New Qiskit Chemistry Module

Qiskit Chemistry Module —Enables ground and excited state calculations. These algorithmic components can be reused throughout the stack. This equips users to easily build applications that leverage core algorithms, circuits, and quantum hardware. The transformations can transform a given driver into qubit operators and determine whether a problem is treated in a fermionic or bosonic context.

IBM has just announced a complete overhaul of its Qiskit Chemistry module, plus the new Qiskit Gradients framework, meant both for quantum application developers as well as domain experts with basic knowledge of quantum computing.

Simulating quantum systems on classical computers has been one of the greatest challenges for the quantum physics and quantum chemistry communities for the past few decades.

The team designed the new Qiskit Chemistry module to be both modular and extensible, while providing high-level applications that make programming more intuitive for anyone interested in quantum computing. The module includes algorithms for calculating molecules’ electronic and vibronic structure, plus algorithmic primitives to serve as the building blocks for higher level applications.

Many near-term quantum algorithms are variational, i.e., they use classical optimization to find a set of parameters which minimize some target function evaluated using a quantum computer. These kinds of algorithms rely on efficient and robust optimization to serve purposes in combinatorial problems or quantum machine learning — and in the quantum chemistry case, the objective is an energy.

Meanwhile, the new Qiskit Gradients framework provides an automated way to compute analytic gradients — basically, how the circuit is changing as the variational algorithm runs — as well as functions of the gradients for a variety of problem classes. This is achieved by automatically constructing the operators required to estimate circuit derivatives and combining this with classical automatic differentiation. The Gradients framework not only supports the estimation of first order gradients but also Hessian and Quantum Fisher Information matrices. This immediately paves the way to more advanced algorithms like Quantum Natural GradientsVariational Quantum Imaginary / Real Time Evolution, and Variational Gibbs State Preparation. The Gradients framework is integrated into Qiskit’s core algorithms, making it straightforward to leverage in existing applications. Further, it will serve as a building block for future application modules, such as quantum machine learning.

Recently, IBM teamed up with ExxonMobil scientists to compute thermodynamic observables for the hydrogen molecule on the ibmq_valencia quantum processor using Qiskit.

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