Thread by @XanaduAI

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

“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

“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

“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”

4/11

“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

“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

“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

“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

“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