IBM Breaks Ground for Complex Quantum Chemistry

By John Russell

September 14, 2017

IBM yesterday reported in Nature Communications the use of a novel algorithm to simulate BeH2 (beryllium-hydride) on a quantum computer. This is the largest molecule so far simulated on a quantum computer. The technique, which used six qubits of a seven-qubit system, is an important step forward and may suggest an approach to simulating ever larger molecules.

“Instead of forcing previously known classical computing methods onto quantum hardware, the scientists reversed the approach by building an algorithm suited to the capability of the current available quantum devices. This allows for extracting the maximal quantum computational power to solve problems that grow exponentially more difficult for classical computers,” according to the IBM announcement.

Quantum chemistry has long been regarded as of the great promises of quantum computing. A good example is nitrogen fixation – essentially making ammonia – from the air. Bacteria do it effortlessly. Industry still does it with a hundred-year-old Haber process, which is used mostly in fertilizer production today.

Today, simulating even small molecules with the needed accuracy to predict energy states and reactivity is hard. IBM performed the numerical simulation on H2, LiH, and BeH2. “While this model of BeH2 can be simulated on a classical computer, IBM’s approach has the potential to scale towards investigating larger molecules that would traditionally be seen to be beyond the scope of classical computational methods, as more powerful quantum systems get built,” noted IBM.

Here’s a good statement of the problem and IBM’s solution taken from the paper (Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets):

“Finding exact solutions to such problems numerically has a computational cost that scales exponentially with the size of the system, and Monte Carlo methods are unsuitable owing to the fermionic sign problem. These limitations of classical computational methods have made solving even few-atom electronic-structure problems interesting for implementation using medium-sized quantum computers. Yet experimental implementations have so far been restricted to molecules involving only hydrogen and helium.

“Here we demonstrate the experimental optimization of Hamiltonian problems with up to six qubits and more than one hundred Pauli terms, determining the ground-state energy for molecules of increasing size, up to BeH2. We achieve this result by using a variational quantum eigenvalue solver (eigensolver) with efficiently prepared trial states that are tailored specifically to the interactions that are available in our quantum processor, combined with a compact encoding of fermionic Hamiltonians and a robust stochastic optimization routine.”

There are, of course many approaches to quantum computing and new kinds of qubits seem to appear weekly. IBM, Microsoft, and Google are focused on so-called universal quantum computers able to do pretty much anything classical computers can do. D-Wave builds quantum annealing computers best suited for certain optimization problems, some of which include quantum chemistry problems.

“IBM, Google and a number of academic labs have chosen relatively mature hardware, such as loops of superconducting wire, to make quantum bits (qubits). These are the building blocks of a quantum computer: they power its speedy calculations thanks to their ability to be in a mixture (or superposition) of ‘on’ and ‘off’ states at the same time.”[i] Microsoft is pursing one of the more exotic approaches – a topological qubit, the Majorana, a particle whose existence has been debated but for which evidence has been rapidly accumulating recently.

As described by IBM’s work, the fundamental goal in electronic-structure problems is to solve for the ground-state energy of many-body interacting fermionic Hamiltonians. Solving this problem on a quantum computer relies on a mapping between fermionic and qubit operators, which restates the problem as a specific instance of a local Hamiltonian problem on a set of qubits.

“Here we introduce and implement a hardware-efficient ansatz preparation for a VQE (variational quantum eigensolvers), whereby trial states are parameterized by quantum gates that are tailored to the physical device that is available. We show numerically the viability of such trial states for small electronic-structure problems and use a superconducting quantum processor to perform optimizations of the molecular energies of H2, LiH and BeH2, and extend its application to a Heisenberg antiferromagnetic model in an external magnetic field,” write the authors, all from IBM Research.[ii]

Below is diagram and caption of the recent work taken from the paper. (inset after)

 

Figure 1 | Quantum chemistry on a superconducting quantum processor. Solving electronic-structure problems on a quantum computer relies on mappings between fermionic and qubit operators. a, Parity mapping of eight spin orbitals (drawn in blue and red, not to scale) onto eight qubits, which are then reduced to six qubits owing to fermionic
spin and parity symmetries. The length of the bars indicate the parity of the spin orbitals that are encoded in each qubit. b, False-coloured optical micrograph of the superconducting quantum processor with seven transmon qubits. These qubits are coupled via two coplanar waveguide resonators (violet) and have individual coplanar waveguide resonators for control and read-out. c, Hardware-efficient quantum circuit for trial- state preparation and energy estimation, shown here for six qubits. For each iteration k, the circuit is composed of a sequence of interleaved single-qubit rotations Uq,d(θk) and entangling unitary operations UENT that entangle all of the qubits in the circuit. A final set of post-rotations
(I, X−p/2 or Yp/2) before the qubits are read out is used to measure the expectation values of the individual Pauli terms in the Hamiltonian and to estimate the energy of the trial state. d, An example of the pulse sequence for the preparation of a six-qubit trial state, in which UENT is implemented as a sequence of two-qubit cross-resonance gates.

IBM has certainly been an industry leader in providing access to quantum computing, most visibly through its IBM Q initiative  launched a year ago with a robust five-qubit quantum computer on the cloud for anyone to freely access; it has recently upgraded to a 16-qubit processor available for beta access.

To help showcase how quantum computers are adept to simulating molecules, developers and users of the IBM Q experience are now able to access a quantum chemistry Jupyter Notebook. The open source quantum chemistry Jupyter Notebook (available through the open access QISKit github repo) allows users to explore a method of ground state energy simulation for small molecules such as hydrogen and lithium hydride.

Quoted in the IBM announcement of the most recent work, Alán Aspuru-Guzik, professor of chemistry and chemical biology at Harvard University characterized IBM’s recent work as impressive, noting “When quantum computers are able to carry out chemical simulations in a numerically exact way, most likely when we have error correction in place and a large number of logical qubits, the field will be disrupted. Exact predictions will result in molecular design that does not need calibration with experiment. This may lead to the discovery of new small-molecule drugs or organic materials.”

Link to IBM paper: http://www.nature.com/nature/journal/v549/n7671/full/nature23879.html?foxtrotcallback=true

[i] http://www.nature.com/news/inside-microsoft-s-quest-for-a-topological-quantum-computer-1.20774

[ii] Abhinav Kandala, Antonio Mezzacapo, Kristan temme, Maika takita, Markus Brink, Jerry M. Chow1 & Jay M. Gambetta

 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

Mystery Solved: Intel’s Former HPC Chief Now Running Software Engineering Group 

April 15, 2024

Last year, Jeff McVeigh, Intel's readily available leader of the high-performance computing group, suddenly went silent, with no interviews granted or appearances at press conferences.  It led to questions -- what's Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Institute for Human-Centered AI (HAI) put out a yearly report to t Read more…

Crossing the Quantum Threshold: The Path to 10,000 Qubits

April 15, 2024

Editor’s Note: Why do qubit count and quality matter? What’s the difference between physical qubits and logical qubits? Quantum computer vendors toss these terms and numbers around as indicators of the strengths of t Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips are available off the shelf, a concern raised at many recent Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announced its second fund targeting €200 million. The very idea th Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. In a way, Nvidia is the new Intel IDF, the hottest chip show Read more…

Exciting Updates From Stanford HAI’s Seventh Annual AI Index Report

April 15, 2024

As the AI revolution marches on, it is vital to continually reassess how this technology is reshaping our world. To that end, researchers at Stanford’s Instit Read more…

Intel’s Vision Advantage: Chips Are Available Off-the-Shelf

April 11, 2024

The chip market is facing a crisis: chip development is now concentrated in the hands of the few. A confluence of events this week reminded us how few chips Read more…

The VC View: Quantonation’s Deep Dive into Funding Quantum Start-ups

April 11, 2024

Yesterday Quantonation — which promotes itself as a one-of-a-kind venture capital (VC) company specializing in quantum science and deep physics  — announce Read more…

Nvidia’s GTC Is the New Intel IDF

April 9, 2024

After many years, Nvidia's GPU Technology Conference (GTC) was back in person and has become the conference for those who care about semiconductors and AI. I Read more…

Google Announces Homegrown ARM-based CPUs 

April 9, 2024

Google sprang a surprise at the ongoing Google Next Cloud conference by introducing its own ARM-based CPU called Axion, which will be offered to customers in it Read more…

Computational Chemistry Needs To Be Sustainable, Too

April 8, 2024

A diverse group of computational chemists is encouraging the research community to embrace a sustainable software ecosystem. That's the message behind a recent Read more…

Hyperion Research: Eleven HPC Predictions for 2024

April 4, 2024

HPCwire is happy to announce a new series with Hyperion Research  - a fact-based market research firm focusing on the HPC market. In addition to providing mark Read more…

Google Making Major Changes in AI Operations to Pull in Cash from Gemini

April 4, 2024

Over the last week, Google has made some under-the-radar changes, including appointing a new leader for AI development, which suggests the company is taking its Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

Leading Solution Providers

Contributors

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Eyes on the Quantum Prize – D-Wave Says its Time is Now

January 30, 2024

Early quantum computing pioneer D-Wave again asserted – that at least for D-Wave – the commercial quantum era has begun. Speaking at its first in-person Ana Read more…

GenAI Having Major Impact on Data Culture, Survey Says

February 21, 2024

While 2023 was the year of GenAI, the adoption rates for GenAI did not match expectations. Most organizations are continuing to invest in GenAI but are yet to Read more…

Intel’s Xeon General Manager Talks about Server Chips 

January 2, 2024

Intel is talking data-center growth and is done digging graves for its dead enterprise products, including GPUs, storage, and networking products, which fell to Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire