Hybrid Quantum-Classical Algorithm for Robust Optimization via Stochastic-Gradient Online Learning [0.03%]
Debbie Lim,Joao F Doriguello,Patrick Rebentrost
Debbie Lim
Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous settings have catered to a rich source of research problems...
Benchmarking the operation of quantum heuristics and Ising machines: scoring parameter setting strategies on optimization applications [0.03%]
基准测试量子启发式和伊辛机器的操作：在优化应用中评估评分参数设置策略
David E Bernal Neira,Robin Brown,Pratik Sathe et al.
David E Bernal Neira et al.
We discuss guidelines for evaluating the performance of parameterized stochastic solvers for optimization problems, with particular attention to systems that employ novel hardware, such as digital quantum processors running variational algo...
Unsupervised beyond-standard-model event discovery at the LHC with a novel quantum autoencoder [0.03%]
用新型量子自动编码器在LHC上无监督超越标准模型事件发现
Callum Duffy,Mohammad Hassanshahi,Marcin Jastrzebski et al.
Callum Duffy et al.
This study explores the potential of unsupervised anomaly detection for identifying physics beyond the standard model that may appear at proton collisions at the Large Hadron Collider. We introduce a novel quantum autoencoder circuit ansatz...
Massimo Pregnolato,Paola Zizzi
Massimo Pregnolato
We describe the binding between the glycoprotein Spike of SARS-CoV-2 and the human host cell receptor ACE2 as a quantum circuit, comprising the one-qubit Hadamard quantum logic gate performing the quantum superposition of the S1 subunit of ...
Vanda Azevedo,Carla Silva,Inês Dutra
Vanda Azevedo
One of the areas with the potential to be explored in quantum computing (QC) is machine learning (ML), giving rise to quantum machine learning (QML). In an era when there is so much data, ML may benefit from either speed, complexity or smal...
Quantum-accessible reinforcement learning beyond strictly epochal environments [0.03%]
超越严格纪元环境的量子可访问强化学习
A Hamann,V Dunjko,S Wölk
A Hamann
In recent years, quantum-enhanced machine learning has emerged as a particularly fruitful application of quantum algorithms, covering aspects of supervised, unsupervised and reinforcement learning. Reinforcement learning offers numerous opt...
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes [0.03%]
马尔可夫决策过程的投影仿真增强学习的收敛性分析
W L Boyajian,J Clausen,L M Trenkwalder et al.
W L Boyajian et al.
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervised learning were established. The first framework in which wa...
Implementation of a Hamming distance-like genomic quantum classifier using inner products on ibmqx2 and ibmq_16_melbourne [0.03%]
利用内积在ibmqx2和ibmq_16_melbourne实现类似汉明距离的基因组量子分类器
Kunal Kathuria,Aakrosh Ratan,Michael McConnell et al.
Kunal Kathuria et al.
Motivated by the problem of classifying individuals with a disease versus controls using a functional genomic attribute as input, we present relatively efficient general purpose inner product-based kernel classifiers to classify the test as...