A much-anticipated prize in quantum computing is the ability to more accurately model chemical bonding behavior. Doing so should lead to better chemical synthesis methods – think nitrogen fixation and ammonia synthesis, for example – as well as to novel materials design. Google researchers reported in a Science paper last week that they were able to simulate bond energies in diazene and in stretched hydrogen chains on Google’s 54-qubit Sycamore quantum processor. This is the largest chemical simulation performed on quantum computer to date reported Google.

The work is a significant POC study that suggests a path forward for using so-called noisy intermediate scale quantum (NISQ) computers for practical applications in quantum chemistry.

There’s a brief account of the work posted in Physics Today. The Google researchers used a Hartree-Fock method which assumed that the wavefunction for a system of electrons can be written “in terms of single-electron functions, without electron–electron interactions, and that each electron feels the average electric field from other electrons.” The wavefunction is then adjusted to minimize its energy.

Here’s an excerpt from the Physics Today article written by Heather Hill:

“The researchers used that method for two common electronic-structure benchmarks: distinguishing the pathways for a diazene molecule, HNNH, to transform between cis and trans isomers and finding the binding energy of stretched linear hydrogen chains for lengths of 6, 8, 10, and 12 atoms. Previous electronic-structure calculations by quantum computers required only up to 6 qubits, but here the researchers used as many as 12 qubits interacting through 72 two-qubit logic gates.

“With all those qubits and gates, error mitigation was essential. The team kept only measurements in which the number of particles stayed the same; a change in that number is a clear sign of an error. The researchers also looked at the one-particle densities, and if the wavefunction didn’t yield the expected 0 and 1 eigenvalues, they projected it onto the closest state that did. They were able to get an accuracy that was high enough to make chemical predictions with 99% fidelity for the logic gates and 97% fidelity for readout.”

Hill points out classical computers can model all the systems in the current study, and classically intractable problems will require an additional one or two orders of magnitude more qubits; nevertheless, the strategies that the researchers developed and implemented should scale up.

Google quantum researchers Nicholas Rubin and Charles Neill note in a Google AI blog, “[We] used a noise-robust variational quantum eigensolver (VQE) to directly simulate a chemical mechanism via a quantum algorithm. Though the calculation focused on the Hartree-Fock approximation of a real chemical system, it was twice as large as previous chemistry calculations on a quantum computer, and contained ten times as many quantum gate operations. Importantly, we validate that algorithms being developed for currently available quantum computers can achieve the precision required for experimental predictions, revealing pathways towards realistic simulations of quantum chemical systems.”

 

Google has released the code for the experiment, which uses OpenFermion, Google’s open source repository for quantum computations of chemistry.

One of biggest challenges facing quantum computing is error correction and mitigation. Rubin and Neil said that Google earlier work to demonstrate quantum supremacy (see HPCwire coverage, Google Goes Public with Quantum Supremacy Achievement; IBM Disagrees) led to development of targeted calibration techniques “that optimally amplify errors so they can be diagnosed and corrected.”

The researchers wrote, “Errors in the quantum computation can originate from a variety of sources in the quantum hardware stack. Sycamore has 54-qubits and consists of over 140 individually tunable elements, each controlled with high-speed, analog electrical pulses. Achieving precise control over the whole device requires fine tuning more than 2,000 control parameters, and even small errors in these parameters can quickly add up to large errors in the total computation.

“To accurately control the device, we use an automated framework that maps the control problem onto a graph with thousands of nodes, each of which represent a physics experiment to determine a single unknown parameter. Traversing this graph takes us from basic priors about the device to a high-fidelity quantum processor, and can be done in less than a day. Ultimately, these techniques along with the algorithmic error mitigation enabled orders of magnitude reduction in the errors.”

Rubin and Neill say the experiment serves as a blueprint for how to run chemistry calculations on quantum processors, and as a jumping off point on the path to physical simulation advantage.

Link to Science paper (Hartree-Fock on a superconducting qubit quantum computer) : https://science.sciencemag.org/content/369/6507/1084

Link to Google blog: https://ai.googleblog.com/2020/08/scaling-up-fundamental-quantum.html

Link to Physics Today article: https://physicstoday.scitation.org/do/10.1063/PT.6.1.20200908a/full/

Feature photo caption: Google’s Sycamore processor mounted in a cryostat, recently used to demonstrate quantum supremacy and the largest quantum chemistry simulation on a quantum computer. Photo Credit: Rocco Ceselin

Figures source: Google AI Blog