#32: July 24th- July 30th





Clustering and enhanced classification using a hybrid quantum autoencoder

Maiyuren Srikumar,Charles D. Hill,Lloyd C.L. HollenbergJul 27 2021 quant-ph arXiv:2107.11988v1

Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify, and classically represent, their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states – which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

RGB Image Classification with Quantum Convolutional Ansaetze

Yu Jing,Yang Yang,Chonghang Wu,Wenbing Fu,Wei Hu,Xiaogang Li,Hua XuJul 26 2021 quant-phcs.CVcs.LG arXiv:2107.11099v1

With the rapid growth of qubit numbers and coherence times in quantum hardware technology, implementing shallow neural networks on the so-called Noisy Intermediate-Scale Quantum (NISQ) devices has attracted a lot of interest. Many quantum (convolutional) circuit ansaetze are proposed for grayscale images classification tasks with promising empirical results. However, when applying these ansaetze on RGB images, the intra-channel information that is useful for vision tasks is not extracted effectively. In this paper, we propose two types of quantum circuit ansaetze to simulate convolution operations on RGB images, which differ in the way how inter-channel and intra-channel information are extracted. To the best of our knowledge, this is the first work of a quantum convolutional circuit to deal with RGB images effectively, with a higher test accuracy compared to the purely classical CNNs. We also investigate the relationship between the size of quantum circuit ansatz and the learnability of the hybrid quantum-classical convolutional neural network. Through experiments based on CIFAR-10 and MNIST datasets, we demonstrate that a larger size of the quantum circuit ansatz improves predictive performance in multiclass classification tasks, providing useful insights for near term quantum algorithm developments.

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

B. Severin,D. T. Lennon,L. C. Camenzind,F. Vigneau,F. Fedele,D. Jirovec,A. Ballabio,D. Chrastina,G. Isella,M. de Kruijf,M. J. Carballido,S. Svab,A. V. Kuhlmann,F. R. Braakman,S. Geyer,F. N. M. Froning,H. Moon,M. A. Osborne,D. Sejdinovic,G. Katsaros,et al (3)Jul 28 2021 cond-mat.mes-hallcs.LGquant-ph arXiv:2107.12975v1

The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.

Performance analysis of a hybrid agent for quantum-accessible reinforcement learning

Arne Hamann,Sabine WölkJul 30 2021 quant-ph arXiv:2107.14001v1

In the last decade quantum machine learning has provided fascinating and fundamental improvements to supervised, unsupervised and reinforcement learning. In reinforcement learning, a so-called agent is challenged to solve a task given by some environment. The agent learns to solve the task by exploring the environment and exploiting the rewards it gets from the environment. For some classical task environments, such as deterministic strictly epochal environments, an analogue quantum environment can be constructed which allows to find rewards quadratically faster by applying quantum algorithms. In this paper, we analytically analyze the behavior of a hybrid agent which combines this quadratic speedup in exploration with the policy update of a classical agent. This leads to a faster learning of the hybrid agent compared to the classical agent. We demonstrate that if the classical agent needs on average ⟨J⟩⟨J⟩ rewards and ⟨T⟩c⟨T⟩c epochs to learn how to solve the task, the hybrid agent will take ⟨T⟩q≤α⟨T⟩c⟨J⟩−−−−−−√⟨T⟩q≤α⟨T⟩c⟨J⟩ epochs on average. Here, αα denotes a constant which is independent of the problem size. Additionally, we prove that if the environment allows for maximally αokmaxαokmax sequential coherent interactions, e.g. due to noise effects, an improvement given by ⟨T⟩q≈αo⟨T⟩c/4kmax⟨T⟩q≈αo⟨T⟩c/4kmax is still possible.

Machine learning identification of symmetrized base states of Rydberg atoms

Daryl Ryan Chong,Minhyuk Kim,Jaewook Ahn,Heejeong JeongJul 30 2021 quant-phphysics.atom-ph arXiv:2107.13745v1

Studying the complex quantum dynamics of interacting many-body systems is one of the most challenging areas in modern physics. Here, we use machine learning (ML) models to identify the symmetrized base states of interacting Rydberg atoms of various atom numbers (up to six) and geometric configurations. To obtain the data set for training the ML classifiers, we generate Rydberg excitation probability profiles that simulate experimental data by utilizing Lindblad equations that incorporate laser intensities and phase noise. Then, we classify the data sets using support vector machines (SVMs) and random forest classifiers (RFCs). With these ML models, we achieve high accuracy of up to 100% for data sets containing only a few hundred samples, especially for the closed atom configurations such as the pentagonal (five atoms) and hexagonal (six atoms) systems. The results demonstrate that computationally cost-effective ML models can be used in the identification of Rydberg atom configurations.

Quantum Annealing Algorithms for Boolean Tensor Networks

Elijah Pelofske,Georg Hahn,Daniel O’Malley,Hristo N. Djidjev,Boian S. AlexandrovJul 30 2021 quant-ph arXiv:2107.13659v1

Quantum annealers manufactured by D-Wave Systems, Inc., are computational devices capable of finding high-quality solutions of NP-hard problems. In this contribution, we explore the potential and effectiveness of such quantum annealers for computing Boolean tensor networks. Tensors offer a natural way to model high-dimensional data commonplace in many scientific fields, and representing a binary tensor as a Boolean tensor network is the task of expressing a tensor containing categorical (i.e., {0,1}{0,1}) values as a product of low dimensional binary tensors. A Boolean tensor network is computed by Boolean tensor decomposition, and it is usually not exact. The aim of such decomposition is to minimize the given distance measure between the high-dimensional input tensor and the product of lower-dimensional (usually three-dimensional) tensors and matrices representing the tensor network. In this paper, we introduce and analyze three general algorithms for Boolean tensor networks: Tucker, Tensor Train, and Hierarchical Tucker networks. The computation of a Boolean tensor network is reduced to a sequence of Boolean matrix factorizations, which we show can be expressed as a quadratic unconstrained binary optimization problem suitable for solving on a quantum annealer. By using a novel method we introduce called parallel quantum annealing, we demonstrate that tensor with up to millions of elements can be decomposed efficiently using a DWave 2000Q adiabatic quantum annealer.

Freedom of mixer rotation-axis improves performance in the quantum approximate optimization algorithm

L. C. G. Govia,C. Poole,M. Saffman,H. K. KroviJul 29 2021 quant-ph arXiv:2107.13129v1

Variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA) are particularly attractive candidates for implementation on near-term quantum processors. As hardware realities such as error and qubit connectivity will constrain achievable circuit depth in the near future, new ways to achieve high-performance at low depth are of great interest. In this work, we present a modification to QAOA that adds additional variational parameters in the form of freedom of the rotation-axis in the XYXY-plane of the mixer Hamiltonian. Via numerical simulation, we show that this leads to a drastic performance improvement over standard QAOA at finding solutions to the MAXCUT problem on graphs of up to 7 qubits. Furthermore, we explore the Z-phase error mitigation properties of our modified ansatz, its performance under a realistic error model for a neutral atom quantum processor, and the class of problems it can solve in a single round.

Quantum computing for classical problems: Variational Quantum Eigensolver for activated processes

Pierpaolo Pravatto,Davide Castaldo,Federico Gallina,Barbara Fresch,Stefano Corni,Giorgio J. MoroJul 29 2021 quant-phcond-mat.stat-mechphysics.chem-phphysics.comp-ph arXiv:2107.13025v1

The theory of stochastic processes impacts both physical and social sciences. At the molecular scale, stochastic dynamics is ubiquitous because of thermal fluctuations. The Fokker-Plank-Smoluchowski equation models the time evolution of the probability density of selected degrees of freedom in the diffusive regime and it is therefore a workhorse of physical chemistry. In this paper we report the development and implementation of a Variational Quantum Eigensolver procedure to solve the Fokker-Planck-Smoluchowski eigenvalue problem. We show that such an algorithm, typically adopted to address quantum chemistry problems, can be applied effectively to classical systems paving the way to new applications of quantum computers. We compute the conformational transition rate in a linear chain of rotors experiencing nearest-neighbour interaction. We provide a method to encode on the quantum computer the probability distribution for a given conformation of the chain and assess its scalability in terms of operations. Performance analysis on noisy quantum emulators and quantum devices (IBMQ Santiago) is provided for a small chain showing results in good agreement with the classical benchmark without further addition of any error mitigation technique.

Analytic energy gradient for state-averaged orbital-optimized variational quantum eigensolvers and its application to a photochemical reaction

Keita Arimitsu,Yuya O. Nakagawa,Sho Koh,Wataru Mizukami,Qi Gao,Takao KobayashiJul 28 2021 physics.chem-phcond-mat.str-elquant-ph arXiv:2107.12705v1

PDFElucidating photochemical reactions is vital to understand various biochemical phenomena and develop functional materials such as artificial photosynthesis and organic solar cells, albeit its notorious difficulty by both experiments and theories. The best theoretical way so far to analyze photochemical reactions at the level of ab initio electronic structure is the state-averaged multi-configurational self-consistent field (SA-MCSCF) method. However, the exponential computational cost of classical computers with the increasing number of molecular orbitals hinders applications of SA-MCSCF for large systems we are interested in. Utilizing quantum computers was recently proposed as a promising approach to overcome such computational cost, dubbed as SA orbital-optimized variational quantum eigensolver (SA-OO-VQE). Here we extend a theory of SA-OO-VQE so that analytical gradients of energy can be evaluated by standard techniques that are feasible with near-term quantum computers. The analytical gradients, known only for the state-specific OO-VQE in previous studies, allow us to determine various characteristics of photochemical reactions such as the minimal energy (ME) points and the conical intersection (CI) points. We perform a proof-of-principle calculation of our methods by applying it to the photochemical \it cis-trans isomerization of 1,3,3,3-tetrafluoropropene. Numerical simulations of quantum circuits and measurements can correctly capture the photochemical reaction pathway of this model system, including the ME and CI points. Our results illustrate the possibility of leveraging quantum computers for studying photochemical reactions.

Categories: Week-in-QML


Leave a Reply

Your email address will not be published. Required fields are marked *