Papers
Live trends in quantum computing research, updated daily from arXiv.
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Hardware platform mentions in abstracts — Photonic leads
Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks
M. Cerezo, A. Sone, Tyler Volkoff +2 more·Jan 2, 2020
Variational quantum algorithms (VQAs) optimize the parameters $\boldsymbol{\theta}$ of a quantum neural network $V(\boldsymbol{\theta})$ to minimize a cost function $C$. While VQAs may enable practical applications of noisy quantum computers, they ar...
Deep Q-learning decoder for depolarizing noise on the toric code
David Fitzek, Mattias Eliasson, A. F. Kockum +1 more·Dec 30, 2019
We present an AI-based decoding agent for quantum error correction of depolarizing noise on the toric code. The agent is trained using deep reinforcement learning (DRL), where an artificial neural network encodes the state-action Q-values of error-co...
QDNN: DNN with Quantum Neural Network Layers
Chen Zhao, Xiao-Shan Gao·Dec 29, 2019
The deep neural network (DNN) became the most important and powerful machine learning method in recent years. In this paper, we introduce a general quantum DNN, which consists of fully quantum structured layers with better representation power than t...
Quantum implementation of an artificial feed-forward neural network
F. Tacchino, P. Barkoutsos, C. Macchiavello +3 more·Dec 28, 2019
Artificial intelligence algorithms largely build on multi-layered neural networks. Coping with their increasing complexity and memory requirements calls for a paradigmatic change in the way these powerful algorithms are run. Quantum computing promise...
Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits
Jin-Guo Liu, Li-xin Mao, Pan Zhang +1 more·Dec 24, 2019
We extend the ability of an unitary quantum circuit by interfacing it with a classical autoregressive neural network. The combined model parametrizes a variational density matrix as a classical mixture of quantum pure states, where the autoregressive...
Evaluation of the spectrum of a quantum system using machine learning based on incomplete information about the wavefunctions
G. Burlak·Dec 24, 2019
We propose an effective approach to rapid estimation of the energy spectrum of quantum systems with the use of machine learning (ML) algorithm. In the ML approach (back propagation), the wavefunction data known from experiments is interpreted as the ...
Transfer learning in hybrid classical-quantum neural networks
A. Mari, T. Bromley, J. Izaac +2 more·Dec 17, 2019
We extend the concept of transfer learning, widely applied in modern machine learning algorithms, to the emerging context of hybrid neural networks composed of classical and quantum elements. We propose different implementations of hybrid transfer le...
Learning Non-Markovian Quantum Noise from Moiré-Enhanced Swap Spectroscopy with Deep Evolutionary Algorithm
M. Niu, Vadim N. Smelyanskyi, P. Klimov +30 more·Dec 9, 2019
Two-level-system (TLS) defects in amorphous dielectrics are a major source of noise and decoherence in solid-state qubits. Gate-dependent non-Markovian errors caused by TLS-qubit coupling are detrimental to fault-tolerant quantum computation and have...
Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations
Chang-Yu Hsieh, Qiming Sun, Shengyu Zhang +1 more·Dec 6, 2019
Neural-network quantum state (NQS) has attracted significant interests as a powerful wave-function ansatz to model quantum phenomena. In particular, a variant of NQS based on the restricted Boltzmann machine (RBM) has been adapted to model the ground...
Contracting Arbitrary Tensor Networks: General Approximate Algorithm and Applications in Graphical Models and Quantum Circuit Simulations.
Feng Pan, Pengfei Zhou, Sujie Li +1 more·Dec 6, 2019
We present a general method for approximately contracting tensor networks with an arbitrary connectivity. This enables us to release the computational power of tensor networks to wide use in inference and learning problems defined on general graphs. ...
Automated Tuning of Double Quantum Dots into Specific Charge States Using Neural Networks
Renato Durrer, B. Kratochwil, J. Koski +5 more·Dec 5, 2019
While quantum dots are at the forefront of quantum device technology, tuning multi-dot systems requires a lengthy experimental process as multiple parameters need to be accurately controlled. This process becomes increasingly time-consuming and diffi...
A path towards quantum advantage in training deep generative models with quantum annealers
Walter Winci, L. Buffoni, Hossein Sadeghi +3 more·Dec 4, 2019
The development of quantum-classical hybrid (QCH) algorithms is critical to achieve state-of-the-art computational models. A QCH variational autoencoder (QVAE) was introduced in reference [] by some of the authors of this paper. QVAE consists of a cl...
Hybrid quantum-classical convolutional neural networks
Junhua Liu, Kwan Hui Lim, Kristin L. Wood +3 more·Nov 8, 2019
Deep learning has been shown to be able to recognize data patterns better than humans in specific circumstances or contexts. In parallel, quantum computing has demonstrated to be able to output complex wave functions with a few number of gate operati...
Quantum Algorithms for Deep Convolutional Neural Networks
Iordanis Kerenidis, Jonas Landman, A. Prakash·Nov 4, 2019
Quantum computing is a new computational paradigm that promises applications in several fields, including machine learning. In the last decade, deep learning, and in particular Convolutional neural networks (CNN), have become essential for applicatio...
Cancer Detection Using Quantum Neural Networks: A Demonstration on a Quantum Computer
Nilima Mishra, Aradh Bisarya, Shubham Kumar +3 more·Nov 1, 2019
Artificial intelligence and machine learning paves the way to achieve greater technical feats. In this endeavor to hone these techniques, quantum machine learning is budding to serve as an important tool. Using the techniques of deep learning and sup...
Quantum state discrimination using noisy quantum neural networks
A. Patterson, Hongxiang Chen, Leonard Wossnig +3 more·Nov 1, 2019
Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (QNN) for state discrimination, applicable on near-term quantum devices ...
A framework for deep energy-based reinforcement learning with quantum speed-up
S. Jerbi, Hendrik Poulsen Nautrup, Lea M. Trenkwalder +2 more·Oct 28, 2019
In the past decade, deep learning methods have seen tremendous success in various supervised and unsupervised learning tasks such as classification and generative modeling. More recently, deep neural networks have emerged in the domain of reinforceme...
Classical Quantum Optimization with Neural Network Quantum States.
Joseph Gomes, K. McKiernan, P. Eastman +1 more·Oct 23, 2019
The classical simulation of quantum systems typically requires exponential resources. Recently, the introduction of a machine learning-based wavefunction ansatz has led to the ability to solve the quantum many-body problem in regimes that had previou...
A simple approach to design quantum neural networks and its applications to kernel-learning methods
Changpeng Shao·Oct 19, 2019
We give an explicit simple method to build quantum neural networks (QNNs) to solve classification problems. Besides the input (state preparation) and output (amplitude estimation), it has one hidden layer which uses a tensor product of $\log M$ two-d...
Precise measurement of quantum observables with neural-network estimators
G. Torlai, G. Mazzola, Giuseppe Carleo +1 more·Oct 16, 2019
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware. This fundamental limitation is particularly severe for quantum algorithms where co...