#38 Sept 4th -10th




Qiskit 0.29.1

  • Increased the qiskit-terra dependency version to the latest release 0.18.2
  • Increased the qiskit-aqua dependency version to the latest release 0.9.5

Quil-Rust v0.5.5

  • impl Hash and Eq for Expression for use in deduplication.

Amazon Braket v1.9.0

  • Verbatim boxes


Entangled Datasets for Quantum Machine Learning

Louis Schatzki,Andrew Arrasmith,Patrick J. Coles,M. CerezoSep 09 2021 quant-phcs.LGstat.ML arXiv:2109.03400v1

High-quality, large-scale datasets have played a crucial role in the development and success of classical machine learning. Quantum Machine Learning (QML) is a new field that aims to use quantum computers for data analysis, with the hope of obtaining a quantum advantage of some sort. While most proposed QML architectures are benchmarked using classical datasets, there is still doubt whether QML on classical datasets will achieve such an advantage. In this work, we argue that one should instead employ quantum datasets composed of quantum states. For this purpose, we introduce the NTangled dataset composed of quantum states with different amounts and types of multipartite entanglement. We first show how a quantum neural network can be trained to generate the states in the NTangled dataset. Then, we use the NTangled dataset to benchmark QML models for supervised learning classification tasks. We also consider an alternative entanglement-based dataset, which is scalable and is composed of states prepared by quantum circuits with different depths. As a byproduct of our results, we introduce a novel method for generating multipartite entangled states, providing a use-case of quantum neural networks for quantum entanglement theory.

Variational quantum amplitude estimation

Kirill Plekhanov,Matthias Rosenkranz,Mattia Fiorentini,Michael LubaschSep 09 2021 quant-ph arXiv:2109.03687v1

We propose to perform amplitude estimation with the help of constant-depth quantum circuits that variationally approximate states during amplitude amplification. In the context of Monte Carlo (MC) integration, we numerically show that shallow circuits can accurately approximate many amplitude amplification steps. We combine the variational approach with maximum likelihood amplitude estimation [Y. Suzuki et al., Quantum Inf. Process. 19, 75 (2020)] in variational quantum amplitude estimation (VQAE). VQAE can exhibit a cubic quantum speedup over classical MC sampling if the variational cost is ignored. If this cost is taken into account, VQAE typically has larger computational requirements than classical MC sampling. To reduce the variational cost, we propose adaptive VQAE and numerically show that it can outperform classical MC sampling.

Quantum-enhanced neural networks in the neural tangent kernel framework

Kouhei Nakaji,Hiroyuki Tezuka,Naoki YamamotoSep 09 2021 quant-ph arXiv:2109.03786v1

Recently quantum neural networks or quantum-classical neural networks (QCNN) have been actively studied, as a possible alternative to the conventional classical neural network (CNN), but their practical and theoretically-guaranteed performance is still to be investigated. On the other hand, CNNs and especially the deep CNNs, have acquired several solid theoretical basis; one of those significant basis is the neural tangent kernel (NTK) theory, which indeed can successfully explain the mechanism of various desirable properties of CNN, e.g., global convergence and good generalization properties. In this paper, we study a class of QCNN where NTK theory can be directly applied. The output of the proposed QCNN is a function of the projected quantum kernel, in the limit of large number of nodes of the CNN part; hence this scheme may have a potential quantum advantage. Also, because the parameters can be tuned only around the initial random variables chosen from unitary 2-design and Gaussian distributions, the proposed QCNN casts as a scheme that realizes the quantum kernel method with less computational complexity. Moreover, NTK is identical to the covariance matrix of a Gaussian process, which allows us to analytically study the learning process and as a consequence to have a condition of the dataset such that QCNN may perform better than the classical correspondence. These properties are all observed in a thorough numerical experiment.

Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

Owen LockwoodSep 08 2021 cs.LGquant-ph arXiv:2109.03188v1

Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices. However, the optimization of quantum machine learning models presents numerous challenges arising from the imperfections of hardware and the fundamental obstacles in navigating an exponentially scaling Hilbert space. In this work, we evaluate the potential of contemporary methods in deep reinforcement learning to augment gradient based optimization routines in quantum variational circuits. We find that reinforcement learning augmented optimizers consistently outperform gradient descent in noisy environments. All code and pretrained weights are available to replicate the results or deploy the models at https://github.com/lockwo/rl_qvc_opt.

A review of Quantum Neural Networks: Methods, Models, Dilemma

Renxin Zhao,Shi WangSep 07 2021 cs.AIquant-ph arXiv:2109.01840v1

The rapid development of quantum computer hardware has laid the hardware foundation for the realization of QNN. Due to quantum properties, QNN shows higher storage capacity and computational efficiency compared to its classical counterparts. This article will review the development of QNN in the past six years from three parts: implementation methods, quantum circuit models, and difficulties faced. Among them, the first part, the implementation method, mainly refers to some underlying algorithms and theoretical frameworks for constructing QNN models, such as VQA. The second part introduces several quantum circuit models of QNN, including QBM, QCVNN and so on. The third part describes some of the main difficult problems currently encountered. In short, this field is still in the exploratory stage, full of magic and practical significance.

Medical image classification via quantum neural networks

Natansh Mathur,Jonas Landman,Yun Yvonna Li,Martin Strahm,Skander Kazdaghli,Anupam Prakash,Iordanis KerenidisSep 07 2021 quant-ph arXiv:2109.01831v1

Machine Learning provides powerful tools for a variety of applications, including disease diagnosis through medical image classification. In recent years, quantum machine learning techniques have been put forward as a way to potentially enhance performance in machine learning applications, both through quantum algorithms for linear algebra and quantum neural networks. In this work, we study two different quantum neural network techniques for medical image classification: first by employing quantum circuits in training of classical neural networks, and second, by designing and training quantum orthogonal neural networks. We benchmark our techniques on two different imaging modalities, retinal color fundus images and chest X-rays. The results show the promises of such techniques and the limitations of current quantum hardware.

Quantum Machine Learning for Finance

Marco Pistoia,Syed Farhan Ahmad,Akshay Ajagekar,Alexander Buts,Shouvanik Chakrabarti,Dylan Herman,Shaohan Hu,Andrew Jena,Pierre Minssen,Pradeep Niroula,Arthur Rattew,Yue Sun,Romina YalovetzkySep 10 2021 quant-phcs.LG arXiv:2109.04298

Quantum computers are expected to surpass the computational capabilities of classical computers during this decade, and achieve disruptive impact on numerous industry sectors, particularly finance. In fact, finance is estimated to be the first industry sector to benefit from Quantum Computing not only in the medium and long terms, but even in the short term. This review paper presents the state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning.

A case study of variational quantum algorithms for a job shop scheduling problem

David Amaro,Matthias Rosenkranz,Nathan Fitzpatrick,Koji Hirano,Mattia FiorentiniSep 09 2021 quant-ph arXiv:2109.03745v1

Combinatorial optimization models a vast range of industrial processes aiming at improving their efficiency. In general, solving this type of problem exactly is computationally intractable. Therefore, practitioners rely on heuristic solution approaches. Variational quantum algorithms are optimization heuristics that can be demonstrated with available quantum hardware. In this case study, we apply four variational quantum heuristics running on IBM’s superconducting quantum processors to the job shop scheduling problem. Our problem optimizes a steel manufacturing process. A comparison on 5 qubits shows that the recent filtering variational quantum eigensolver (F-VQE) converges faster and samples the global optimum more frequently than the quantum approximate optimization algorithm (QAOA), the standard variational quantum eigensolver (VQE), and variational quantum imaginary time evolution (VarQITE). Furthermore, F-VQE readily solves problem sizes of up to 23 qubits on hardware without error mitigation post processing. Combining F-VQE with error mitigation and causal cones could allow quantum optimization heuristics to scale to relevant problem sizes.

Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow

Zhiding Liang,Zhepeng Wang,Junhuan Yang,Lei Yang,Jinjun Xiong,Yiyu Shi,Weiwen JiangSep 09 2021 quant-phcs.LG arXiv:2109.03430v1

In the noisy intermediate-scale quantum (NISQ) era, one of the key questions is how to deal with the high noise level existing in physical quantum bits (qubits). Quantum error correction is promising but requires an extensive number (e.g., over 1,000) of physical qubits to create one “perfect” qubit, exceeding the capacity of the existing quantum computers. This paper aims to tackle the noise issue from another angle: instead of creating perfect qubits for general quantum algorithms, we investigate the potential to mitigate the noise issue for dedicate algorithms. Specifically, this paper targets quantum neural network (QNN), and proposes to learn the errors in the training phase, so that the identified QNN model can be resilient to noise. As a result, the implementation of QNN needs no or a small number of additional physical qubits, which is more realistic for the near-term quantum computers. To achieve this goal, an application-specific compiler is essential: on the one hand, the error cannot be learned if the mapping from logical qubits to physical qubits exists randomness; on the other hand, the compiler needs to be efficient so that the lengthy training procedure can be completed in a reasonable time. In this paper, we utilize the recent QNN framework, QuantumFlow, as a case study. Experimental results show that the proposed approach can optimize QNN models for different errors in qubits, achieving up to 28% accuracy improvement compared with the model obtained by the error-agnostic training.

Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models

Daniel Flam-Shepherd,Tony Wu,Xuemei Gu,Alba Cervera-Lierta,Mario Krenn,Alan Aspuru-GuzikSep 07 2021 cs.LGquant-ph arXiv:2109.02490v1

Quantum physics experiments produce interesting phenomena such as interference or entanglement, which is a core property of numerous future quantum technologies. The complex relationship between a quantum experiment’s structure and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand. We present the first deep generative model of quantum optics experiments where a variational autoencoder (QOVAE) is trained on a dataset of experimental setups. In a series of computational experiments, we investigate the learned representation of the QOVAE and its internal understanding of the quantum optics world. We demonstrate that the QOVAE learns an intrepretable representation of quantum optics experiments and the relationship between experiment structure and entanglement. We show the QOVAE is able to generate novel experiments for highly entangled quantum states with specific distributions that match its training data. Importantly, we are able to fully interpret how the QOVAE structures its latent space, finding curious patterns that we can entirely explain in terms of quantum physics. The results demonstrate how we can successfully use and understand the internal representations of deep generative models in a complex scientific domain. The QOVAE and the insights from our investigations can be immediately applied to other physical systems throughout fundamental scientific research.

Exploration of Quantum Neural Architecture by Mixing Quantum Neuron Designs

Zhepeng Wang,Zhiding Liang,Shanglin Zhou,Caiwen Ding,Jinjun Xiong,Yiyu Shi,Weiwen JiangSep 09 2021 quant-phcs.AI arXiv:2109.03806v1

With the constant increase of the number of quantum bits (qubits) in the actual quantum computers, implementing and accelerating the prevalent deep learning on quantum computers are becoming possible. Along with this trend, there emerge quantum neural architectures based on different designs of quantum neurons. A fundamental question in quantum deep learning arises: what is the best quantum neural architecture? Inspired by the design of neural architectures for classical computing which typically employs multiple types of neurons, this paper makes the very first attempt to mix quantum neuron designs to build quantum neural architectures. We observe that the existing quantum neuron designs may be quite different but complementary, such as neurons from variation quantum circuits (VQC) and Quantumflow. More specifically, VQC can apply real-valued weights but suffer from being extended to multiple layers, while QuantumFlow can build a multi-layer network efficiently, but is limited to use binary weights. To take their respective advantages, we propose to mix them together and figure out a way to connect them seamlessly without additional costly measurement. We further investigate the design principles to mix quantum neurons, which can provide guidance for quantum neural architecture exploration in the future. Experimental results demonstrate that the identified quantum neural architectures with mixed quantum neurons can achieve 90.62% of accuracy on the MNIST dataset, compared with 52.77% and 69.92% on the VQC and QuantumFlow, respectively.

Acceleration Method for Learning Fine-Layered Optical Neural Networks

Kazuo Aoyama,Hiroshi SawadaSep 07 2021 cs.LGphysics.opticsquant-phstat.ML arXiv:2109.01731v1

An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matrix in optical analog circuits. Among them, a circuit with a multiple-layered structure of programmable Mach-Zehnder interferometers (MZIs) can realize a specific class of unitary matrices with a limited number of MZIs as its weight matrix. The circuit is effective for balancing the number of programmable MZIs and ONN performance. However, it takes a lot of time to learn MZI parameters of the circuit with a conventional automatic differentiation (AD), which machine learning platforms are equipped with. To solve the time-consuming problem, we propose an acceleration method for learning MZI parameters. We create customized complex-valued derivatives for an MZI, exploiting Wirtinger derivatives and a chain rule. They are incorporated into our newly developed function module implemented in C++ to collectively calculate their values in a multi-layered structure. Our method is simple, fast, and versatile as well as compatible with the conventional AD. We demonstrate that our method works 20 times faster than the conventional AD when a pixel-by-pixel MNIST task is performed in a complex-valued recurrent neural network with an MZI-based hidden unit.

Genetic-Multi-initial Generalized VQE: Advanced VQE method using Genetic Algorithms then Local Search

Hikaru Wakaura,Takao TomonoSep 07 2021 quant-phphysics.chem-ph arXiv:2109.02009v1

Variational-Quantum-Eigensolver (VQE) method has been known as the method of chemical calculation using quantum computers and classical computers. This method also can derive the energy levels of excited states by Variational-Quantum-Deflation (VQD) method. Although, parameter landscape of excited state have many local minimums that the results are tend to be trapped by them. Therefore, we apply Genetic Algorithms then Local Search (GA then LS) as the classical optimizer of VQE method. We performed the calculation of ground and excited states and their energies on hydrogen molecule by modified GA then LS. Here we uses Powell, Broyden-Fletcher-Goldfarb-Shanno, Nelder-Mead and Newton method as an optimizer of LS. We obtained the result that Newton method can derive ground and excited states and their energies in higher accuracy than others. We are predicting that newton method is more effective for seed up and be more accurate.

Categories: Week-in-QML


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