31 research outputs found
GENETIC ALGORITHM FOR BINARY AND FUNCTIONAL DECISION DIAGRAMS OPTIMIZATION
Decision diagrams (DD) are a widely used data structure for discrete functions representation. The major problem in DD-based applicationsis the DD size minimization (reduction of the number of nodes), because their size is dependent on the variables order. Genetic algorithms are often used in different optimization problems including the DD size optimization. In this paper, we apply the genetic algorithm to minimize the size of both Binary Decision Diagrams (BDDs) and Functional Decision Diagrams (FDDs). In both cases, in the proposed algorithm, a Bottom-Up Partially Matched Crossover (BU-PMX) is used as the crossover operator. In the case of BDDs, mutation is done in the standard way by variables exchanging. In the case of FDDs, the mutation by changing the polarity of variables is additionally used. Experimental results of optimization of the BDDs and FDDs of the set of benchmark functions are also presented
Minimization of Quantum Circuits using Quantum Operator Forms
In this paper we present a method for minimizing reversible quantum circuits
using the Quantum Operator Form (QOF); a new representation of quantum circuit
and of quantum-realized reversible circuits based on the CNOT, CV and
CV quantum gates. The proposed form is a quantum extension to the
well known Reed-Muller but unlike the Reed-Muller form, the QOF allows the
usage of different quantum gates. Therefore QOF permits minimization of quantum
circuits by using properties of different gates than only the multi-control
Toffoli gates. We introduce a set of minimization rules and a pseudo-algorithm
that can be used to design circuits with the CNOT, CV and CV quantum
gates. We show how the QOF can be used to minimize reversible quantum circuits
and how the rules allow to obtain exact realizations using the above mentioned
quantum gates.Comment: 11 pages, 14 figures, Proceedings of the ULSI Workshop 2012 (@ISMVL
2012
Minimization of lines in reversible circuits
Reversible computing has been theoretically shown to be an efficient approach over conventional computing due to the property of virtually zero power dissipation. A major concern in reversible circuits is the number of circuit lines or qubits which are a limited resource. In this thesis we explore the line reduction problem using a decision diagram based synthesis approach and introduce a line reduction algorithm— Minimization of lines using Ordered Kronecker Functional Decision Diagrams (MOKFDD). The algorithm uses a new sub-circuit for a positive Davio node structure in addition to the existing node structures. We also present a shared node ordering for OKFDDs. OKFDDs are a combination of OBDDs and OFDDs. The experimental results shows that the number of circuit lines and quantum cost can be reduced with our proposed approach.NSER
Scalable Design and Synthesis of Reversible Circuits
The expectations on circuits are rising with their number of applications, and technologies alternative to CMOS are becoming more important day by day. A promising alternative is reversible computation, a computing paradigm with applications in quantum computation, adiabatic circuits, program inversion, etc. An elaborated design flow is not available to reversible circuit design yet. In this work, two directions are considered: Exploiting the conventional design flow and developing a new flow according to the properties of reversible circuits. Which direction should be taken is not obvious, so we discuss the possible assets and drawbacks of taking either direction. We present ideas which can be exploited and outline open challenges which still have to be addressed. Preliminary results obtained by initial implementations illustrate the way to go. By this we present and discuss two promising and complementary directions for the scalable design and synthesis of reversible circuits
Programming Quantum Computers Using Design Automation
Recent developments in quantum hardware indicate that systems featuring more
than 50 physical qubits are within reach. At this scale, classical simulation
will no longer be feasible and there is a possibility that such quantum devices
may outperform even classical supercomputers at certain tasks. With the rapid
growth of qubit numbers and coherence times comes the increasingly difficult
challenge of quantum program compilation. This entails the translation of a
high-level description of a quantum algorithm to hardware-specific low-level
operations which can be carried out by the quantum device. Some parts of the
calculation may still be performed manually due to the lack of efficient
methods. This, in turn, may lead to a design gap, which will prevent the
programming of a quantum computer. In this paper, we discuss the challenges in
fully-automatic quantum compilation. We motivate directions for future research
to tackle these challenges. Yet, with the algorithms and approaches that exist
today, we demonstrate how to automatically perform the quantum programming flow
from algorithm to a physical quantum computer for a simple algorithmic
benchmark, namely the hidden shift problem. We present and use two tool flows
which invoke RevKit. One which is based on ProjectQ and which targets the IBM
Quantum Experience or a local simulator, and one which is based on Microsoft's
quantum programming language Q.Comment: 10 pages, 10 figures. To appear in: Proceedings of Design, Automation
and Test in Europe (DATE 2018