Xanadu Profile picture
Sep 27, 2022 11 tweets 5 min read Read on X
How can we simulate lithium-ion batteries with a quantum computer?🔋⚛️🤔

Our collaborative paper (journals.aps.org/pra/abstract/1…) with Volkswagen has the answer, so let's dive in with a thread by our Head of Algorithms @ixfoduap 🧵 1/11 Image
“This work is unlike most other papers: it’s somewhere between a review paper 📘, a tutorial 📗, and a technical manuscript 📙

This is by design! We want it to be a self-contained reference for everyone interested in battery simulation with #quantum computers 🤓📖”

2/11 Image
“Our main challenge was to navigate the interdisciplinary nature of the project. Requiring expertise in batteries, DFT, quantum chemistry, materials science and quantum algorithms. It was only possible by assembling a team of experts across these disciplines” 👨‍🔬 👩‍🔬 ⚛️ 🔋

3/11 Image
“We focus on simulating key properties of batteries: voltage ⚡, ionic mobility 🏃‍♀️ and thermal stability 🔥. These require simulations of *cathode materials*. Prior work looked at electrolyte molecules, which are less relevant in the context of next-generation batteries”

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“Simulating materials requires different quantum algorithms than those employed for molecules. We were persuaded that the best methods are the *first-quantization*🥇techniques developed by @Google and collaborators, see arxiv.org/abs/2105.12767

5/11
“Our work is thus largely an application of first-quantization algorithms ➡️ to battery simulation. We put great effort into a pedagogical explanation of each step, which we hope readers will find useful 💡”

6/11 Image
“A missing piece of the puzzle were methods for *initial state preparation*. We provide a recipe to do this. The main insight is a method to implement Givens rotations ♻️ on anti-symmetrized states (used to prepare a HF state in a basis of Bloch atomic orbitals)"

7/11 Image
“It remains unclear whether the overlap of this state with the true ground state is large enough for use in quantum phase estimation 🤔. This is a *crucial* open problem in the field, which is likely even more pressing for materials than for molecules”

8/11
“Putting these insights together, we perform a resource estimation analysis for a realistic cathode material, dilithium iron silicate. This is the first time this has been done! 🚀 ”

9/11 Image
“Our main conclusions:

1. Quantum algorithms are likely still too expensive to be practical. We need more work to reduce their cost! 📉

2. Cost of initial state preparation is significant but still smaller than full quantum phase estimation 😌”

10/11 Image
“This is the first step 👣 in a journey where we will be hyper-focused 🔎 on researching better quantum algorithms for battery simulation. We’re fortunate to pursue this in partnership with fantastic scientists across industry 🏢🚘 and academia 🏛️. Stay tuned for more!”

11/11

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