Genetic Programming
نویسندگان
چکیده
Quantum computers are computational devices that use the dynamics of atomic-scale objects to store and manipulate information. Only a few, small-scale quantum computers have been built to date, but quantum computers can in principle outperform all possible classical computers in significant ways. Quantum computation is therefore a subject of considerable theoretical interest that may also have practical applications in the future. Genetic programming can automatically discover new algorithms for quantum computers [Spector et al., 1998]. We describe how to simulate a quantum computer so that the fitness of a quantum algorithm can be determined on classical hardware. We then describe ways in which three different genetic programming approaches can drive the simulator to evolve new quantum algorithms. The approaches are standard tree-based genetic programming, stack-based linear genome genetic programming, and stackless linear genome genetic programming. We demonstrate the techniques on four different problems: the two-bit early promise problem, the scaling majority-on problem, the four-item database search problem, and the two-bit and-or problem. For three of these problems (all but majority-on) the automatically discovered algorithms are more efficient than any possible classical algorithms for the same problems. One of the better-than-classical algorithms (for the two-bit and-or problem) is in fact more efficient than any previously known quantum algorithm for the same problem, suggesting that genetic programming may be a valuable tool in the future study of quantum programming.
منابع مشابه
Bankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach
In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...
متن کاملApplication of Genetic Programming as a Powerful Tool for Modeling of Cellulose Acetate Membrane Preparation
متن کامل
Bedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming
Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...
متن کاملA Method for Solving Optimal Control Problems Using Genetic Programming
This paper deals with a novel method for solving optimal control problems based on genetic programming. This approach produces some trial solutions and seeks the best of them. If the solution cannot be expressed in a closed analytical form then our method produces an approximation with a controlled level of accuracy. Using numerical examples, we will demonstrate how to use the results.
متن کاملDimensionality Reduction and Improving the Performance of Automatic Modulation Classification using Genetic Programming (RESEARCH NOTE)
This paper shows how we can make advantage of using genetic programming in selection of suitable features for automatic modulation recognition. Automatic modulation recognition is one of the essential components of modern receivers. In this regard, selection of suitable features may significantly affect the performance of the process. Simulations were conducted with 5db and 10db SNRs. Test and ...
متن کاملModeling Ghotour-Chai River’s Rainfall-Runoff process by Genetic Programming
Considering the importance of water and computing the amount of rainfall runoff resulted from precipitation in recent decades, using appropriate methods for predicting the amount of runoff from rainfall date has been really essential. Rainfall-runoff models are used to estimate runoff generated from precipitation in the catchment area. Rainfall-runoff process is totally a non-linear phenomenon....
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001