InQuanto 2.0‍

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Quantinuum has announced, InQuanto 2.0‍, a computational chemistry platform for quantum computing. In close collaboration with its industrial partners, Quantinuum has designed, developed, and discovered methods using InQuanto for exploring the application of near-term quantum technology to material and molecular problems that remain challenging or intractable for even the most powerful classical computers.

What’s inside InQuanto 2.0?

InQuanto continues to be built around the latest quantum algorithms, advanced subroutines, and chemistry-specific noise-mitigation techniques. In the new version, Quantinuum  have added new features to enhance efficiency, such as new protocol classes that can speed up vector calculations by an order of magnitude, and integral operator classes that exploit symmetries and can reduce memory requirements.

In this latest version, Quantinuum introduced new tools for developing custom ansätze, new embedding techniques and novel hybrid methods to improve efficiency and precision, which in some cases have only recently been described in the scientific literature. And these rapid advances are supported by new ways for computational chemists to build InQuanto into their workflow, whether that is by improving visualisation and interoperability with other chemistry packages, or by demonstrating the ability to run it in the cloud, for example, through a recent demonstration with Amazon Braket.

InQuanto 2.0 brings together a range of new features that continue to make it the right choice for computational chemists on quantum computers:

  • Workflow improvements in protocol classes for more efficient small test calculations — up to 10x speed-ups in some state vector calculations

  • Symmetry-exploiting integral operator classes for efficient handling of the two-electron integral for a chemistry Hamiltonian using ~50% less memory

  • Optimized computables for n-particle reduced density matrices

  • Wide range of restructured ansätze to support multi-reference calculations to enable new types of variational quantum algorithms — with improved custom ansatz development tools

  • Generalised variational quantum solvers to perform imaginary and real-time evolution simulations

  • Added Fragment Molecular Orbital embedding method

  • New QRDM-NEVPT2 method to measure 4-particle reduced density matrices and add corrections to VQE energy

User Experience
  • FCIDUMP read/write for improved integration with other quantum chemistry packages

  • Unit cell visualisation extensions, and support for trotterisation in the operator level

  • Improved resource cost estimation on H-Series quantum computers, Powered by Honeywell 

Research case study: Ford battery researchers used InQuanto to study how quantum computers could be used to model lithium-ion batteries.