STAQ(ing) the Quantum Computing Deck

By John Russell

August 16, 2018

Quantum computers – at least for now – remain noisy. That’s another way of saying unreliable and in diverse ways that often depend on the specific quantum technology used. One idea is to mitigate noisiness and perhaps seamlessly capture some of the underlying quantum physics by mapping quantum algorithms more directly to the underlying hardware; this might make nearer-term quantum computers practical for some problems. This approach, at least in part, is central to the Software Tailored Architecture for Quantum Design (STAQ) project, announced by NSF last week and led by co-PIs Kenneth Brown and Jungsang Kim of Duke University.

Every project needs a goal and the big callout here is building a 64- (or more) qubit ion trap-based quantum computer capable of tackling problems that classical computers currently stumble on. But that doesn’t catch the scope of the project which is making a point of leveraging multidiscipline expertise to put co-design to work in the quantum domain, exploring specific algorithms for condensed matter physics and quantum chemistry, as well as more general quantum algorithm optimization. There’s also a requirement to run a summer school to share the learnings.

“I always joke that if I knew what the silicon transistor was of quantum computing, I would just do it. But I don’t.” Brown told HPCwire. “Right now I think both superconductors and ion traps have shown a lot of progress and demonstrated a large number of algorithms. The advantage of trapped ions is that every ion is the same. For these small chains [of ions in the trap] you do get this advantage of basically being able to achieve communication between any pair. In superconducting devices, typically, you are only able to talk to sort of neighbor qubits. So if you have an algorithm which requires a longer distance communication between qubits, there is some cost you have to pay to get the information from one to the other.”

There’s a lot going on here – as there is throughout the quantum computing research community. Zeroing in on ion traps for quantum computing isn’t new but it hasn’t received the same notice that semiconductor-based superconducting approaches have á la IBM, Google, D-Wave, Rigetti et. al. NIST (National Institute of Standards and Technology) has put ion trap technology for use as super accurate atomic clocks and a few academic groups have also explored ion trap quantum computing, but without the fanfare attendant other efforts. It turns out ion trap technology – somewhat similar to the mass spec we all know – has several strengths for use in quantum computing.

Brown, Kim, and colleague Christopher Monroe’s (University of Maryland) have written a nice paper on the topic, Co-Designing a Scalable Quantum Computer with Trapped Atomic Ions. Brown is quick to point out 1,000-qubit scale-up ideas presented in the 2016 paper far exceed STAQ’s goal, but that such scaling ambitions do seem reachable over time with ion trap technology.

Here’s brief excerpt from their paper touching on ion technology’s attraction:

“Superconducting circuitry exploits the significant advantages of modern lithography and fabrication technologies: it can be integrated on a solid-state platform and many qubits can simply be printed on a chip. However, they suffer from inhomogeneities and decoherence, as no two superconducting qubits are the same, and their connectivity cannot be reconfigured without replacing the chip or modifying the wires connecting them within a very low temperature environment.

“Trapped atomic ions, on the other hand, feature virtually identical qubits, and their wiring can be reconfigured by modifying externally applied electromagnetic fields. However, atomic qubit switching speeds are generally much slower than solid state devices, and the development of engineering infrastructure for trapped ion quantum computers and the mitigation of noise and decoherence from the applied control fields is just beginning.”

Perhaps a quick (and imperfect) description of ion trap technology is warranted. It’s similar to mass spec. Ions are loaded into traps by generating neutral atoms of the desired element and ionizing the atoms once in the trapping volume. Electrodes (rods) are used to generate forces to contain the ions. RF and laser emissions are used to control the ions, which can be lined in ‘stationary’ chains. Individual ions have their electron states manipulating using lasers which turns them into qubit registers. Brown’s group is using Ytterbium (Yb+) ions whose outer electron shell structure is well-suited for manipulation.

“The trap we use looks like a computer chip, sort of like metal on silicon chip. It’s similar to the four-rod trap (quadrupole) you probably know from mass spec. You cut one of the rods and then you’ve unfolded the trap onto a plate and advantage of that is it allows you to then move the ions around, break the break chains apart, and that sort of thing. It also gives you more control over fields that are containing the ions and the direction of the chain itself. That is housed in a vacuum chamber which is achieved with either vacuum system or with a cryogenic chamber. This is one of the designs questions we are working on right, deciding which way to go,” said Brown.

One important ion trap technology advantage, according to Brown, is the qubit type, something called ‘hyperfine’ qubits. “They basically have no memory error. So unlike many other qubits where you have a constant decay – and it’s all relative to the gate speeds – our relative decay-to-gate-speed is a long, long time. For example, the best result I know of is if you have a microsecond gate time, which is kind of typical for ions, you can have a memory time of ten minutes,” he said.

As explained in their paper, “Qubits stored in trapped atomic ions are represented by two stable electronic levels within each ion, often represented as an effective spin with the two states |↓⟩and |↑⟩corresponding to bit values 0 and 1. The qubits can be initialized and detected with nearly perfect accuracy using conventional optical pumping and state-dependent fluorescence techniques. This restricts the atomic species of trapped ion qubits to those with simple electronic structure (e.g., those with a single valence electron: Be+, Mg+, Ca+, Sr+, Ba+, Zn+, Hg+, Cd+, and Yb+)” Shown below is a schematic from their paper roughly describing a chip-based ion trap.

Co-design is the central tenet for STAQ. “This idea of the software tailored architecture, co-design, is basically we want to make the tools which optimize the mapping of the ideal mathematical algorithm to the actual device of interest. So there are a few things we plan to leverage. One is the at the bottom layer. We often abstract the physics of the quantum device. This has actually been really useful for quantum information as a whole. It allows people to talk about superconducting machines, or ion trap machines, or photon computers, so all these things using the same language. But the underlying physics beneath that gate layer [are] different and there might be some opportunities to simplify some algorithms such that we actually don’t completely remove that abstraction and allow some of the physics [specific to ion trap technology] to seep up to the programmer,” noted Brown.

Building a stack able to take advantage of this flexibility is one of STAQ’s goals. “The idea of the stack is to try to actually do what one of my colleagues says is like a crossword puzzle. We just don’t optimize the algorithm, and then optimize the gate set, and then optimize each gate on the hardware, but we try to modify the gates so that it’s the most appropriate for optimizing the algorithm given the problem,” said Brown.

The breadth of expertise on the STAQ team, said Brown, is a distinct advantage: “We have computer architects. We have quantum information theorists. We have people more on the applications side, and hardware people. You need all those people. You need those different layers working. I think what’s nice is we are reaching a point where these machines are reaching sufficient sophistication that it is easier to find people to think about architecture.”

In some sense flexibility in manipulating ion chains (breaking apart at different lengths, remote entanglement among qubits) allows an almost FPGA-programming-like quality to ion trap quantum computing. “You can do these two-qubit gates between any pair [of ions] and the reason is it’s not like a direct interaction with its neighbor but an interaction which is mediated by the collective motion of the ion chain. In terms of actually mapping algorithms to computers it’s quite nice because if I think about the connection between qubits it’s like a fully connected graph,” said Brown.

“Now that’s not going to scale to 1000 qubits but it’s not clear what the limit is. We know 10 qubits, 20 qubits is no problem. [And] we have some ideas on how to get to 50 qubits but at some point we are going to have to shift the way we put these things together.”

Quantum chemistry is one area of application being examined. “The challenge in doing quantum chemistry on a normal conventional computer is there’s a mismatch between how much classical data we need to store a quantum state,” said Brown. “With a quantum computer you already have this win where there’s a better match. The quantum state on the computer representing the molecule uses a comparable amount of space because they are both in some sense quantum memory. The next thing is each system has kind of its own natural interaction. With an ion trap system, the way the particular gate is performed, the underlying interaction looks a lot looks a lot like a magnetic interaction between two systems. So if the problem you are trying to solve maps nicely to this kind of magnetic interaction, there are actually a lot of shortcuts you can take.”

Given ion trap technology’s flexibility, STAQ hopes to learn whether it may be possible or worthwhile to create application-specific architectures.

“That is one of our big research questions,” according to Brown. “[The issue] is what is the gain there. If you think about a tablet computer or an iPad, it has a facial recognition chip. Its job is just to see faces, right. So we expect that quantum computers will be kind of like that, at least in near term, sort of an extra processor that is interacting with some classical computer. It may turn out to be possible to make quantum processors that are say specifically designed for quantum chemistry problems, that could be a great accelerator for all kinds of applications in chemistry.”

While STAQ plans to leverage the underlying characteristics ion trap technology which might include ASIC-like capabilities, “all of the devices we plan to make will be universal in that they will allow you to do universal quantum computing,” emphasized Brown.

STAQ will also run an annual summer school at Duke aimed at two different audiences, said Brown, one drawn from upper level undergraduate and early graduate school students looking to learn more about quantum information and another group drawn from industry.

Looking at near-term (~18-month) goals, Brown said, “On the algorithm side I hope to identify target algorithms for a computer on the scale of say 60 to 70 qubits. On the experimental side, that first year and a half will be building a new engineering design and building a new system based on our previous experiments with ion traps but moving more towards a functional computer and [something] less like a physics experiment.”

Link to NSF grant: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1818914

Link to Brown’s 2016 paper: https://arxiv.org/abs/1602.02840

Link to earlier HPCwire article: https://www.hpcwire.com/2018/08/07/nsf-invests-15-million-quantum-staq/

Images: Brown paper; NSF

Subscribe to HPCwire's Weekly Update!

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

Top Ten Ways AI Affects HPC in 2019

March 26, 2019

AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization plans for the future, AI workloads are on our minds — how Read more…

By James Reinders

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, provided a brief but insightful portrait of Nvidia’s rese Read more…

By John Russell

ORNL Helps Identify Challenges of Extremely Heterogeneous Architectures

March 21, 2019

Exponential growth in classical computing over the last two decades has produced hardware and software that support lightning-fast processing speeds, but advancements are topping out as computing architectures reach thei Read more…

By Laurie Varma

HPE Extreme Performance Solutions

HPE and Intel® Omni-Path Architecture: How to Power a Cloud

Learn how HPE and Intel® Omni-Path Architecture provide critical infrastructure for leading Nordic HPC provider’s HPCFLOW cloud service.

powercloud_blog.jpgFor decades, HPE has been at the forefront of high-performance computing, and we’ve powered some of the fastest and most robust supercomputers in the world. Read more…

IBM Accelerated Insights

Insurance: Where’s the Risk?

Insurers are facing extreme competitive challenges in their core businesses. Property and Casualty (P&C) and Life and Health (L&H) firms alike are highly impacted by the ongoing globalization, increasing regulation, and digital transformation of their client bases. Read more…

Interview with 2019 Person to Watch Jim Keller

March 21, 2019

On the heels of Intel's reaffirmation that it will deliver the first U.S. exascale computer in 2021, which will feature the company's new Intel Xe architecture, we bring you our interview with our 2019 Person to Watch Jim Keller, head of the Silicon Engineering Group at Intel. Read more…

By HPCwire Editorial Team

Top Ten Ways AI Affects HPC in 2019

March 26, 2019

AI workloads are becoming ubiquitous, including running on the world’s fastest computers — thereby changing what we call HPC forever. As every organization Read more…

By James Reinders

GTC 2019: Chief Scientist Bill Dally Provides Glimpse into Nvidia Research Engine

March 22, 2019

Amid the frenzy of GTC this week – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, Read more…

By John Russell

At GTC: Nvidia Expands Scope of Its AI and Datacenter Ecosystem

March 19, 2019

In the high-stakes race to provide the AI life-cycle solution of choice, three of the biggest horses in the field are IBM, Intel and Nvidia. While the latter is only a fraction of the size of its two bigger rivals, and has been in business for only a fraction of the time, Nvidia continues to impress with an expanding array of new GPU-based hardware, software, robotics, partnerships and... Read more…

By Doug Black

Nvidia Debuts Clara AI Toolkit with Pre-Trained Models for Radiology Use

March 19, 2019

AI’s push into healthcare got a boost yesterday with Nvidia’s release of the Clara Deploy AI toolkit which includes 13 pre-trained models for use in radiolo Read more…

By John Russell

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

Oil and Gas Supercloud Clears Out Remaining Knights Landing Inventory: All 38,000 Wafers

March 13, 2019

The McCloud HPC service being built by Australia’s DownUnder GeoSolutions (DUG) outside Houston is set to become the largest oil and gas cloud in the world th Read more…

By Tiffany Trader

Quick Take: Trump’s 2020 Budget Spares DoE-funded HPC but Slams NSF and NIH

March 12, 2019

U.S. President Donald Trump’s 2020 budget request, released yesterday, proposes deep cuts in many science programs but seems to spare HPC funding by the Depar Read more…

By John Russell

Quantum Computing Will Never Work

November 27, 2018

Amid the gush of money and enthusiastic predictions being thrown at quantum computing comes a proposed cold shower in the form of an essay by physicist Mikhail Read more…

By John Russell

The Case Against ‘The Case Against Quantum Computing’

January 9, 2019

It’s not easy to be a physicist. Richard Feynman (basically the Jimi Hendrix of physicists) once said: “The first principle is that you must not fool yourse Read more…

By Ben Criger

Why Nvidia Bought Mellanox: ‘Future Datacenters Will Be…Like High Performance Computers’

March 14, 2019

“Future datacenters of all kinds will be built like high performance computers,” said Nvidia CEO Jensen Huang during a phone briefing on Monday after Nvidia revealed scooping up the high performance networking company Mellanox for $6.9 billion. Read more…

By Tiffany Trader

ClusterVision in Bankruptcy, Fate Uncertain

February 13, 2019

ClusterVision, European HPC specialists that have built and installed over 20 Top500-ranked systems in their nearly 17-year history, appear to be in the midst o Read more…

By Tiffany Trader

Intel Reportedly in $6B Bid for Mellanox

January 30, 2019

The latest rumors and reports around an acquisition of Mellanox focus on Intel, which has reportedly offered a $6 billion bid for the high performance interconn Read more…

By Doug Black

Looking for Light Reading? NSF-backed ‘Comic Books’ Tackle Quantum Computing

January 28, 2019

Still baffled by quantum computing? How about turning to comic books (graphic novels for the well-read among you) for some clarity and a little humor on QC. The Read more…

By John Russell

It’s Official: Aurora on Track to Be First US Exascale Computer in 2021

March 18, 2019

The U.S. Department of Energy along with Intel and Cray confirmed today that an Intel/Cray supercomputer, "Aurora," capable of sustained performance of one exaf Read more…

By Tiffany Trader

Contract Signed for New Finnish Supercomputer

December 13, 2018

After the official contract signing yesterday, configuration details were made public for the new BullSequana system that the Finnish IT Center for Science (CSC Read more…

By Tiffany Trader

Leading Solution Providers

SC 18 Virtual Booth Video Tour

Advania @ SC18 AMD @ SC18
ASRock Rack @ SC18
DDN Storage @ SC18
HPE @ SC18
IBM @ SC18
Lenovo @ SC18 Mellanox Technologies @ SC18
NVIDIA @ SC18
One Stop Systems @ SC18
Oracle @ SC18 Panasas @ SC18
Supermicro @ SC18 SUSE @ SC18 TYAN @ SC18
Verne Global @ SC18

Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

February 5, 2019

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is “the first distributed and reproducible benchmarking s Read more…

By John Russell

IBM Quantum Update: Q System One Launch, New Collaborators, and QC Center Plans

January 10, 2019

IBM made three significant quantum computing announcements at CES this week. One was introduction of IBM Q System One; it’s really the integration of IBM’s Read more…

By John Russell

IBM Bets $2B Seeking 1000X AI Hardware Performance Boost

February 7, 2019

For now, AI systems are mostly machine learning-based and “narrow” – powerful as they are by today's standards, they're limited to performing a few, narro Read more…

By Doug Black

The Deep500 – Researchers Tackle an HPC Benchmark for Deep Learning

January 7, 2019

How do you know if an HPC system, particularly a larger-scale system, is well-suited for deep learning workloads? Today, that’s not an easy question to answer Read more…

By John Russell

HPC Reflections and (Mostly Hopeful) Predictions

December 19, 2018

So much ‘spaghetti’ gets tossed on walls by the technology community (vendors and researchers) to see what sticks that it is often difficult to peer through Read more…

By John Russell

Arm Unveils Neoverse N1 Platform with up to 128-Cores

February 20, 2019

Following on its Neoverse roadmap announcement last October, Arm today revealed its next-gen Neoverse microarchitecture with compute and throughput-optimized si Read more…

By Tiffany Trader

France to Deploy AI-Focused Supercomputer: Jean Zay

January 22, 2019

HPE announced today that it won the contract to build a supercomputer that will drive France’s AI and HPC efforts. The computer will be part of GENCI, the Fre Read more…

By Tiffany Trader

Move Over Lustre & Spectrum Scale – Here Comes BeeGFS?

November 26, 2018

Is BeeGFS – the parallel file system with European roots – on a path to compete with Lustre and Spectrum Scale worldwide in HPC environments? Frank Herold Read more…

By John Russell

  • arrow
  • Click Here for More Headlines
  • arrow
Do NOT follow this link or you will be banned from the site!
Share This