As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.
As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.
As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn t enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.
As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: can qubit tune-up be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn t enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.
As quantum computers scale up, the manual process of qubit tune-up becomes increasingly impractical due to its time-consuming and repetitive nature. While existing research has explored some automation techniques, many models remain underutilized for this purpose. This research aims to answer the question: is qubit tune-up able to be automated using the Long Short-Term Memory (LSTM) model? For the purposes of this project, only the rabi and ramsey measurement cycle was automated. These measurements are used to fine-tune a rough qubit frequency by repeating them until the optimal qubit frequency is obtained. The LSTM model uses the qubit frequency at one time step to forecast the qubit frequency at the next time step. A rabi-ramsey simulation was made to fabricate a dataset to train and test the LSTM model. As the model was trained, the error of the model decreased. Although there wasn't enough training data to generate perfect predictions, this shows it is possible to utilize forecasting models in automating the tune-up process.
Abstract Tuning gate-defined quantum dots for qubit operation is an often frustrating and time-consuming activity in spin qubit research due to inherent device variability and the complex interactions between dots. Furthermore, tuning these devices grows more complicated as quantum computers grow to utility-scale. Computer-automated and machine learning techniques are popular approaches to accelerate the tuning process. However, these techniques need to become much faster and more accurate to address the tuning of millions of qubits in utility-scale quantum computing. We examine the literature of computer-automated and machine learning spin qubit tuning algorithms, grouping the algorithms by similar tasks and analyzing them from a computer science perspective to keep necessary background knowledge of quantum computing/physics at a minimum. We look at which approaches seem propitious for scaling up to utility-scale systems and where computer scientists could potentially have the most impact.
Building a quantum computer able to solve real-world problems is facing several challenges, both in software and hardware.Some of these challenges are the physical implementation of a large number of qubits, as well as qubit quality and control.This thesis addresses one of the outstanding challenges -the automated initialisation of solid-state qubits.These qubit types, which encode information in charge, spin or topologically protected quasiparticle states, are based on electrostatically defined quantum dots.In order to define quantum dots, suitable gate voltage combinations need to be determined and several parameters such as the inter-dot tunnel coupling adjusted to desired values.With growing device complexity and an increasing number of devices required for measurements, a manual approach to tuning is impractical.Considering the success of machine learning in many disciplines over recent years, it is a promising tool whose suitability for qubit tuning is explored here.We first implement a two-stage device characterisation and dot-tuning process by automating well-established, manual tuning procedures, where the experimenter's decisions is replaced by supervised machine learning.A demonstration on several previously unmeasured devices shows that such an approach is sufficient to tune devices autonomously and without pre-measured input.Next, we address charge-state detection based on charge stability diagrams, an essential component of automated tuning.Beside our use of simple binary classifiers trained on experimental data, deep learning models trained on synthetic data have been previously used to perform this task.While the use of synthetic data has the advantage of providing the large training datasets required for deep learning without the need to hand label experimental data, it also trains classifiers on data coming from different distributions than the data encountered during tuning.We evaluate the classification accuracy of a range of machine learning models trained on simulated and experimental data, assessing their ability to generalise to experimental charge stability diagrams.We find that experimental training data as well as realistic quantum dot simulations and noise models iii Suominen, these two dragged me out of the lab on a regular basis and made sure all doubts and misery were drowned in bubbles.Their input on how to navigate the first period of new beginnings and changes was priceless.Beside the usual PhD challenges and as a result of living in two places at one, my adventure also included some overhead paperwork.Overwhelmed by it at times, it was more than reassuring to have a friend like Mario Könz.He has always been keen on sharing some of his abundant knowledge, be it in programming or in how to deal with Swiss authorities.My endeavour would have failed without his readiness to help me out on the Swiss paperwork front.Similarly, Anïs Mairena, whose presence already got me through my Master's in Lausanne, also saved me from a lot of admit headache.I am also immensely grateful for crossing the paths of John Hornibrook and Xanthe Croot.As the kindest and most generous people I have ever met, these two made me feel at home far away at home.I will always remember those 30km bush walks with Xanthe, during which I not only got to know some of the QNL lab rats better, but was also able to recharge on positivity, happiness and sunshine.Back in the lab, it was John who patiently listened to my complains and answered all my dumb questions.From my very first steps in python and QCoDeS to the implementation of my tuning package, I had the privilege to be able to count on William H. P Nielsen and Jens H. Nielsen for support.They not only helped me get through writing drivers for instruments I didn't how to use, but their patience, comprehensive feedback and technical expertise made me the programmer I am today.Despite the rather atypical working arrangement, I was blessed to have someone who could comprehend my situation: Natalie Pearson.I was fortunate not to be the only one packing my bags for Copenhagen early in their PhD.We embarked on the Danish journey together and were able to support each other in a new country.In German one would say "geteiltes Leid ist halbes Leid".Furthermore, I would like to mention those who always had a time for a spontaneous chat, ultimately getting me through those days that didn't seem to end:
Merger of Quantum computing and Machine learning explores a new shift in artificial intelligence. Quantum neural networks and parameterized quantum circuits are tools that enable the merger of these two branches. Here, we use Quantum circuit enabled long short term memory (QLSTM) neural network to forecast time series data. We show the efficacy of quantum computing by conducting experiments on two different time series data sets. In our first experiment, we are predicting the amount of rainfall fall and the second experiment has electric load (power) prediction. Our dataset for rainfall prediction includes hourly information on the weather conditions i.e., wind speed, wind direction, minimum and maximum temperatures, and pressure with the amount of rain falls. For electric load (power generation) dataset, few of the features include amount of biomass, amount of fossil brown coal/ignite, amount of fossil coal derived gas, nuclear power, solar power, and the corresponding wind velocity forecasts. We compare the training as well as the test loss of classical Bidirectional LSTM (BILSTM) and the Quantum BILSTM and observe that LSTM based on quantum approach reduces both the training and test loss considerably when compared to its classical part with very few epochs of training.