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chuckziss t1_j22hzvv wrote

Reply to comment by itsmeyour in Ford used a quantum computer to find better EV battery materials by upyoars

The three NISQ (Noisy Intermediate Scale Quantum) applications that are promising are VQE (Variational Quantum Eigensolver), QAOA (Quantum Approximate Optimisation Algorithms), and QML (Quantum Machine Learning).

VQE is probably what Ford tried to use here, and it calculates the ground state energy of a molecule. Even using many QCs in parallel gives mediocre results for VQE when compared to super computers.

QAOA can be used for optimisation problems that are difficult for classical computers. We really don’t have enough qubits to out compete super computers, but there are some fun novel experiments you can design in matrix completion, or supply chain optimisation.

QML is honestly a joke. It’s just a tradition ML model with one quantum layer. I can go into more detail, but it’s quite underwhelming in the end.

The bottom line is that there really isn’t a good current application of QC. All of the above algorithms are out-competed by a decent classical computer.

I’ll gladly throw some sources your way if you’re interested in learning more.

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itsmeyour t1_j22iihb wrote

Oh man, if its not too much work for you. Already I feel like you helped me understand the layout of the land. I think I remember for a while the basic "can we entangle and hold" was the mission. Very much appreciate you

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chuckziss t1_j22jb9p wrote

I’m not sure about that quote, but my interpretation is that the mission is to store information in qubits for longer.

This is still largely true - current devices are bad at keeping information, and often you get qubit “decoherence” (decay/loss of information) such that you can no longer do anything useful with the information.

https://arxiv.org/abs/2111.14940 - talks about VQA which comprise VQE and QAOA. This paper does computing with many devices at once. It demonstrates things well, but the end result is still solving a trivial problem.

https://community.qiskit.org/textbook/ch-machine-learning/machine-learning-qiskit-pytorch.html - all the qiskit tutorials are great if you want hands on learning, but this just demonstrates the state of the art in combining classical and quantum machine learning. The end result is just an MNIST model… which is very doable with classical computing on its own.

I’ll leave it there, but happy to continue the conversation.

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CoolEnemy t1_j22jksd wrote

Yo i would really the the sources if you can

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chuckziss t1_j22jou2 wrote

I replied in another comment - happy to add more specific sources if you want

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