20 research outputs found
How to Efficiently Handle Complex Values? Implementing Decision Diagrams for Quantum Computing
Quantum computing promises substantial speedups by exploiting quantum
mechanical phenomena such as superposition and entanglement. Corresponding
design methods require efficient means of representation and manipulation of
quantum functionality. In the classical domain, decision diagrams have been
successfully employed as a powerful alternative to straightforward means such
as truth tables. This motivated extensive research on whether decision diagrams
provide similar potential in the quantum domain -- resulting in new types of
decision diagrams capable of substantially reducing the complexity of
representing quantum states and functionality. From an implementation
perspective, many concepts and techniques from the classical domain can be
re-used in order to implement decision diagrams packages for the quantum realm.
However, new problems -- namely how to efficiently handle complex numbers --
arise. In this work, we propose a solution to overcome these problems.
Experimental evaluations confirm that this yields improvements of orders of
magnitude in the runtime needed to create and to utilize these decision
diagrams. The resulting implementation is publicly available as a quantum DD
package at http://iic.jku.at/eda/research/quantum_dd
Mapping switch-level simulation onto gate-level hardware accelerators
In this paper, we present a framework for performing switch-level simulation on hardware accelerators
Discrete Function Representations Utilizing Decision Diagrams and Spectral Techniques
All discrete function representations become exponential in size in the worst case. Binary decision diagrams have become a common method of representing discrete functions in computer-aided design applications. For many functions, binary decision diagrams do provide compact representations. This work presents a way to represent large decision diagrams as multiple smaller partial binary decision diagrams. In the Boolean domain, each truth table entry consisting of a Boolean value only provides local information about a function at that point in the Boolean space. Partial binary decision diagrams thus result in the loss of information for a portion of the Boolean space. If the function were represented in the spectral domain however, each integer-valued coefficient would contain some global information about the function. This work also explores spectral representations of discrete functions, including the implementation of a method for transforming circuits from netlist representations directly into spectral decision diagrams