While working with Dr. Nguyen from boltz.ai, one of the first groups in the world using quantum computing to help increase the crop yields of farmers, I got to know a roadmap of how Dr. Nguyen and his team plan on applying quantum computing to agriculture in the coming years.
Just recently, boltz.ai published a graphic outlining their use case for quantum computers in agriculture on their website. In it, Dr. Quang Nguyen and his team outline the problem: different crops require different resources and nutrients in order to maximise their crop yields. Their solution? To create a system “that helps farmers apply the right crop input sources at the right rate, the right time, and in the right place.”
The current machine learning utilizes software in order to process and store information. The energy usage of this process is extremely inefficient, however, leading to questions of how this process could be sped up.
The quantum computing world continues to display its advantages as quantum computers are applied to more and more areas. Just recently, researchers were able to apply quantum computing to a real world logistics optimization problem which required assigning aircraft to certain routes. The quantum computer, which only had 2 qubits, was able to complete the task, demonstrating that even small quantum computers can be used in the real world.
As the quantum world continues to expand and advance, it seems that more and more people are catching onto quantum computing. Just this fall, IBM had released their quantum roadmap for the next couple of years, with plans of having 1,121 qubits by the year of 2023.
Accepted into the Qubit x Qubit quantum computing course and two weeks into the lectures, we learned about quantum computing in the abstract, and how quantum gates affect qubits. I think it’s important to talk about why applying two hadamard gates to a qubit returns the qubit back to its initial state.
Ok — here goes! In this blog post, I will be explaining a part of chapter 9 of Quantum Computing as a High School Module, the Deutsch Jozsa Algorithm.
I used the Cirq programming language to program entanglement after understanding chapter 7, “Entanglement” of Quantum Computing as a High School Module.
I used the Cirq programming language to program single qubit gates after understanding chapter 6, “Quantum Gates” of Quantum Computing as a High School Module.
On Monday, July 20th, I had the great honor of talking to Dr. Allard De Wit from Wageningen University, one of the main contributors of the WOFOST crop model. WOFOST is one of the best crop models being used to measure crop yields around the world.
Quantum computing is the future, and as each day passes, we are still discovering new and innovative ways to use this technology. One new place I believe that quantum computing will be a game-changer is in crop modeling.
If for one day I could make one topic become the most popular on the internet, I would make it "crop modeling". Because crop modeling can save our planet. Crop modeling is an agricultural tool that will help small scale producers increase their crop yields, and ultimately, help us feed the world.
In the past couple of weeks, I have been reading through an introductory course on quantum computing from Cornell and FermiLab (you can check it out here: https://arxiv.org/abs/1905.00282).
A couple of weeks ago, I submitted a solution, Quantum Mind , to MIT Solve 2020. In the submission, I filmed the pitch at Terhune Orchards. The submission went in the food sustainability category, and involved a revolutionary innovation, quantum computing, as well as satellite connectivity, being used to increase crop yields.
For the last week, I downloaded a Cornell and FermiLab course about how to be a quantum engineer. The course, Quantum Computing as a High School Module, created by Anastasia Perry, Ranbel Sun, Ciaran Hughes, Joshua Isaacson, Jessica Turner, taught me the basics of quantum computing.