A Quantum Computing Approach to Model Checking for Advanced Manufacturing Problems

2014 ◽  
Author(s):  
Federico M. Spedalieri ◽  
John Damoulakis
Keyword(s):  
Quantum Computing ◽  
Model Checking ◽  
Advanced Manufacturing ◽  
Manufacturing Problems ◽  
Computing Approach

scholarly journals Atomic-precision advanced manufacturing for Si quantum computing

MRS Bulletin ◽  
2021 ◽  
Author(s):  
Ezra Bussmann ◽  
Robert E. Butera ◽  
James H. G. Owen ◽  
John N. Randall ◽  
Steven M. Rinaldi ◽  
...  
Keyword(s):  
Quantum Computing ◽  
Fault Tolerant ◽  
Scale Up ◽  
Synthesis Method ◽  
Advanced Manufacturing ◽  
Materials Synthesis ◽  
Spin Interactions ◽  
Atomic Precision ◽  
Quantum Error ◽  
Technique Development

AbstractA materials synthesis method that we call atomic-precision advanced manufacturing (APAM), which is the only known route to tailor silicon nanoelectronics with full 3D atomic precision, is making an impact as a powerful prototyping tool for quantum computing. Quantum computing schemes using atomic (31P) spin qubits are compelling for future scale-up owing to long dephasing times, one- and two-qubit gates nearing high-fidelity thresholds for fault-tolerant quantum error correction, and emerging routes to manufacturing via proven Si foundry techniques. Multiqubit devices are challenging to fabricate by conventional means owing to tight interqubit pitches forced by short-range spin interactions, and APAM offers the required (Å-scale) precision to systematically investigate solutions. However, applying APAM to fabricate circuitry with increasing numbers of qubits will require significant technique development. Here, we provide a tutorial on APAM techniques and materials and highlight its impacts in quantum computing research. Finally, we describe challenges on the path to multiqubit architectures and opportunities for APAM technique development. Graphic Abstract

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.

Quantum Computing Approach for Alignment-Free Sequence Search and Classification

2012 ◽  
pp. 279-300
Author(s):  
Rao M. Kotamarti ◽  
Mitchell A. Thornton ◽  
Margaret H. Dunham
Keyword(s):  
Quantum Computing ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Search Space ◽  
Classification Problem ◽  
Sequence Classification ◽  
Sequence Search ◽  
Quantum Version ◽  
Computing Approach ◽  
Candidate Set

Many classes of algorithms that suffer from large complexities when implemented on conventional computers may be reformulated resulting in greatly reduced complexity when implemented on quantum computers. The dramatic reductions in complexity for certain types of quantum algorithms coupled with the computationally challenging problems in some bioinformatics problems motivates researchers to devise efficient quantum algorithms for sequence (DNA, RNA, protein) analysis. This chapter shows that the important sequence classification problem in bioinformatics is suitable for formulation as a quantum algorithm. This chapter leverages earlier research for sequence classification based on Extensible Markov Model (EMM) and proposes a quantum computing alternative. The authors utilize sequence family profiles built using EMM methodology which is based on using pre-counted word data for each sequence. Then a new method termed quantum seeding is proposed for generating a key based on high frequency words. The key is applied in a quantum search based on Grover algorithm to determine a candidate set of models resulting in a significantly reduced search space. Given Z as a function of M models of size N, the quantum version of the seeding algorithm has a time complexity in the order of as opposed to O(Z) for the standard classic version for large values of Z.

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