scholarly journals Variational Quantum Circuit-Based Reinforcement Learning for POMDP and Experimental Implementation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tomoaki Kimura ◽  
Kodai Shiba ◽  
Chih-Chieh Chen ◽  
Masaru Sogabe ◽  
Katsuyoshi Sakamoto ◽  
...  
Keyword(s):  
Time Series ◽  
Reinforcement Learning ◽  
Time Series Analysis ◽  
Supervised Learning ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Practical Applications ◽  
Experimental Implementation ◽  
Partially Observable ◽  
Real Machine

Variational quantum circuit is proposed for applications in supervised learning and reinforcement learning to harness potential quantum advantage. However, many practical applications in robotics and time-series analysis are in partially observable environment. In this work, we propose an algorithm based on variational quantum circuits for reinforcement learning under partially observable environment. Simulations suggest learning advantage over several classical counterparts. The learned parameters are then tested on IBMQ systems to demonstrate the applicability of our approach for real-machine-based predictions.

scholarly journals Efficient and effective quantum compiling for entanglement-based machine learning on IBM Q devices

2018 ◽  
Vol 16 (08) ◽  
pp. 1840006 ◽  
Author(s):  
Davide Ferrari ◽  
Michele Amoretti
Keyword(s):  
Machine Learning ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Entangled States ◽  
Hard Problem ◽  
Practical Applications ◽  
Ghz States ◽  
Quantum Machine Learning ◽  
Uniform Quantum

Quantum compiling means fast, device-aware implementation of quantum algorithms (i.e. quantum circuits, in the quantum circuit model of computation). In this paper, we present a strategy for compiling IBM Q-aware, low-depth quantum circuits that generate Greenberger–Horne–Zeilinger (GHZ) entangled states. The resulting compiler can replace the QISKit compiler for the specific purpose of obtaining improved GHZ circuits. It is well known that GHZ states have several practical applications, including quantum machine learning. We illustrate our experience in implementing and querying a uniform quantum example oracle based on the GHZ circuit, for solving the classically hard problem of learning parity with noise.

scholarly journals Quality Prediction and Yield Improvement in Process Manufacturing Based on Data Analytics

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1068
Author(s):  
Ji-hye Jun ◽  
Tai-Woo Chang ◽  
Sungbum Jun
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Supervised Learning ◽  
Manufacturing Process ◽  
Continuous Flow ◽  
Classification Model ◽  
Production Plan ◽  
Continuous Manufacturing ◽  
Series Analysis ◽  
Discrete Manufacturing

Quality management is important for maximizing yield in continuous-flow manufacturing. However, it is more difficult to manage quality in continuous-flow manufacturing than in discrete manufacturing because partial defects can significantly affect the quality of an entire lot of final product. In this paper, a comprehensive framework that consists of three steps is proposed to predict defects and improve yield by using semi-supervised learning, time-series analysis, and classification model. In Step 1, semi-supervised learning using both labeled and unlabeled data is applied to generate quality values. In addition, feature values are predicted in time-series analysis in Step 2. Finally, in Step 3, we predict quality values based on the data obtained in Step 1 and Step 2 and calculate yield values with the use of the predicted value. Compared to a conventional production plan, the suggested plan increases yield by up to 8.7%. The production plan proposed in this study is expected to contribute to not only the continuous manufacturing process but the discrete manufacturing process. In addition, it can be used in early diagnosis of equipment failure.

scholarly journals Cost function dependent barren plateaus in shallow parametrized quantum circuits

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
M. Cerezo ◽  
Akira Sone ◽  
Tyler Volkoff ◽  
Lukasz Cincio ◽  
Patrick J. Coles
Keyword(s):  
Cost Function ◽  
Large Scale ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Quantum Circuits ◽  
Quantum Computers ◽  
Heuristic Methods ◽  
Practical Applications ◽  
Local Observables ◽  
Large Scale Simulations

AbstractVariational quantum algorithms (VQAs) optimize the parameters θ of a parametrized quantum circuit V(θ) to minimize a cost function C. While VQAs may enable practical applications of noisy quantum computers, they are nevertheless heuristic methods with unproven scaling. Here, we rigorously prove two results, assuming V(θ) is an alternating layered ansatz composed of blocks forming local 2-designs. Our first result states that defining C in terms of global observables leads to exponentially vanishing gradients (i.e., barren plateaus) even when V(θ) is shallow. Hence, several VQAs in the literature must revise their proposed costs. On the other hand, our second result states that defining C with local observables leads to at worst a polynomially vanishing gradient, so long as the depth of V(θ) is $${\mathcal{O}}(\mathrm{log}\,n)$$ O ( log n ) . Our results establish a connection between locality and trainability. We illustrate these ideas with large-scale simulations, up to 100 qubits, of a quantum autoencoder implementation.

Dynamical Peculiarities of Concatenational Changes in Functional Cardiovascular Parameters

2018 ◽  
Vol 3 (82) ◽  
Author(s):  
Eurelija Venskaitytė ◽  
Jonas Poderys ◽  
Tadas Česnaitis
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Functional State ◽  
The Body ◽  
Functional Changes ◽  
High Intensity Training ◽  
Series Analysis ◽  
Greco Roman ◽  
St Segment ◽  
Standard Lead

Research  background  and  hypothesis.  Traditional  time  series  analysis  techniques,  which  are  also  used  for the analysis of cardiovascular signals, do not reveal the relationship between the  changes in the indices recorded associated with the multiscale and chaotic structure of the tested object, which allows establishing short-and long-term structural and functional changes.Research aim was to reveal the dynamical peculiarities of interactions of cardiovascular system indices while evaluating the functional state of track-and-field athletes and Greco-Roman wrestlers.Research methods. Twenty two subjects participated in the study, their average age of 23.5 ± 1.7 years. During the study standard 12 lead electrocardiograms (ECG) were recorded. The following ECG parameters were used in the study: duration of RR interval taken from the II standard lead, duration of QRS complex, duration of JT interval and amplitude of ST segment taken from the V standard lead.Research  results.  Significant  differences  were  found  between  inter-parametric  connections  of  ST  segment amplitude and JT interval duration at the pre and post-training testing. Observed changes at different hierarchical levels of the body systems revealed inadequate cardiac metabolic processes, leading to changes in the metabolic rate of the myocardium and reflected in the dynamics of all investigated interactions.Discussion and conclusions. It has been found that peculiarities of the interactions of ECG indices interactions show the exposure of the  functional changes in the body at the onset of the workload. The alterations of the functional state of the body and the signs of fatigue, after athletes performed two high intensity training sessions per day, can be assessed using the approach of the evaluation of interactions between functional variables. Therefore the evaluation of the interactions of physiological signals by using time series analysis methods is suitable for the observation of these processes and the functional state of the body.Keywords: electrocardiogram, time series, functional state.

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