Training of quantum circuits on a hybrid quantum computer

Zhu, Daiwei; Linke, Norbert; Benedetti, Marcello; Landsman, K. A.; Nguyen, Nhung H.; Alderete, C. Huerta; Perdomo-Ortiz, Alejandro; Korda, Nathan; Garfoot, A.; Brecque, Charles; Egan, Laird; Perdómo, Oscar; Monroe, Christopher

doi:10.1126/sciadv.aaw9918

Cited by 224 publications

(189 citation statements)

References 36 publications

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“…In their simulations they successfully train models for the canoni-cal Bars-and-Stripes dataset and for Boltzmann distributions, and use them to design a performance indicator for hybrid quantum-classical systems. Zhu et al [22] implement this schema on four qubits of an actual trapped ion computer and experimentally demonstrate convergence of the model to the target distribution.…”

Section: B Generative Modelingmentioning

confidence: 99%

Parameterized quantum circuits as machine learning models

Benedetti

Lloyd

Sack

et al. 2019

Quantum Sci. Technol.

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713

495

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Hybrid quantum-classical systems make it possible to utilize existing quantum computers to their fullest extent. Within this framework, parameterized quantum circuits can be thought of as machine learning models with remarkable expressive power. This Review presents components of these models and discusses their application to a variety of data-driven tasks such as supervised learning and generative modeling. With experimental demonstrations carried out on actual quantum hardware, and with software actively being developed, this rapidly growing field could become one of the first instances of quantum computing that addresses real world problems.

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Section: B Generative Modelingmentioning

confidence: 99%

Parameterized quantum circuits as machine learning models

Benedetti

Lloyd

Sack

et al. 2019

Quantum Sci. Technol.

Self Cite

713

495

View full text Add to dashboard Cite

show abstract

“…While these results are specific to a particular quantum chemistry problem and the trapped-ion QC hardware, the computational methodology we develop is completely general to simulating quantum systems. We anticipate that similar advances can be applied to other optimization problems that work on variational methods, such as the quantum approximate optimization algorithm [42] and various quantum machine learning applications [43,44]. Increased attention to co-design principles like those demonstrated here will be necessary to push the boundary of possibility in near-term quantum computation.…”

Section: Discussionmentioning

confidence: 94%

Ground-state energy estimation of the water molecule on a trapped-ion quantum computer

et al. 2020

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Quantum computing leverages the quantum resources of superposition and entanglement to efficiently solve computational problems considered intractable for classical computers. Examples include calculating molecular and nuclear structure, simulating strongly-interacting electron systems, and modeling aspects of material function. While substantial theoretical advances have been made in mapping these problems to quantum algorithms, there remains a large gap between the resource requirements for solving such problems and the capabilities of currently available quantum hardware. Bridging this gap will require a co-design approach, where the expression of algorithms is developed in conjunction with the hardware itself to optimize execution. Here, we describe a scalable co-design framework for solving chemistry problems on a trapped ion quantum computer, and apply it to compute the ground-state energy of the water molecule. The robust operation of the trapped ion quantum computer yields energy estimates with errors approaching the chemical accuracy, which is the target threshold necessary for predicting the rates of chemical reaction dynamics.

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“…(In some cases, algorithmic differentiation techniques may provide gradient information [36].) Since gradient-based methods can be sensitive to noise [44], they may be less suitable for noisy intermediate-scale quantum hardware.…”

Section: A Parameter Optimization In Hybrid Algorithmsmentioning

confidence: 99%

Multistart Methods for Quantum Approximate optimization

Shaydulin

Safro

Larson

2019

2019 IEEE High Performance Extreme Computing Conference (HPEC)

103

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Hybrid quantum-classical algorithms such as the quantum approximate optimization algorithm (QAOA) are considered one of the most promising approaches for leveraging near-term quantum computers for practical applications. Such algorithms are often implemented in a variational form, combining classical optimization methods with a quantum machine to find parameters to maximize performance. The quality of the QAOA solution depends heavily on quality of the parameters produced by the classical optimizer. Moreover, the presence of multiple local optima in the space of parameters makes it harder for the classical optimizer. In this paper we study the use of a multistart optimization approach within a QAOA framework to improve the performance of quantum machines on important graph clustering problems. We also demonstrate that reusing the optimal parameters from similar problems can improve the performance of classical optimization methods, expanding on similar results for MAXCUT.

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Training of quantum circuits on a hybrid quantum computer

Cited by 224 publications

References 36 publications

Parameterized quantum circuits as machine learning models

Parameterized quantum circuits as machine learning models

Ground-state energy estimation of the water molecule on a trapped-ion quantum computer

Multistart Methods for Quantum Approximate optimization

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