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We’ve taken another important step on our path towards frictionless quantum computing: A new release of Qiskit with a completely overhauled Qiskit Chemistry module, as well as a brand new Qiskit Gradients framework. Both enhancements pave the way for quantum application software that serves the needs of:
- domain experts without a deep quantum computing background who want to experiment with them as black boxes, as well as
- quantum algorithms researchers who want to develop and test new algorithmic building blocks, program runtimes, error mitigation techniques and real quantum hardware in an application context.
READ: Introducing The New Qiskit Chemistry Module And Gradients Framework For Next-Level Quantum Computing
An overhauled Qiskit Chemistry module
The Qiskit Chemistry module includes a variety of algorithms designed to be modular and extensible, while also providing high-level applications that enable subject matter experts to get started with quantum computing. This includes algorithms for electronic and vibronic structure calculations, as well as algorithmic primitives which can be used for higher-level applications. Recently, this allowed IBM Quantum and ExxonMobil scientists to compute thermodynamic observables for the Hydrogen molecule on the 5-qubit ibmq_valencia quantum processor using Qiskit.
“The development of the new Qiskit Chemistry module is both exciting and very important. It will enable scientists with limited exposure to quantum hardware to quickly begin to simulate interesting and relevant chemistry problems. Perhaps as important, this new module will also help scientists think ‘in a quantum way’ when solving some of our most challenging problems.”
– Laurent White, Section Head, Computational Physics, ExxonMobil Research and Engineering
Many near-term quantum algorithms – including the ones available in the Qiskit Chemistry module – are variational, i.e., they use classical optimization to find a set of parameters which minimize an objective function, evaluated using a quantum computer. This objective can be an energy in quantum chemistry, an expected return in financial portfolio optimization, or a loss function in quantum machine learning. All in all, many quantum algorithms rely on efficient and robust optimization.
Introducing Qiskit Gradients
The new Qiskit Gradients framework provides an automated way to compute analytic gradients as well as functions, thereof, for a variety of problem classes, for instance in chemistry, optimization, and machine learning. This is achieved by automatically constructing the operators required to estimate circuit derivatives, and combining this with classical automatic differentiation techniques. The framework is integrated in Qiskit’s core algorithms, such that it is straight-forward to leverage it in existing applications. Further, it will serve as a building block for future application modules, such as quantum machine learning.
The Gradients framework not only supports the estimation of first-order gradients but also Hessians, as well as the Quantum Fisher Information. This immediately paves the way to more advanced algorithms, like Quantum Natural Gradients, Variational Quantum Imaginary / Real Time Evolution, or Variational Gibbs State Preparation. The Gradients framework is integrated in Qiskit’s core algorithms, such that it is straight-forward to use it in existing applications. Further, it will serve as a building block for future application modules, such as quantum machine learning.
Get started with Qiskit Chemistry & Gradients
For more, read the IBM team’s Qiskit Medium blog about how to explore the Qiskit Chemistry module and Gradients framework, from installing the latest Qiskit version, to how to start playing around with the newly released tutorial notebooks for chemistry and gradients. In addition, you can check out the Qiskit slack workspace to connect with other Qiskit users and contributors.
Quantum starts here