Evidence for quantum annealing with more than one hundred qubits

By Sergio Boixo, Troels F. Rønnow, Sergei V. Isakov, Zhihui Wang, Dave Wecker, Daniel A. Lidar, John M. Martinis, Matthias Troyer. Nature Physics, 2013

779 citations recorded by the source corpus.

Quantum technology is maturing to the point where quantum devices, such as quantum communication systems, quantum random number generators and quantum simulators may be built with capabilities exceeding classical computers. A quantum annealer, in particular, solves optimization problems by evolving a known initial configuration at non-zero temperature towards the ground state of a Hamiltonian encoding a given problem. Here, we present results from tests on a 108 qubit D-Wave One device based on superconducting flux qubits. By studying correlations we find that the device performance is inconsistent with classical annealing or that it is governed by classical spin dynamics. In contrast, we find that the device correlates well with simulated quantum annealing. We find further evidence for quantum annealing in the form of small-gap avoided level crossings characterizing the hard problems. To assess the computational power of the device we compare it against optimized classical algorithms. Quantum annealing is expected to solve certain optimization problems more efficiently, but there are still open questions regarding the functioning of devices such as D-Wave One. A numerical and experimental investigation of its performance shows evidence for quantum annealing with 108 qubits.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/b923317d7eba5a5d56b2e9aff09cf4e5ec2ea132

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Citations in the corpus

What is the Computational Value of Finite Range Tunneling (2015, background): “This debate has now been settled in the affirmative with a sequence of publications [6,8,9,12,13,18,21] demonstrating that quantum resources are present and functional in the processors.”
Realizing the classical XY Hamiltonian in polariton simulators. (2016, positive): “More generally, quantum simulators can be used to solve hard optimization problems that are at the heart of almost any multicomponent system: new materials for energy, pharmaceuticals, and photosynthesis, among others [13].”
Quantum Approximate Optimization Algorithm: Performance, Mechanism, and Implementation on Near-Term Devices (2018, background): “A predecessor of QAOA, quantum annealing (QA) has been widely studied for the purpose of solving combinatorial optimization problems [39–42].”
A fully programmable 100-spin coherent Ising machine with all-to-all connections (2016, background): “In the case of recent experimental investigations of quantum annealers, there has been controversy about the operating regime and mechanism (11, 13, 35), and about whether the class of machines being considered has already delivered a speedup or will ever deliver a speedup (12,36–42).”
Physics-Inspired Optimization for Quadratic Unconstrained Problems Using a Digital Annealer (2018, background): “Efforts to benchmark quantum annealers against classical counterparts such as simulated annealing (SA) [15] have abounded [14, 16–30].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, methodology): “In terms of practical implementation, a quantum annealing chipset is already commercially available from the company D-Wave1 [135].”
Architectural Considerations in the Design of a Superconducting Quantum Annealing Processor (2014, positive): “The authors, as well as a number of other researchers, have gained some experience using this first generation processor [6]–[9].”
Quantum Optimization of Fully-Connected Spin Glasses (2014, background): “[11] Sergio Boixo, Troels F Rønnow, Sergei V Isakov, Zhihui Wang, David Wecker, Daniel A Lidar, John M Martinis, and Matthias Troyer.”
Decoherence in adiabatic quantum computation (2015, methodology): “In simulated quantum annealing [24, 74, 75] (SQA), Monte Carlo dynamics are used to sample from the instantaneous Gibbs state along the annealing evolution.”
Adiabatic Quantum Computing (2016, methodology): “The scaling of the algorithm with n is then determined from the scaling with n of TTSopt, as, e.g., in (Boixo et al., 2014).”
Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models (2019, methodology): “The ability to obtain a quantum speedup effect for this system applied to QUBO problems has been demonstrated in [14].”