Quantum optimization with a novel Gibbs objective function and ansatz architecture search

Li, Li; Fan, Minjie; Coram, Marc; Riley, Patrick; Leichenauer, Stefan

doi:10.1103/physrevresearch.2.023074

Cited by 107 publications

(85 citation statements)

References 26 publications

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“…Another consideration is that various other modifications to QAOA such as Li et al [11], which modifies the objective function, can be combined with bang-bang QAOA. One compelling modification could involve the ability to apply a Hamiltonian for negative time, corresponding to negative {β i } and {γ i } parameters in standard QAOA such that total time becomes T = i |β i | + |γ i | [22].…”

Section: Discussionmentioning

confidence: 99%

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Investigating quantum approximate optimization algorithms under bang-bang protocols

Liang

Leichenauer³

2020

Phys. Rev. Research

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The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices. We analyze the algorithm as a bang-bang protocol with fixed total time and a randomized greedy optimization scheme. We investigate the performance of bang-bang QAOA on MAX-2-SAT, finding the appearance of phase transitions with respect to the total time. As the total time increases, the optimal bang-bang protocol experiences a number of jumps and plateaus in performance, which match up with an increasing number of switches in the standard QAOA formulation. At large times, it becomes more difficult to find a globally optimal bang-bang protocol and performances suffer. We also investigate the effects of changing the initial conditions of the randomized optimization algorithm and see that better local optima can be found by using an adiabatic initialization.

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Section: Discussionmentioning

confidence: 99%

“…We will first describe the standard QAOA as given by Farhi et al [1], followed by our bang-bang QAOA modification. Note that there exist a wide variety of other interesting modifications to the standard QAOA [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Investigating quantum approximate optimization algorithms under bang-bang protocols

Liang

Leichenauer³

2020

Phys. Rev. Research

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“…The following is a review of the works in this area. Fan et al 30 proposed a quantum approximate optimization algorithm, which is a standard method for combinatorial optimization with a gate-based quantum computer. The paper introduced a new Gibbs objective function and demonstrated its superiority, and used the architecture of an Ansatz search algorithm to search for the discrete space of a quantum circuit.…”

Section: Quantum Circuits Optimization Techniquesmentioning

confidence: 99%

An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

Salehi

Zomorodi‐Moghadam

Pławiak

et al. 2022

Preprint

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Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. Itmakes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computingcan be used to implement many existing problems in different fields incredibly effectively. One important field that quantumcomputing has shown great results in machine learning. Until now, many different quantum algorithms have been presented toperform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will bereduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computationtime, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since thesealgorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms ofquantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimizequantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach includingdifferent optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms forbig data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original onesand by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% indifferent circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction forone iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the maincircuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost andperformance are improved.

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“…Some other works use special structures inspired by physics to build quantum classifiers, e.g., the tree tensor network classifiers, the multi-scale entanglement renormalization ansatz classifiers [51] and the multi-level quantum system classifiers [55]. It is worth mentioning that there are algorithms that search for appropriate structures of the variational quantum circuits for certain tasks [121][122][123][124][125][126][127][128][129][130][131][132][133][134][135][136][137], e.g., a quantum neu-220301-3 roevolution algorithm that autonomously finds suitable quantum neural networks [126] and a differentiable quantum architecture search algorithm that allows automated quantum circuit designs in an end-to-end differentiable fashion [123].…”

Section: Introductionmentioning

confidence: 99%

Recent advances for quantum classifiers

Deng

2021

Sci. China Phys. Mech. Astron.

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Machine learning has achieved dramatic success in a broad spectrum of applications. Its interplay with quantum physics may lead to unprecedented perspectives for both fundamental research and commercial applications, giving rise to an emergent research frontier of quantum machine learning. Along this line, quantum classifiers, which are quantum devices that aim to solve classification problems in machine learning, have attracted tremendous attention recently. In this review, we give a relatively comprehensive overview for the studies of quantum classifiers, with a focus on recent advances. First, we will review a number of quantum classification algorithms, including quantum support vector machines, quantum kernel methods, quantum decision tree classifiers, quantum nearest neighbor algorithms, and quantum annealing based classifiers. Then, we move on to introduce the variational quantum classifiers, which are essentially variational quantum circuits for classifications. We will review different architectures for constructing variational quantum classifiers and introduce the barren plateau problem, where the training of quantum classifiers might be hindered by the exponentially vanishing gradient. In addition, the vulnerability aspect of quantum classifiers in the setting of adversarial learning and the recent experimental progress on different quantum classifiers will also be discussed.

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Quantum optimization with a novel Gibbs objective function and ansatz architecture search

Cited by 107 publications

References 26 publications

Investigating quantum approximate optimization algorithms under bang-bang protocols

Investigating quantum approximate optimization algorithms under bang-bang protocols

An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

Recent advances for quantum classifiers

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