Towards Optimizations of Quantum Circuit Simulation for Solving Max-Cut Problems with QAOA (2312.03019v1)
Abstract: Quantum approximate optimization algorithm (QAOA) is one of the popular quantum algorithms that are used to solve combinatorial optimization problems via approximations. QAOA is able to be evaluated on both physical and virtual quantum computers simulated by classical computers, with virtual ones being favored for their noise-free feature and availability. Nevertheless, performing QAOA on virtual quantum computers suffers from a slow simulation speed for solving combinatorial optimization problems which require large-scale quantum circuit simulation (QCS). In this paper, we propose techniques to accelerate QCS for QAOA using mathematical optimizations to compress quantum operations, incorporating efficient bitwise operations to further lower the computational complexity, and leveraging different levels of parallelisms from modern multi-core processors, with a study case to show the effectiveness on solving max-cut problems.
- 1994. Quantum annealing: A new method for minimizing multidimensional functions. Chemical Physics Letters 219, 5 (1994), 343–348. https://doi.org/10.1016/0009-2614(94)00117-0
- Complexity and Approximation. Springer.
- Fischer Black and Robert Litterman. 1992. Global portfolio optimization. Financial analysts journal 48, 5 (1992), 28–43.
- Cirq 2022. Cirq is a Python library for writing, manipulating, and optimizing quantum circuits and running them against quantum computers and simulators. https://github.com/quantumlib/Cirq
- A Quantum Approximate Optimization Algorithm. arXiv:1411.4028
- A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 5516 (2001), 472–475.
- Merrill M Flood. 1956. The traveling-salesman problem. Operations research 4, 1 (1956), 61–75.
- Vlad Gheorghiu. 2018. Quantum++: A modern C++ quantum computing library. PLOS ONE 13, 12 (dec 2018), e0208073.
- QuEST and High Performance Simulation of Quantum Computers. Scientific Reports 9, 1 (jul 2019). https://doi.org/10.1038/s41598-019-47174-9
- Christopher Z Mooney. 1997. Monte carlo simulation. Number 116. Sage.
- Faster Population Counts using AVX2 Instructions. CoRR abs/1611.07612 (2016). arXiv:1611.07612 http://arxiv.org/abs/1611.07612
- A variational eigenvalue solver on a photonic quantum processor. Nature Communications 5, 1 (jul 2014). https://doi.org/10.1038/ncomms5213
- Colin Reeves. 2003. Genetic algorithms. Springer. 55–82 pages.
- P.W. Shor. 1994. Algorithms for quantum computation: discrete logarithms and factoring. In Proceedings 35th Annual Symposium on Foundations of Computer Science. 124–134. https://doi.org/10.1109/SFCS.1994.365700
- qHiPSTER: The Quantum High Performance Software Testing Environment. arXiv:1601.07195
- ProjectQ: an open source software framework for quantum computing. Quantum 2 (jan 2018), 49. https://doi.org/10.22331/q-2018-01-31-49
- Simulated annealing. Springer.
- Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices. Phys. Rev. X 10 (Jun 2020), 021067. Issue 2. https://doi.org/10.1103/PhysRevX.10.021067