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arXiv:1401.7087

How "Quantum" is the D-Wave Machine?

Seung Woo Shin, Graeme Smith, John A. Smolin, Umesh V. Vazirani

Published Jan 28, 2014• 89 citations
arXivHTMLPDF

Abstract

Recently there has been intense interest in claims about the performance of the D-Wave machine. In this paper, we outline a simple classical model, and show that it achieves excellent correlation with published input-output behavior of the D-Wave One machine on 108 qubits. While raising questions about "how quantum" the D-Wave machine is, the new model also provides additional algorithmic insights into the nature of the native computational problem solved by the D-Wave machine.

DOI
10.48550/arxiv.1401.7087

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Recently there has been intense interest in claims about the performance of the D-Wave machine. In this paper, we outline a simple classical model, and show that it achieves excellent correlation with published input-output behavior of the D-Wave One machine on 108 qubits. While raising questions about "how quantum" the D-Wave machine is, the new model also provides additional algorithmic insights into the nature of the native computational problem solved by the D-Wave machine.
The SSSV model is a simple classical model that achieves excellent correlation with published experimental data on the D-Wave machine's behavior on random instances of its native problem, thus raising questions about how "quantum" the D-Wave machine is at large scales. In response, a recent preprint by Vinci et al. proposes a particular set of instances on which the D-Wave machine behaves differently from the SSSV model. In this short note, we explain how a simple modeling of systematic errors in the machine allows the SSSV model to reproduce the behavior reported in the experiments of Vinci et al.
Recently, there has been a rapid increase interest in quantum computers. Their work is based on the use of quantum-mechanical phenomena such as superposition and entanglement for computing to transform input data into outputs, which can actually provide effective performance 3-4 orders of magnitude higher than any modern computing devices. In the past few decades, there has been an acute problem of creating a quantum computer that uses quantum mechanical effects for computations. Currently, there are prototypes of devices of this class, but it is not yet possible to achieve an effective solution to the planned tasks with their help due to a number of difficulties in implementation. However, there is a mathematical apparatus that describes the behavior of quantum particles using a wave function. Hence, it becomes possible to simulate quantum computations on the classical architecture and check the reliability of algorithms belonging to the NP class, which are theoretically executed on a quantum computer in polynomial time and which cannot be solved on classical computers within an acceptable time frame. Effectively simulate the operation of a quantum system on devices with classical architecture is impossible, it remains to propose methods for acceleration of quantum computing on classical systems.
D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output-one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.