’s search algorithm to the block cipher that is the target of potential quantum attacks, the target block cipher must be implemented as quantum circuits. Starting with the AES block cipher, a number of works have been conducted to optimize and implement target block ciphers into quantum circuits. Recently, many studies have been published to implement lightweight block ciphers as quantum circuits. In this paper, we present optimal quantum circuit designs of symmetric key cryptography, including PRESENT and GIFT block ciphers. The proposed method optimized PRESENT and GIFT block ciphers by minimizing qubits, quantum gates, and circuit depth. We compare proposed PRESENT and GIFT quantum circuits with other results of lightweight block cipher implementations in quantum circuits. Finally, quantum resources of PRESENT and GIFT block ciphers required for the oracle of the Grover search algorithm were estimated.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

scholarly journals Exploiting Dynamic Quantum Circuits in a Quantum Algorithm with Superconducting Qubits

2021 ◽  
Vol 127 (10) ◽  
Author(s):  
A. D. Córcoles ◽  
Maika Takita ◽  
Ken Inoue ◽  
Scott Lekuch ◽  
Zlatko K. Minev ◽  
...  
Keyword(s):  
Quantum Algorithm ◽  
Superconducting Qubits ◽  
Quantum Circuits

scholarly journals The Hardness of Speeding-up Knapsack

1998 ◽  
Vol 5 (14) ◽  
Author(s):  
Sandeep Sen
Keyword(s):  
Decision Tree ◽  
Knapsack Problem ◽  
Decision Tree Model ◽  
Fixed Degree ◽  
Tree Model ◽  
Pram Model ◽  
Speed Up ◽  
Decision Version ◽  
Polynomial Class ◽  
Strongly Polynomial

We show that it is not possible to speed-up the Knapsack problem efficiently in the parallel algebraic decision tree model. More specifically, we prove that any parallel algorithm in the fixed degree algebraic decision tree model that solves the decision version of the Knapsack problem requires  Omega(sqrt(n)) rounds even by using 2^sqrt(n) processors. We extend the result to the PRAM model without bit-operations. These results are consistent with Mulmuley's recent result on the separation of the strongly-polynomial class and the corresponding NC class in the arithmetic PRAM model.<br />Keywords lower-bounds, parallel algorithms, algebraic decision tree

Solving the 0/1 knapsack problem using an adaptive genetic algorithm

2002 ◽  
Vol 16 (1) ◽  
pp. 23-30 ◽  
Author(s):  
ZOHEIR EZZIANE
Keyword(s):  
Genetic Algorithm ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Adaptive Genetic Algorithm ◽  
Stochastic Algorithms ◽  
Initial Population ◽  
Genetic Operators ◽  
Adaptive Penalty ◽  
Genetic Algorithm Approach

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

Abnormal Quantum State Search Based on Parallel Phase Comparison

2021 ◽  
Author(s):  
Guanlei Xu ◽  
Xiaogang Xu ◽  
Xiaotong Wang
Keyword(s):  
Quantum State ◽  
Search Algorithm ◽  
Quantum States ◽  
Quantum Search ◽  
Superposition State ◽  
Quantum Operator ◽  
Abnormal State ◽  
Number Formula ◽  
Speed Up ◽  
Grover Search

We discuss the problem of filtering out abnormal states from a larger number of quantum states. For this type of problem with [Formula: see text] items to be searched, both the traditional search by enumeration and classical Grover search algorithm have the complexity about [Formula: see text]. In this letter a novel quantum search scheme with exponential speed up is proposed for abnormal states. First, a new comprehensive quantum operator is well-designed to extract the superposition state containing all abnormal states with unknown number [Formula: see text] with complexity [Formula: see text] in probability 1 via well-designed parallel phase comparison. Then, every abnormal state is achieved respectively from [Formula: see text] abnormal states via [Formula: see text] times’ measurement. Finally, a numerical example is given to show the efficiency of the proposed scheme.

Ant Colony Optimization and Multiple Knapsack Problem

2007 ◽  
pp. 498-509 ◽  
Author(s):  
S. Fidanova
Keyword(s):  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Knapsack Problem ◽  
Optimization Problem ◽  
Combinatorial Optimization Problem ◽  
Ant Colony ◽  
Multiple Knapsack Problem ◽  
Constraint Problem ◽  
Multiple Knapsack ◽  
Ant Colony Optimization Algorithms

The ant colony optimization algorithms and their applications on the multiple knapsack problem (MKP) are introduced. The MKP is a hard combinatorial optimization problem with wide application. Problems from different industrial fields can be interpreted as a knapsack problem including financial and other management. The MKP is represented by a graph, and solutions are represented by paths through the graph. Two pheromone models are compared: pheromone on nodes and pheromone on arcs of the graph. The MKP is a constraint problem which provides possibilities to use varied heuristic information. The purpose of the chapter is to compare a variety of heuristic and pheromone models and different variants of ACO algorithms on MKP.

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