Classical Simulation of Quantum Adiabatic Algorithms using Mathematica on GPUs
Sandra D\'iaz-Pier, Salvador E. Venegas-Andraca, Jos\'e Luis, G\'omez-Mu\~noz

TL;DR
This paper introduces a parallel processing simulation environment on personal computers using Mathematica and C++ to emulate quantum adiabatic algorithms, enabling larger quantum systems to be simulated classically.
Contribution
The paper presents a novel simulation platform combining Mathematica and C++ with GPU support, significantly increasing the size of quantum systems that can be classically simulated.
Findings
Simulator can handle more qubits than previous classical simulators.
Parallel processing on GPUs enhances simulation speed and capacity.
Provides a comparative review of existing quantum simulators.
Abstract
In this paper we present a simulation environment enhanced with parallel processing which can be used on personal computers, based on a high-level user interface developed on Mathematica\copyright which is connected to C++ code in order to make our platform capable of communicating with a Graphics Processing Unit. We introduce the reader to the behavior of our proposal by simulating a quantum adiabatic algorithm designed for solving hard instances of the 3-SAT problem. We show that our simulator is capable of significantly increasing the number of qubits that can be simulated using classical hardware. Finally, we present a review of currently available classical simulators of quantum systems together with some justifications, based on our willingness to further understand processing properties of Nature, for devoting resources to building more powerful simulators.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Cloud Computing and Resource Management · Parallel Computing and Optimization Techniques
