Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor

Harris, Richard E.; Johnson, Mark W.; Lanting, T.; Berkley, A. J.; Johansson, Jan; Bunyk, P.; Tolkacheva, E.; Ladizinsky, E.; Ladizinsky, N.; Oh, T.; Cioata, Florentin; Perminov, I.; Spear, Peter D.; Enderud, C.; Rich, Chris; Uchaikin, S.; Thom, M. C.; Chapple, E. M.; Wang, J.; Wilson, Brian; Amin, M. H. S.; Dickson, Neil G.; Karimi, Kamran; Macready, B.; Truncik, C. J. S.; Rose, Geordie

doi:10.1103/physrevb.82.024511

Cited by 275 publications

(295 citation statements)

References 53 publications

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“…Up to now many experiments have confirmed the existence of quantum phenomena in such processors [15,[35][36][37][38], which includes entanglement [39]. A quantum annealing processor implements the time-dependent Hamiltonian…”

Section: Qbm With a Quantum Annealing Processormentioning

confidence: 99%

Quantum Boltzmann Machine

et al. 2018

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Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, we propose a new machine-learning approach based on quantum Boltzmann distribution of a quantum Hamiltonian. Because of the noncommutative nature of quantum mechanics, the training process of the quantum Boltzmann machine (QBM) can become nontrivial. We circumvent the problem by introducing bounds on the quantum probabilities. This allows us to train the QBM efficiently by sampling. We show examples of QBM training with and without the bound, using exact diagonalization, and compare the results with classical Boltzmann training. We also discuss the possibility of using quantum annealing processors for QBM training and application.

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Section: Qbm With a Quantum Annealing Processormentioning

confidence: 99%

Quantum Boltzmann Machine

et al. 2018

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“…Details about these physical parameters can be found in Ref. [39]. 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 (a) DW2-ISI …”

Section: Appendix B: Comparing Decoding Strategiesmentioning

confidence: 99%

“…These devices and the underlying technology have been described before in detail in various publications (e.g., Refs. [37][38][39]44]). …”

Section: Benchmarking Using Antiferromagnetic Chainsmentioning

confidence: 99%

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Performance of two different quantum annealing correction codes

Mishra

Albash

Lidar

2015

Quantum Inf Process

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Quantum annealing is a promising approach for solving optimization problems, but like all other quantum information processing methods, it requires error correction to ensure scalability. In this work we experimentally compare two quantum annealing correction codes in the setting of antiferromagnetic chains, using two different quantum annealing processors. The lower temperature processor gives rise to higher success probabilities. The two codes differ in a number of interesting and important ways, but both require four physical qubits per encoded qubit. We find significant performance differences, which we explain in terms of the effective energy boost provided by the respective redundantly encoded logical operators of the two codes. The code with the higher energy boost results in improved performance, at the expense of a lower degree encoded graph. Therefore, we find that there exists an important tradeoff between encoded connectivity and performance for quantum annealing correction codes.

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“…Recently, we have designed, fabricated and tested a programmable superconducting QA processor 22,23 . Here we report the first experimental exploration of the effect of thermal noise on QA.…”

mentioning

confidence: 99%

Thermally assisted quantum annealing of a 16-qubit problem

et al. 2013

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Efforts to develop useful quantum computers have been blocked primarily by environmental noise. Quantum annealing is a scheme of quantum computation that is predicted to be more robust against noise, because despite the thermal environment mixing the system's state in the energy basis, the system partially retains coherence in the computational basis, and hence is able to establish well-defined eigenstates. Here we examine the environment's effect on quantum annealing using 16 qubits of a superconducting quantum processor. For a problem instance with an isolated small-gap anticrossing between the lowest two energy levels, we experimentally demonstrate that, even with annealing times eight orders of magnitude longer than the predicted single-qubit decoherence time, the probabilities of performing a successful computation are similar to those expected for a fully coherent system. Moreover, for the problem studied, we show that quantum annealing can take advantage of a thermal environment to achieve a speedup factor of up to 1,000 over a closed system.

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Experimental investigation of an eight-qubit unit cell in a superconducting optimization processor

Cited by 275 publications

References 53 publications

Quantum Boltzmann Machine

Quantum Boltzmann Machine

Performance of two different quantum annealing correction codes

Thermally assisted quantum annealing of a 16-qubit problem

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