GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm

• URL: http://arxiv.org/abs/2104.03293v4
• Date: Mon, 16 May 2022 11:38:40 GMT
• Title: GPU-accelerated simulations of quantum annealing and the quantum approximate optimization algorithm
• Authors: Dennis Willsch, Madita Willsch, Fengping Jin, Kristel Michielsen, Hans De Raedt
• Abstract summary: We study large-scale applications using a GPU-accelerated version of the massively parallel J"ulich universal quantum computer simulator (JUQCS--G)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study large-scale applications using a GPU-accelerated version of the massively parallel J\"ulich universal quantum computer simulator (JUQCS--G). First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core GPUs. Then, we use JUQCS--G to study the relation between quantum annealing (QA) and the quantum approximate optimization algorithm (QAOA). We find that a very coarsely discretized version of QA, termed approximate quantum annealing (AQA), performs surprisingly well in comparison to the QAOA. It can either be used to initialize the QAOA, or to avoid the costly optimization procedure altogether. Furthermore, we study the scaling of the success probability when using AQA for problems with 30 to 40 qubits. We find that the case with the largest discretization error scales most favorably, surpassing the best result obtained from the QAOA.

Related papers

• Optimization by Decoded Quantum Interferometry [43.55132675053983]
  We introduce a quantum algorithm for reducing classical optimization problems to classical decoding problems. We show that DQI achieves a better approximation ratio than any quantum-time classical algorithm known to us.
  arXiv  Detail & Related papers  (2024-08-15T17:47:42Z)
• Hybrid Classical-Quantum Simulation of MaxCut using QAOA-in-QAOA [0.0]
  In this work, an implementation of the QAOA2 method for the scalable solution of the MaxCut problem is presented. The limits of the Goemans-Williamson (GW) algorithm as a purely classical alternative to QAOA are investigated. Results from large-scale simulations of up to 33 qubits are presented, showing the advantage of QAOA in certain cases and the efficiency of the implementation.
  arXiv  Detail & Related papers  (2024-06-25T09:02:31Z)
• Fast Simulation of High-Depth QAOA Circuits [10.778538580079365]
  We present a simulator for the Quantum Approximate Optimization Algorithm (QAOA) Our simulator is designed with the goal of reducing the computational cost of QAOA parameter optimization. We reduce the time for a typical QAOA parameter optimization by eleven times for $n = 26$ qubits compared to a state-of-the-art GPU quantum circuit simulator based on cuQuantum.
  arXiv  Detail & Related papers  (2023-09-09T17:01:29Z)
• Evidence of Scaling Advantage for the Quantum Approximate Optimization Algorithm on a Classically Intractable Problem [8.738180371389097]
  The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem. We observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers.
  arXiv  Detail & Related papers  (2023-08-04T14:17:21Z)
• Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization [11.445200448951072]
  Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic algorithm. We demonstrate that the intuition from the adiabatic algorithm applies to the task of choosing the QAOA initial state.
  arXiv  Detail & Related papers  (2023-05-05T21:54:28Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines [81.4597482536073]
  Quantum approximate optimization algorithms (QAOAs) utilize the power of quantum machines and inherit the spirit of adiabatic evolution. We propose QAOA-in-QAOA ($textQAOA2$) to solve arbitrary large-scale MaxCut problems using quantum machines. Our method can be seamlessly embedded into other advanced strategies to enhance the capability of QAOAs in large-scale optimization problems.
  arXiv  Detail & Related papers  (2022-05-24T03:49:10Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Optimal Qubit Mapping with Simultaneous Gate Absorption [9.530683922512873]
  A key step in compilation is mapping the qubits in the program to physical qubits on a given quantum computer. We present OLSQ-GA, an optimal qubit mapper with a key feature of simultaneous SWAP gate absorption. OLSQ-GA reduces depth by up to 50.0% and SWAP count by 100% compared to other state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-14T05:15:36Z)
• Accelerating variational quantum algorithms with multiple quantum processors [78.36566711543476]
  Variational quantum algorithms (VQAs) have the potential of utilizing near-term quantum machines to gain certain computational advantages. Modern VQAs suffer from cumbersome computational overhead, hampered by the tradition of employing a solitary quantum processor to handle large data. Here we devise an efficient distributed optimization scheme, called QUDIO, to address this issue.
  arXiv  Detail & Related papers  (2021-06-24T08:18:42Z)
• Hybrid quantum-classical algorithms for approximate graph coloring [65.62256987706128]
  We show how to apply the quantum approximate optimization algorithm (RQAOA) to MAX-$k$-CUT, the problem of finding an approximate $k$-vertex coloring of a graph. We construct an efficient classical simulation algorithm which simulates level-$1$ QAOA and level-$1$ RQAOA for arbitrary graphs.
  arXiv  Detail & Related papers  (2020-11-26T18:22:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.