scholarly journals Estimating Algorithmic Information Using Quantum Computing for Genomics Applications

2021 ◽  
Vol 11 (6) ◽  
pp. 2696
Author(s):  
Aritra Sarkar ◽  
Zaid Al-Ars ◽  
Koen Bertels
Keyword(s):  
Quantum Computing ◽  
Classical Case ◽  
Generative Models ◽  
Quantum Superposition ◽  
Phylogenetic Tree Analysis ◽  
Protein Protein Interaction ◽  
Algorithmic Information ◽  
Quantum Programming ◽  
Program Output ◽  
Experimental Use

Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to the exhaustive enumeration needed in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and is publicly available on GitHub.

scholarly journals Estimating Algorithmic Information using Quantum Computing for Genomics Applications

2021 ◽  
Author(s):  
Aritra Sarkar ◽  
Zaid Al-Ars ◽  
Koen Bertels
Keyword(s):  
Quantum Computing ◽  
Classical Case ◽  
Generative Models ◽  
Research Centre ◽  
Quantum Superposition ◽  
Phylogenetic Tree Analysis ◽  
Protein Protein Interaction ◽  
Algorithmic Information ◽  
Quantum Programming ◽  
Program Output

Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to an indispensable exhaustive enumeration in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and can be found on https://github.com/Advanced-Research-Centre/QPULBA

Computing, Philosophy and Reality

2010 ◽  
pp. 238-252
Author(s):  
Joseph Brenner
Keyword(s):  
Quantum Computing ◽  
Cellular Automata ◽  
Computational Modeling ◽  
Computational Models ◽  
Mathematical Structure ◽  
Quantum Superposition ◽  
Information Theoretic ◽  
Process View ◽  
New Interpretation ◽  
The Universe

The conjunction of the disciplines of computing and philosophy implies that discussion of computational models and approaches should include explicit statements of their underlying worldview, given the fact that reality includes both computational and non-computational domains. As outlined at ECAP08, both domains of reality can be characterized by the different logics applicable to them. A new “Logic in Reality” (LIR) was proposed as best describing the dynamics of real, non-computable processes. The LIR process view of the real macroscopic world is compared here with recent computational and information-theoretic models. Proposals that the universe can be described as a mathematical structure equivalent to a computer or by simple cellular automata are deflated. A new interpretation of quantum superposition as supporting a concept of paraconsistent parallelism in quantum computing and an appropriate ontological commitment for computational modeling are discussed.

scholarly journals Quantum Computing and Complexity in Art

Leonardo ◽  
2019 ◽  
Vol 52 (3) ◽  
pp. 230-235
Author(s):  
Libby Heaney
Keyword(s):  
Quantum Computing ◽  
Quantum Entanglement ◽  
Quantum Measurement ◽  
Research Experience ◽  
Quantum Superposition ◽  
Interactive Installation

The author draws on her research experience in quantum computing to discuss the conception and form of an interactive installation, CLOUD. CLOUD explores complexity in the postdigital by referencing the principles of quantum superposition, quantum entanglement and quantum measurement.

A Hybrid Ant Algorithm for the Vehicle Routing Problem

2012 ◽  
Vol 182-183 ◽  
pp. 2118-2122
Author(s):  
Yu Li ◽  
Liang Ma
Keyword(s):  
Quantum Computing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Quantum Walk ◽  
Ant Algorithm ◽  
Quantum Superposition ◽  
Routing Problem ◽  
Numerical Examples ◽  
Search Capability

A hybrid algorithm for solving the vehicle routing problem is proposed based upon the combination of Ant Colony Optimization and quantum computing. The algorithm takes the advantage of the principles in quantum computing, such as the qubit, quantum gate, and the quantum superposition of states. It can search the best solution by quantum walk and can further improve the search capability of the algorithm for the best solution. Numerical examples are tested and verified, that show the good performances.

scholarly journals Quantum Turing Machines: Computations and Measurements

2020 ◽  
Vol 10 (16) ◽  
pp. 5551
Author(s):  
Stefano Guerrini ◽  
Simone Martini ◽  
Andrea Masini
Keyword(s):  
Programming Languages ◽  
Classical Case ◽  
Turing Machines ◽  
Quantum Programming Languages ◽  
Quantum Programming ◽  
Definition Of ◽  
Observation Protocol ◽  
Class Of Functions ◽  
Computable Functions ◽  
Arbitrary Quantum

Contrary to the classical case, the relation between quantum programming languages and quantum Turing Machines (QTM) has not been fully investigated. In particular, there are features of QTMs that have not been exploited, a notable example being the intrinsic infinite nature of any quantum computation. In this paper, we propose a definition of QTM, which extends and unifies the notions of Deutsch and Bernstein & Vazirani. In particular, we allow both arbitrary quantum input, and meaningful superpositions of computations, where some of them are “terminated” with an “output”, while others are not. For some infinite computations an “output” is obtained as a limit of finite portions of the computation. We propose a natural and robust observation protocol for our QTMs, which does not modify the probability of the possible outcomes of the machines. Finally, we use QTMs to define a class of quantum computable functions—any such function is a mapping from a general quantum state to a probability distribution of natural numbers. We expect that our class of functions, when restricted to classical input-output, will not be different from the set of the recursive functions.

Quantum Computing and Quantum Communication

2018 ◽  
pp. 7715-7730
Author(s):  
Göran Pulkkis ◽  
Kaj J. Grahn
Keyword(s):  
Quantum Computing ◽  
Quantum Computation ◽  
Quantum Cryptography ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum States ◽  
Future Perspectives ◽  
Quantum Informatics ◽  
Quantum Programming ◽  
Computing Models

This article presents state-of-the-art and future perspectives of quantum computing and communication. Timeline of relevant findings in quantum informatics, such as quantum algorithms, quantum cryptography protocols, and quantum computing models, is summarized. Mathematics of information representation with quantum states is presented. The quantum circuit and adiabatic models of quantum computation are outlined. The functionality, limitations, and security of the quantum key distribution (QKD) protocol is presented. Current implementations of quantum computers and principles of quantum programming are shortly described.

GENERATING TEST PATTERNS FOR FPGA CIRCUITS: A QUANTUM COMPUTING APPROACH

2009 ◽  
Vol 06 (01) ◽  
pp. 131-145
Author(s):  
AMARDEEP SINGH
Keyword(s):  
Quantum Computing ◽  
Test Pattern ◽  
Pattern Generation ◽  
Test Pattern Generation ◽  
Test Vector ◽  
Quantum Superposition ◽  
New Approach ◽  
Classical Computing ◽  
Computing Approach ◽  
Quantum Parallelism

This paper presents an effective test pattern generation approach for FPGA circuits by applying quantum computing algorithms. A prototypical new algorithm named QFPGA is developed utilizing the properties of quantum theory, such as quantum superposition and quantum parallelism. The effectiveness of this technique in terms of result quality, CPU requirements, fault detection and number of iterations is experimentally compared with some of the existing classical approaches, like exhaustive search, simulated annealing and genetic algorithms. The algorithm developed is so efficient that it requires only √N (N is the total number of vectors) iterations to find the desired test vector, whereas in classical computing it takes N/2 iterations. Simulation results on various benchmark circuits are also covered in this paper. The extendability of the new approach enables users to easily find the test vector from FPGA circuits and can be adapted for testing FPGA chips.

scholarly journals The Modern Quantum Computing Tools Invstigation

2020 ◽  
Vol 2 (95) ◽  
pp. 15-20
Author(s):  
I. K. Bozhko ◽  
G.G. Chetverykov ◽  
О.А. Karataiev
Keyword(s):  
Quantum Computing ◽  
Quantum Computation ◽  
Programming Language ◽  
The Other ◽  
Current Paper ◽  
Main Research ◽  
Research Areas ◽  
Current State ◽  
Quantum Programming ◽  
The One

The current paper covers the investigation of the current state of the existing tools for quantum programming includingQCL (Quantum Computation Language), quantum pseudocode, Q# programming language and Quipper. Since quantumcomputing is one of the main research areas today, the respective tools are being created quite often. They are aimed onsimplifying the development of quantum programs, on the one hand, and provide some platform for testing and runningthem, on the other hand. So, the authors investigated the currently available tools and provided the results in the article

Grover-based Ashenhurst-Curtis decomposition using quantum language quipper

2019 ◽  
Vol 19 (1&2) ◽  
pp. 35-66
Author(s):  
Yiwei Li ◽  
Edison Tsai ◽  
Marek Perkowski ◽  
Xiaoyu Song
Keyword(s):  
Quantum Computing ◽  
Search Algorithm ◽  
Minimum Cost ◽  
Database Systems ◽  
Search Problem ◽  
Decomposition Problem ◽  
Functional Decomposition ◽  
Quantum Programming ◽  
Grover’S Search Algorithm ◽  
Quantum Language

Functional decomposition plays a key role in several areas such as system design, digital circuits, database systems, and Machine Learning. This paper presents a novel quantum computing approach based on Grover’s search algorithm for a generalized Ashenhurst-Curtis decomposition. The method models the decomposition problem as a search problem and constructs the oracle circuit based on the set-theoretic partition algebra. A hybrid quantum-based algorithm takes advantage of the quadratic speedup achieved by Grover’s search algorithm with quantum oracles for finding the minimum-cost decomposition. The method is implemented and simulated in the quantum programming language Quipper. This work constitutes the first attempt to apply quantum computing to functional decomposition.

Quantum Computing and Quantum Communication

2019 ◽  
pp. 1641-1659
Author(s):  
Göran Pulkkis ◽  
Kaj J. Grahn
Keyword(s):  
Quantum Computing ◽  
Quantum Computation ◽  
Quantum Cryptography ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum States ◽  
Future Perspectives ◽  
Quantum Informatics ◽  
Quantum Programming ◽  
Computing Models

This chapter presents state-of-the-art and future perspectives of quantum computing and communication. Timeline of relevant findings in quantum informatics, such as quantum algorithms, quantum cryptography protocols, and quantum computing models, is summarized. Mathematics of information representation with quantum states is presented. The quantum circuit and adiabatic models of quantum computation are outlined. The functionality, limitations, and security of the quantum key distribution (QKD) protocol is presented. Current implementations of quantum computers and principles of quantum programming are shortly described.

Sign in / Sign up

Export Citation Format

Share Document