scholarly journals QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis

Author(s):  
Ed Younis ◽  
Koushik Sen ◽  
Katherine Yelick ◽  
Costin Iancu
Author(s):  
Marc G. Davis ◽  
Ethan Smith ◽  
Ana Tudor ◽  
Koushik Sen ◽  
Irfan Siddiqi ◽  
...  

Author(s):  
Debjyoti Bhattacharjee ◽  
Mathias Soeken ◽  
Srijit Dutta ◽  
Anupam Chattopadhyay ◽  
Giovanni De Micheli

2013 ◽  
Vol 28 (01) ◽  
pp. 1350191
Author(s):  
XIAOYU LI ◽  
GUOWU YANG ◽  
CARLOS MANUEL TORRES ◽  
DESHENG ZHENG ◽  
KANG L. WANG

The quantum incrementer is one of the simplest quantum operators, which exhibits basic arithmetic operations such as addition, the propagation of carry qubits and the resetting of carry qubits. In this paper, three quantum incrementer gate circuit topologies are derived and compared based upon their total number of gates, the complexity of the circuits, the types of gates used and the number of carry or ancilla qubits implemented. The first case is a generalized n-qubit quantum incrementer gate with the notation of (n:0). Two other quantum incrementer topologies are proposed with the notations of (n:n-1: RE ) and (n:n-1: RD ). A general method is derived to decompose complicated quantum circuits into simpler quantum circuits which are easier to manage and physically implement. Due to the cancelation of intermediate unitary gates, it is shown that adding ancilla qubits slightly increases the complexity of a given circuit by the order of 3n, which pales in comparison to the complexity of the original circuit of the order n2 without reduction. Finally, a simple application of the generalized n-qubit quantum incrementer gate is introduced, which is related to quantum walks.


2021 ◽  
Vol 24 (67) ◽  
pp. 90-101
Author(s):  
Otto Menegasso Pires ◽  
Eduardo Inacio Duzzioni ◽  
Jerusa Marchi ◽  
Rafael De Santiago

Quantum Computing has been evolving in the last years. Although nowadays quantum algorithms performance has shown superior to their classical counterparts, quantum decoherence and additional auxiliary qubits needed for error tolerance routines have been huge barriers for quantum algorithms efficient use.These restrictions lead us to search for ways to minimize algorithms costs, i.e the number of quantum logical gates and the depth of the circuit. For this, quantum circuit synthesis and quantum circuit optimization techniques are explored.We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis. The agent had the task of creating quantum circuits up to 5 qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.


2017 ◽  
Vol 13 (4) ◽  
pp. 1-27 ◽  
Author(s):  
Mahboobeh Houshmand ◽  
Mehdi Sedighi ◽  
Morteza Saheb Zamani ◽  
Kourosh Marjoei

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