Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers

Grover, Jeffrey A.; Basham, James I.; Marakov, Alexander; Disseler, Steven M.; Hinkey, Robert T.; Khalil, Moe; Stegen, Zachary; Chamberlin, Thomas; DeGottardi, Wade; Clarke, David J.; Medford, James; Strand, Joel; Stoutimore, M. J. A.; Новиков, Сергей Петрович; Ferguson, David; Lidar, Daniel A.; Zick, Klaus; Przybysz, Anthony

doi:10.1103/prxquantum.01.020314

Cited by 14 publications

(13 citation statements)

References 42 publications

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“…The extent of improvement of QAML-Z over QAML for Higgs decay classification suggests that noisy intermediatescale quantum devices may be approaching real-world applicability in machine learning despite their limitations. As various metrics affecting the performance of quantum annealing technology continue to improve [71][72][73][74][75][76][77], we anticipate that further work on benchmarking wall-clock times of classical and quantum devices will benefit greatly from practically relevant algorithms such as QAML-Z, where performance equal to classical state-of-the-art machine learning has already been demonstrated in certain regimes. More broadly, the favorable results of zooming in on an Ising model to achieve a solution unreachable by discrete optimization provides future direction for quantum annealing applications, potentially extending to quantum machine learning algorithms beyond QAML.…”

Section: Discussionmentioning

confidence: 99%

Quantum adiabatic machine learning by zooming into a region of the energy surface

et al. 2020

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Recent work has shown that quantum annealing for machine learning, referred to as QAML, can perform comparably to state-of-the-art machine learning methods with a specific application to Higgs boson classification. We propose QAML-Z, an algorithm that iteratively zooms in on a region of the energy surface by mapping the problem to a continuous space and sequentially applying quantum annealing to an augmented set of weak classifiers. Results on a programmable quantum annealer show that QAML-Z matches classical deep neural network performance at small training set sizes and reduces the performance margin between QAML and classical deep neural networks by almost 50% at large training set sizes, as measured by area under the receiver operating characteristic curve. The significant improvement of quantum annealing algorithms for machine learning and the use of a discrete quantum algorithm on a continuous optimization problem both opens a class of problems that can be solved by quantum annealers and suggests the approach in performance of near-term quantum machine learning towards classical benchmarks.

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Section: Discussionmentioning

confidence: 99%

Quantum adiabatic machine learning by zooming into a region of the energy surface

et al. 2020

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“…These resonators, when their terminating rf-SQUID is biased to a flux sensitive region, act as magnetic flux detectors, capable of discerning the qubit or coupler unit's persistent current state. When the terminating rf-SQUID is biased to its flux insensitive operating point, the resonator is exclusively sensitive to the unit's energy level occupation through the resonatorunit dispersive interaction [68]. Being able to operate in these two modes alleviates the need for multiple readout structures, further freeing up space on chip.…”

Section: Resultsmentioning

confidence: 99%

Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

Tennant,

Dai,

Martinez

et al. 2021

Preprint

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Spin chains have long been considered an effective medium for long-range interactions, entanglement generation, and quantum state transfer. In this work, we explore the properties of a spin chain implemented with superconducting flux circuits, designed to act as a connectivity medium between two superconducting qubits. The susceptibility of the chain is probed and shown to support long-range, cross chain correlations. In addition, interactions between the two end qubits, mediated by the coupler chain, are demonstrated. This work has direct applicability in near term quantum annealing processors as a means of generating long-range, coherent coupling between qubits.

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“…Details of the persistent-current readout can be found in ref. 37 . Relevant device parameters are discussed in "Methods".…”

Section: S-curve Width Reduction Via Annealing Path Controlmentioning

confidence: 99%

Anneal-path correction in flux qubits

et al. 2021

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Quantum annealers require accurate control and optimized operation schemes to reduce noise levels, in order to eventually demonstrate a computational advantage over classical algorithms. We study a high coherence four-junction capacitively shunted flux qubit (CSFQ), using dispersive measurements to extract system parameters and model the device. Josephson junction asymmetry inherent to the device causes a deleterious nonlinear cross-talk when annealing the qubit. We implement a nonlinear annealing path to correct the asymmetry in situ, resulting in a substantial increase in the probability of the qubit being in the correct state given an applied flux bias. We also confirm the multi-level structure of our CSFQ circuit model by annealing it through small spectral gaps and observing quantum signatures of energy level crossings. Our results demonstrate an anneal-path correction scheme designed and implemented to improve control accuracy for high-coherence and high-control quantum annealers, which leads to an enhancement of success probability in annealing protocols.

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Fast, Lifetime-Preserving Readout for High-Coherence Quantum Annealers

Cited by 14 publications

References 42 publications

Quantum adiabatic machine learning by zooming into a region of the energy surface

Quantum adiabatic machine learning by zooming into a region of the energy surface

Demonstration of long-range correlations via susceptibility measurements in a one-dimensional superconducting Josephson spin chain

Anneal-path correction in flux qubits

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