Use Case Study How Can Quantum Computing Help Automotive Battery Simulation
On December 13, Quantinuum announced the launch of InQuanto 2.0, a quantum computational chemistry platform [1]: more powerful and scalable to explore recent applications of quantum technology to material and molecular problems that are still challenging or difficult to solve for the most powerful classical computers.
01What's inside InQuanto 2.0?
InQuanto continues to build around the latest quantum algorithms, advanced subroutines, and chemically specific noise cancellation techniques. In the new version, we have added new features to improve efficiency, such as new protocol classes that can speed up vector calculations by an order of magnitude, integral operator classes that can take advantage of symmetry and reduce memory requirements.
Quantinuum introduces new tools for developing custom answers, new embedding techniques, and new hybrid methods to improve efficiency and precision, in some cases that have only recently been described in the scientific literature. These rapid advances are supported by new ways for computational chemists to incorporate InQuanto into their workflows, whether by improving visualization and interoperability with other chemistry software packages, or by demonstrating the ability to run in the cloud to meet the increasingly advanced needs of enterprise partners tomorrow.
In summary, InQuanto 2.0 brings together a series of new features that continue to be the right choice for computational chemists on quantum computers:
1) Efficiency
Improved workflow for protocol classes to increase the efficiency of small test calculations – some state vector calculations are up to 10 times faster.
Symmetry uses classes of integral operators to efficiently process chemical Hamiltonian two-electron integrals using about 50% less memory.
Computability of optimized n-particle densification matrix.
2) Algorithm
Extensive recombinant answers supporting multi-reference computations to enable new types of variational quantum algorithms: with improved custom answer development tools
A versatile variational quantum solver for hypothetical and real-time evolutionary simulations
Added fragment molecular orbital embedding method
The new QRDM-NEVPT2 method measures the 4-particle reduction density matrix and increases the corrected value of the VQE energy
3) User experience
FCIDUMP read/write, improving integration with other quantum chemistry software packages
Cell visualization extends and supports Trojanization at the operator level
Improved resource cost estimation on H-Series quantum computers, powered by Honeywell
02InQuanto 2.0 use case: battery simulation for Ford vehicles
Quantum researchers at Ford Motor Company are seeking new ways to mimic the chemistry of lithium-ion batteries.
Typically, highly accurate simulations of complex, real-world molecules are beyond the reach of the most advanced classical computers because the problem space is large and grows exponentially with the size of the system. Quantum computers overcome this problem by exponentially expanding their computing power.
The scientific team used quantum computers to study lithium-ion battery chemistry [2]. In this work, the scientists used the variational quantum solver (VQE), an algorithm for finding the ground state of a quantum mechanical system. VQE is a hybrid quantum-classical algorithm that is deployed on today's quantum computers, solving only the parts of molecular systems that benefit most from quantum computing, leaving the rest of the computation to classical computers.
Using this hybrid approach, supported by Quantinuum's quantum chemistry platform, InQuanto, the team was able to process molecules directly relevant to battery research. They also demonstrated a simulation that brings the field of quantum chemistry closer to being able to tackle real-world problems on quantum computers.
In the experiment, a gas phase model was used to simulate the building blocks of CoO2/CoO2 for simulation on a quantum computer.
Forecasts for future battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), committed to policy (STEP) and sustainability (SD) scenarios
03The software platform empowers applications and will further improve hardware capabilities
As a next step, Quantinuum is rapidly ramping up the capabilities of its H-series hardware:
Develop new methods for noise reduction and error correction
Pioneered the use of logical qubits
[1]https://www.quantinuum.com/news/by-chemists-for-chemists-introducing-inquanto-tm-2-0
