Quantum natural gradient generalised to noisy and non-unitary circuits

Koczor, Bálint; Benjamin, Simon C.

doi:10.48550/arxiv.1912.08660

Cited by 42 publications

(94 citation statements)

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“…The SWAP-test is an elementary subroutine crucial for the implementation of a number of important algorithms, which include the following. (a) finding excited states of quantum systems, such as in quantum chemistry [85,86]; (b) Simulating quantum dynamics of mixed quantum states and general processes [87,88]; (c) implementing the quantum natural gradient optimisation approach in variational quantum eigensolvers and in other variational quantum algorithms [89][90][91].…”

Section: Other Applicationsmentioning

confidence: 99%

Multicore Quantum Computing

Jnane,

Undseth,

Cai

et al. 2022

Preprint

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Any architecture for practical quantum computing must be scalable. An attractive approach is to create multiple cores, computing regions of fixed size that are well-spaced but interlinked with communication channels. This exploded architecture can relax the demands associated with a single monolithic device: the complexity of control, cooling and power infrastructure as well as the difficulties of cross-talk suppression and near-perfect component yield. Here we explore interlinked multicore architectures through analytic and numerical modelling. While elements of our analysis are relevant to diverse platforms, our focus is on semiconductor electron spin systems in which numerous cores may exist on a single chip. We model shuttling and microwave-based interlinks and estimate the achievable fidelities, finding values that are encouraging but markedly inferior to intra-core operations. We therefore introduce optimsed entanglement purification to enable highfidelity communication, finding that 99.5% is a very realistic goal. We then assess the prospects for quantum advantage using such devices in the NISQ-era and beyond: we simulate recently proposed exponentially-powerful error mitigation schemes in the multicore environment and conclude that these techniques impressively suppress imperfections in both the inter-and intra-core operations.

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Section: Other Applicationsmentioning

confidence: 99%

Multicore Quantum Computing

Jnane,

Undseth,

Cai

et al. 2022

Preprint

Self Cite

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“…[37]. In addition, we mention that other methods [56-63, 72, 73], such as quantum natural gradient [56,57] and L-BFGS method [58,59], have been introduced to obtain the gradients as well in the literature. Different methods bears their pros and cons, and the choice of which one to use depends on the specific problem in practice.…”

Section: Variational Quantum Classifiersmentioning

confidence: 99%

Recent advances for quantum classifiers

Li,

Deng

2021

Preprint

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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 machine, quantum kernel methods, quantum decision tree, and quantum nearest neighbor algorithm. 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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“…have been employed and benchmarked in VQA applications [32][33][34]. Their often poor performance has led researchers to explore a new field, quantum-aware optimizers [33,[35][36][37][38][39][40][41][42], which aim to tailor the optimizer to the idiosyncrasies of VQAs. In the VQA setting, optimizers that use gradient information in theory offer improved convergence over those that do not [43].…”

Section: Introductionmentioning

confidence: 99%

Adaptive shot allocation for fast convergence in variational quantum algorithms

Gu¹,

Lowe²,

Dub³

et al. 2021

Preprint

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Variational Quantum Algorithms (VQAs) are a promising approach for practical applications like chemistry and materials science on near-term quantum computers as they typically reduce quantum resource requirements. However, in order to implement VQAs, an efficient classical optimization strategy is required. Here we present a new stochastic gradient descent method using an adaptive number of shots at each step, called the global Coupled Adaptive Number of Shots (gCANS) method, which improves on prior art in both the number of iterations as well as the number of shots required. These improvements reduce both the time and money required to run VQAs on current cloud platforms. We analytically prove that in a convex setting gCANS achieves geometric convergence to the optimum. Further, we numerically investigate the performance of gCANS on some chemical configuration problems. We also consider finding the ground state for an Ising model with different numbers of spins to examine the scaling of the method. We find that for these problems, gCANS compares favorably to all of the other optimizers we consider.

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Quantum natural gradient generalised to noisy and non-unitary circuits

Cited by 42 publications

References 0 publications

Multicore Quantum Computing

Multicore Quantum Computing

Recent advances for quantum classifiers

Adaptive shot allocation for fast convergence in variational quantum algorithms

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