Fireside Chat with LBNL’s Advanced Quantum Testbed Director

By John Russell

October 26, 2021

Last week, Irfan Siddiqi led a “fireside chat” with a few media and analysts to introduce the Department of Energy’s relatively new Advanced Quantum Testbed (AQT), which is based at Lawrence Berkeley National Laboratory. AQT is one of two DOE quantum testbeds working with commercial and academic researchers and (broadly) with the new National Quantum Information Sciences (QIS) Research Centers, which were created under the National Quantum Initiative Act (NQIA, 2018).

Siddiqi is the director of the AQT which focuses on superconducting quantum circuits. He is also director of one of the new QIS research centers – the Quantum Systems Accelerator (QSA) with LBNL as the lead institution. The other testbed is the Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia, which is focused on trapped ions.

The conversation during Siddiqi’s virtual fireside chat, along with a few colleagues from the AQT team, was wide-ranging covering quantum hardware technology, the need for better control systems, hybrid algorithm efforts, promising early projects, and the coming “quantum winter” – yes, he said, one is coming, the question is how severe. He, no surprise, is an optimist.

Interestingly, the DOE testbed projects are somewhat apart (funding path) from the QIS research centers.

“Although there is overlap between the research done at the National QIS centers and the quantum computing testbeds, their missions have an essential distinction. The testbeds are externally-facing and community-focused, i.e., making quantum processors (hardware and software) available to scientists, startups, and the broader community, especially those who do not produce their processors, for deep collaborative research. On the other hand, the national QIS centers conduct internal research by their respective team members,” said LBNL’s Monica Hernandez.

AQT director Irfan Siddiqi (Credit: Berkeley Lab)

Not surprisingly there is still close collaboration between AQT and QSA as both are headed by Siddiqi. For example, some AQT researchers working on electronic controls systems for superconducting circuits are also QSA-funded researchers.

The bubbling U.S.-funded quantum organizational acronym stew (tough phrase I know) is still relatively unfamiliar. That said, the broad shape of the U.S. quantum landscape is slowly clarifying. The five national QIS research centers and the Quantum Economic Development Council (QED-C) are emerging as dominant influencers. QED-C’s mission is “to enable and grow a robust commercial quantum-based industry and associated supply chain in the United States” and works closely with the National Institute of Standards and Technology (NIST).

(Included at the end of the article is a brief description of the five centers along with a few comments on governance/collaboration from Siddiqi who is this year’s chair of the Directors’ Council, which coordinates activities among the national QIS research centers.)

Meanwhile, the DOE testbeds are busily ramping up their activities. AQT specializes in superconducting qubit technology. It has on-site wafer fabrication capabilities and is currently spending much of its effort on control systems and algorithm testing/development. Papers are one form of deliverable but direct sharing of learnings with users and with other centers is also key. As a DOE-funder facility the underlying assumption is that the learnings gained will inform DOE mission applications.

On average, said Siddiqi, there are 5-6 users onsite at LBNL’s AQT. Of the roughly 20 proposals submitted in fall of 2020, seven were eventually undertaken; they spanned algorithms or simulations, circuit characterization or control; control hardware; and processor architectures. Much of the mission is to explore and develop approaches to make practical use of noisy intermediate scale quantum (NISQ) computers.

While it is still early days for AQT, Sidiqqi cited a project in which AQT researchers improved a classical-inspired, quantum algorithm by using their understanding of circuit noise. They were looking at nuclear scattering problems, where you have two or multiple objects coming together and you want to figure out how they arrange themselves after. It’s a common chemistry problem as well.

“We looked at different ways to do quantum chemistry, in particular with an algorithm called QITE. The idea with QITE is that it is an algorithm that is actually inspired by classical computation. In classical chemistry, there is a way to figure out the energy spectrum but it’s kind of funky. You take (convert) time to imaginary time. I won’t elaborate much on this but what happens here is you go ahead and turn time into a different variable and at the end of the day, everything dies except the one answer you want. In a nutshell, this algorithm works.

“There’s a quantum version of it we wanted to run, and we ran it, and again it was kind of the same story [that] the vanilla version of it doesn’t run well. We put in technologies that we had developing at the testbed and it runs much better,” said Siddiqi, citing a paper (Leveraging Randomized Compiling for the QITE Algorithm) now posted on arXiv.

Abstract excerpt from the paper: “The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.”

“Part of [the work] looks at how can you understand the noise in your system. We did explicit calculations of that. How can you change the noise – not all noise is bad; well actually all noise is bad, but certain noise is worse than others,” said Siddiqi. By converting one type of noise into another, they we were able to run more complicated algorithms.

Main AQT cryogenic dilution refrigerator (Credit: Berkeley Lab

In a more hardware vein, AQT has also been actively soliciting control chips and control circuit designs from external partners for testing on its various qubits types. “We have several different chips that we can run in our fab facility. So for transmon qubits we can go up to eight qubits in a ring as the standard workhorse that we use to do a lot of science. We have 10 [transmon qubits] that are now, for example, in a parametric coupling scheme. We [also] have noise protected qubits, fluxonium qubits, bosonic encoded cat qubit, that are coming out,” said Siddiqi.

The idea is to be able to explore the full range of semiconductor-based superconducting qubit technology.

While external users aren’t able to use AQT’s fabrication facilities, said Siddiqi, they can submit design ideas which AQT assesses and sometimes implements. AQT also houses a 1,000 microwatt dilution refrigerator, and is in the process of updating LBNL building B73 to be a ‘central hub’ for quantum research.

Not surprisingly, control electronics is a critical area of investigation. Currently, it is necessary to use cables to connect every qubit on a processor housed inside the dilution refrigerator to control electronics at room temperature. But there’s only so many control cables one can stuff into even a large dilution refrigerator. Integrated controls are needed. Kasra Nowrouzi leads hardware activities at AQT including development and testing of control signals.

“We have two parallel tracks of control solutions that we make available to both internal researchers and external users. The hardware used today to generate the signals to operate the processing of information on quantum processors, and then to read out and analyze that data, is all based on FPGAs. In industry, these solutions are quite expensive. They’re not purpose-designed necessarily. At national labs, we have the advantage of having developed a lot of this infrastructure internally,” said Nowrouzi.

“We have already open-sourced some of that work, both the hardware and the firmware, and we are in the process of open-sourcing more of it. We have also started pooling our resources with other national labs. For example, aside from us, the main major national lab working on this is Fermilab. We just started a collaboration with them and they’ve applied to be one of the users at our facility here,” he said.

AQT quantum processor unit (Credit: Berkeley Lab)

The longer-term objective, said Nowrouzi, “is coming up with a shared vision that would create hardware that would take advantage of all of the expertise existing within both of our teams with the stated goal of open-sourcing all of this.” Once opened sourced, the cost of such control would be a fraction of what they are now, “maybe 10 percent of what it would cost if you were to buy it from a commercial entity.”

Some external AQT collaborators have already incorporated results into their products, said Sidiqqi citing Quantum Benchmark (now owned by Keysight) a make of quantum circuit validation, characterization and error mitigation tools. The overall briefing by Sidiqqi touched on many topics. Here are just three of many bullets:

  • Quantum Annealing. ATQ doesn’t work with QA systems citing cascading error issues not unlike what happens in analog computing. That said, Sidiqqi is intrigued by the potential of hybrid approaches. “What is interesting is if you combine, let’s say one gate with your annealing piece, it gets a lot better. I would be tremendously interested in that because if I just have to do one gate that’s not so bad. I don’t have to [control] all microwaves in terms of DC wires so I think there may be something very interesting in the hybrid domain.”
  • Hybrid Algorithms. “The idea seems absolutely brilliant. Right. You find the one step which is hard to do classically and do that quantum mechanically. Why do all the other things using the quantum system [as] it cost you extra gates extra hardware so on so forth,” he said. It turns out the so-called “barren plateau” and particle swarm challenges are not trivial. The devil is in the details. He favors the classical-inspired approach, as was used with the QITE project mentioned earlier.
  • Quantum chip clustering. Given the challenge of scaling up the number of qubits on a single processor, many think clustering devices will be how we scale quantum computing. “I very much think that modularization at the chip level is a very good direction to follow up, and we are actually very actively looking at this. The idea that you can build one monolithic chip with a very, very large number of qubits with a very very low defect density is a hard problem. There may be useful things one can do about having modules or entanglements contained and communicating them to other modules,” said Sidiqqi.

Two related questions posed to Siddiqi drew pragmatic responses. Given the hype surrounding quantum technology but relatively distant payoff – though large – was he worried about a quantum winter like the one suffered by AI research 30 years ago when it failed to deliver quickly? Also, what is a realistic timeframe for expecting real-world quantum applications?

“Do we believe that there will be winter, yeah,” said Siddiqi. “but I think our job is to sort of smooth that out, right, to take out those bumps. The deep winters, the ones that are problematic, are when you forget the technology. That happens when for example people write papers 40 years ago and then they’re forgotten and they have to be rediscovered and that’s painful because that’s just time lost.

“So if we can take out that particular lag, and whether the ball is in academia a little bit longer or in a national lab before it’s passed back to the industry to take out those kinks, I think we’re all on board for that.”

As for when we’ll see practical quantum computing emerge:

“I could answer the usual physicist’s way [saying] somewhere between 10 and 100 years.
But I think a decade is not crazy. I think for me, it’s the question of whether it’s in my lifetime or not, right, and I think it’s in my lifetime. So, having been on the funding side, the government is aware that the time-scale is slow. We don’t buy too much of the hype from the companies, and we would put it at something like 10 to 15 years is a reasonable time to expect something.”

 

ADDENDUM

The Gathering Shape of U.S-Funded Quantum Efforts

Tracking the organizational structure of U.S.  QIS efforts can be challenging. There were, of course, many active commercial and scattered DOE and academic quantum research programs before passage of the National Quantum Initiative Act (NQIA, 2018). On the commercial side, D-wave has been at it for more than 20 years. IBM has likewise been pursuing QC for a very long time. The last five years have seen something like a gold rush on the commercial side though no one has yet to strike it rich.

On the academic and government side NQIA has become a galvanizing force, supported largely by Department of Energy funding. Now, after roughly three years, a clearer organizational picture of its various NQIA programs is emerging with the National QIS research centers and the Quantum Economic Development Consortium (QED-C) taking prominent roles. Here’s a snapshot of the five QIS Centers (descriptions adapted from QIS center homepage) sitting loosely atop U.S. government QIS research.

  • Q-NEXT – Next Generation Quantum Science and Engineering (director, David Awschalom; lead Institution, Argonne National Laboratory). Q-NEXT will create a focused, connected ecosystem to deliver quantum interconnects, to establish national foundries, and to demonstrate communication links, networks of sensors, and simulation testbeds. In addition to enabling scientific innovation, Q-NEXT will build a quantum-smart workforce, create quantum standards by building a National Quantum Devices Database, and provide pathways to the practical commercialization of quantum technology by embedding industry in all aspects of its operations and incentivizing start-ups.
  • C2QA – Co-design Center for Quantum Advantage (director, Andrew Houck; lead Institution, Brookhaven National Laboratory) C2QA aims to overcome the limitations of today’s noisy intermediate scale quantum (NISQ) computer systems to achieve quantum advantage for scientific computations in high-energy, nuclear, chemical and condensed matter physics. The integrated five-year goal of C2QA is to deliver a factor of 10 improvement in each of software optimization, underlying materials and device properties, and quantum error correction, and to ensure these improvements combine to provide a factor of 1,000 improvement in appropriate computation metrics.
  • SQMS – Superconducting Quantum Materials and Systems Center (director, Anna Grassellino; lead institution, Fermi National Accelerator Laboratory) The primary mission of SQMS is to achieve transformational advances in the major crosscutting challenge of understanding and eliminating the decoherence mechanisms in superconducting 2D and 3D devices, with the goal of enabling construction and deployment of superior quantum systems for computing and sensing. In addition to the scientific advances, SQMS will target tangible deliverables in the form of unique foundry capabilities and quantum testbeds for materials, physics, algorithms, and simulations that could broadly serve the national QIS ecosystem.
  • QSA – Quantum Systems Accelerator (director, Irfan Siddiqi; lead institution, Lawrence Berkeley National Laboratory) QSA aims to co-design the algorithms, quantum devices, and engineering solutions needed to deliver certified quantum advantage in scientific applications. QSA’s multi-disciplinary team will pair advanced quantum prototypes—based on neutral atoms, trapped ions, and superconducting circuits—with algorithms specifically constructed for imperfect hardware to demonstrate optimal applications for each platform in scientific computing, materials science, and fundamental physics. The QSA will deliver a series of prototypes to broadly explore the quantum technology trade-space, laying the basic science foundation to accelerate the maturation of commercial technologies.
  • The Quantum Science Center – QSC (director: David Dean; lead institution, Oak Ridge National Laboratory) QSC is dedicated to overcoming key roadblocks in quantum state resilience, controllability, and ultimately scalability of quantum technologies. This goal will be achieved through integration of the discovery, design, and demonstration of revolutionary topological quantum materials, algorithms, and sensors, catalyzing development of disruptive technologies. In addition to the scientific goals, integral to the activities of the QSC are development of the next generation of QIS workforce by creating a rich environment for professional development and close coordination with industry to transition new QIS applications to the private sector.

The testbeds, like AQT at LBNL, are part of the separately-funded Quantum Testbeds for Science (QTS) program from DOE. These are separate from the national QIS centers. LBNL along with Oak Ridge National Laboratory and Lawrence Livermore National Laboratory were part of Quantum Testbed Pathfinder program, which predates the QTS. When the QTS program started up, LBNL had its Pathfinder program rolled into it, so that it wouldn’t have two projects simultaneously with very similar purposes.

That all makes sense but can be confusing to outside observers seeking to understand the overall org chart of U.S.-funded quantum research efforts.

In fact, the Pathfinder program is continuing. “We are getting ready to put on a workshop in December to help us determine the next phase of QTS and Pathfinder,” Raphael Pooser, a Pathfinder PI and ORNL member, told HPCwire. “Think of QTS as the experimental part of the program and Pathfinder as the theoretical (with a fair bit of experimental testing thrown in) that helps develop the benchmarks and tests that the testbeds can use.

“With the next workshop, we hope to understand what all of the recent advances in the field mean for this program at DOE and figure out what we need to do to maximize benefit of the programs in that context. Should DOE build more testbeds? Are there new testing techniques we can use or port between various platforms to make it easier to measure and diagnose performance across many different devices?”

It’s not always clear how all of these activities – quantum research centers, various flavors of testbeds, etc. – are being coordinated. There is no Quantum Czar per se. There is now a council of center directors that coordinates center activities and there is a chairperson of that council. This year, Siddiqi is the council’s chair but the position rotates.

Siddiqi said, “We are endeavoring at the moment to come up with a joint operating plan that says how are we going to synchronize and energize efforts in workforce development, in internships, and having folks move around the country. That’s something we’re actively working on.”

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