By Sandra Helsel posted 18 Aug 2022
Quantum News Briefs opens today with Multiverse’s CTO Sam Mugel analysis of quantum computing’s role in predicting and preventing future economic downturns, followed by Tristan Greene’s look at the recent dispute between DeepMind’s quantum AI findings and Russian and Korean scientists’ rebuttal that those findings are not accurate or not relevant. This is followed by a look at the Quantum Science Center’s focus on topological quantum computing and MORE.
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Could Quantum Computing Better Predict And Prevent Economic Downturns?
Sam Mugel, Ph.D., CTO of Multiverse Computing, wrote recently in Forbes about the potential benefit of quantum computing in predicting and therefore predicting economic downturns. Quantum News briefs summarizes here.
Mugel’s article is timely with growing fears of an economic downturn. Global economies are responding to a slate of evolving pressures related to the global pandemic, supply chain disruptions, geopolitical conflicts and the highest inflation rates in decades, to name a few. Providing organizations with insights into the behavior of economies has huge value, yet we are notably bad at predicting economic crises.
Economies have continuously evolving networks that include multiple players and assets. This complexity of possible configurations is what makes them so difficult to model effectively, even when using today’s most powerful supercomputers.
Attention is turning towards quantum’s use as a tool for codifying quantitative macroeconomic problems, revealing how wealth evolves over time in response to changes or perturbations within the financial network. It has already been shown that quantum annealers, devices originally developed to solve complex optimization problems, are ideally suited to this work.
As tools for simulating complex networks develop further over the next decade, central banks and financial institutions will be much better equipped to improve economic resilience. Insights into vulnerabilities will help protect financial institutions and entities like pension funds from the shock of exceptional events likely to happen over the lifetime of portfolios. It will also help central banks mount a better defense against future efforts to weaponize the economy.
Muguel concludes, “While quantum computing has far to travel in realizing its full potential, the technology is already generating valuable new insights and pointing to solutions in market forecasting and stability where none existed before.” Read Muguel’s original article here.
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DeepMind Disagrees with Russian Scientists Who Disputed Quantum AI Research Findings
Tristan Greene of NextWeb’s Neural covered a recent disagreement facing DeepMind, an Alphabet research company based in London, that published a fascinating research paper eight months ago in which it claimed to have solved the huge challenge of “simulating matter on the quantum scale with AI.” Now, a group of academic researchers from Russia and South Korea may have uncovered a problem with the original research that places the paper’s entire conclusion in doubt. Quantum News Briefs summarizes below; Greene’s original and extensive review of this disagreement can be read here.
In December, DeepMind published a paper entitled “Pushing the frontiers of density functionals by solving the fractional electron problem.” In this paper, the DeepMind team claims to have radically improved current methods for modeling quantum behavior through the development of a neural network.
DeepMind’s paper made it through the initial, formal review process. Now, in August 2022, a team of eight academics from Russia and South Korea have published a comment questioning DeepMind’s conclusion.
Per a press release from Skolkovo Institute of Science and Technology: “DeepMind AI’s ability to generalize the behavior of such systems does not follow from the published results and requires revisiting.”
In our opinion, the improvements in the performance of DM21 on the BBB test dataset relative to DM21m may be caused by a much more prosaic reason: an unintended overlap between the training and test datasets.
If this is true, it would mean DeepMind didn’t actually teach a neural network to predict quantum mechanics. The academics are disputing how DeepMind’s AI came to its conclusions. DeepMind was quick to respond. The company published its response on the same day as the comment and provided an immediate and firm rebuke:
We disagree with their analysis and believe that the points raised are either incorrect or not relevant to the main conclusions of the paper and to the assessment of general quality of DM21.
Greene closes with a provocative prediction: “Eventually, as AI systems continue to scale, we could reach a point where we no longer have the tools necessary to understand how they work. When this happens, we could see a divergence between corporate technology and that which passes external peer review.”
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One Goal of ORNL’s Quantum Science Center Is to Help Deliver Topological Quantum Computing
The Quantum Science Center (QSC), headquartered at Oak Ridge National Laboratory, is one of five centers created by the National Quantum Initiative Act in 2018 and run by the Department of Energy. John Russell of HPCWire took a deep dive into QSC; Quantum News Briefs summarizes here.
QSC’s goal is to help deliver topological quantum computing. This approach depends on an as-yet unproven particle, Marjorana, one of a class of mysterious non-abelian anyons that follow non-abelian statistics.
The race for topological quantum computing is a bit of a gamble. There are skeptics. Microsoft has been the biggest champion of the topological approach and is a close QSC collaborator. Interestingly, in its effort to flesh out topological quantum computing, QSC is leveraging existing NISQ systems.
However, there is a great deal more than chasing non-abelian particles going on at QSC which is digging into materials science, algorithm development, and sensors, although much of what is being done in these areas is intended to support development of topological computers.
It is perhaps noteworthy that the QIS centers seem to be trying to carve out identities beyond the labs at which they are headquartered. Newly named director of QSC Travis Humble said, “You’re exactly right. There’s so much interest in this topic at the moment that anyone who has an institution is ill prepared to be able to take it all on. So for example, at Oak Ridge, we’re the lead for Quantum Science Center, but there are 17 partners overall, that are contributing to it, and honestly, if we took any one of them away, we’d end up with a gap in our capabilities.”
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2D Array of Electron and Nuclear Spin Qubits Opens New Frontier in Quantum Science
Researchers at Purdue University have opened a new frontier in quantum science and technology, enabling applications like atomic-scale nuclear magnetic resonance spectroscopy, and to read and write quantum information with nuclear spins in 2D materials.mAs published Monday (Aug. 15) in Nature Materials, the research team used electron spin qubits as atomic-scale sensors, and also to effect the first experimental control of nuclear spin qubits in ultrathin hexagonal boron nitride.
“This is the first work showing optical initialization and coherent control of nuclear spins in 2D materials,” said corresponding author Tongcang Li, a Purdue associate professor of physics and astronomy and electrical and computer engineering, and member of the Purdue Quantum Science and Engineering Institute. “Now we can use light to initialize nuclear spins and with that control, we can write and read quantum information with nuclear spins in 2D materials. This method can have many different applications in quantum memory, quantum sensing, and quantum simulation.”
In this work, Li and his team established an interface between photons and nuclear spins in ultrathin hexagonal boron nitrides. The nuclear spins can be optically initialized – set to a known spin — via the surrounding electron spin qubits. Once initialized, a radio frequency can be used to change the nuclear spin qubit, essentially “writing” information, or to measure changes in the nuclear spin qubits, or “read” information. Their method harnesses three nitrogen nuclei at a time, with more than 30 times longer coherence times than those of electron qubits at room temperature. And the 2D material can be layered directly onto another material, creating a built-in sensor.
