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Quantum News Briefs September 12: NOVONIX and SandboxAQ collaborate on breakthrough AI solutions for battery technology; Physicists from ParityQC and the University of Innsbruck develop novel quantum error mitigation method; Riken researchers utilize machine learning for efficient quantum computer error correction + MORE

Quantum News Briefs looks at news in the quantum industry.
By Sandra Helsel posted 12 Sep 2023

Quantum News Briefs September 12:

NOVONIX and SandboxAQ collaborate on breakthrough AI solutions for battery technology

NOVONIX Limited, a leading battery materials and technology company, and SandboxAQ, an enterprise SaaS company that combines artificial intelligence (AI) with quantum analysis (AQ) to address some of the world’s most challenging problems, announced on September 11 they will collaborate to predict the lifespan of lithium-ion batteries, by leveraging SandboxAQ’s AI-driven chemical simulation software and NOVONIX’s Ultra-High Precision Coulometry (UHPC) technology and extensive battery cell prototyping and testing capabilities.
With the rapidly growing demand for lithium-ion batteries required to support the global electrification trend, optimizing battery performance and cycle life on a timely basis has never been more critical to enhance performance and reduce battery costs. NOVONIX is focused on developing key technologies and materials that are needed for long-life, high-performance battery applications. This enhanced data and analytics offering complements NOVONIX’s UHPC testing equipment and R&D prototyping and testing services to provide actionable information faster for the battery industry. The resulting models will be used for data products and services in the first half of 2024, building on NOVONIX’s purpose-built, proprietary, battery data platform.
Predicting lithium-ion battery performance and degradation has been an ongoing challenge due to the complexity of the electrochemical system inside a lithium-ion cell, which depends on many factors such as cell chemistry, temperature, cycle rate and operational voltage windows, as well as physical cell design parameters. Presently, the battery industry performs extensive lifetime and performance assessments, which can take years for the necessary analytical results to drive cell and material improvements.
Nadia Harhen, General Manager of Simulation & Optimization at Sandbox AQ, said: “AI and Quantum technologies will revolutionize nearly every industry. Collaborating with the scientists at NOVONIX to deploy machine learning algorithms and quantum simulations for battery R&D, we have an opportunity for immediate and substantial impact across application areas in energy storage. SandboxAQ’s predictive modeling technologies, paired with NOVONIX’s industry-leading expertise, will transform the battery industry’s ability to make informed decisions around chemistries, processes, cells, and technologies at every stage of research and manufacturing.”
Dr. Chris Burns, CEO of NOVONIX said: “We are thrilled to partner with SandboxAQ and leverage their transformational AI and Quantum (AQ) solutions to accelerate innovation in the evolving battery landscape. Click here to read announcement in-entirety.

Physicists from ParityQC and the University of Innsbruck develop novel quantum error mitigation method

A group of physicists within ParityQC and the University of Innsbruck– Anita Weidinger, Glen Bigan Mbeng and Wolfgang Lechner – have developed a novel strategy to mitigate errors in quantum computers, based on the ParityQC Architecture. Quantum News Briefs summarizes the announcement.
The paper outlining the invention has now been published in the journal Physical Review A, as an Editors’ Suggestion, highlighting research that is considered of particular interest, importance, or clarity. This new error mitigation technique exploits the redundant encoding of the ParityQC Architecture to successfully mitigate errors in quantum optimization algorithms. This promising solution aims to tackle the issue of hardware noise and errors which limit the performance of current quantum devices.
The authors demonstrate that with the ParityQC Architecture it is possible to efficiently mitigate errors in near-term algorithms. The proposed error mitigation approach is based on the ParityQC Architecture (also known as LHZ Architecture), a novel type of encoding that was discovered in 2015 and is now a patented technology of ParityQC. The Architecture uses a redundant encoding of logical variables to solve optimization problems on quantum chips. The physicists found that this redundancy can be exploited to mitigate errors in quantum optimization algorithms, specifically the Quantum Approximate Optimization Algorithm (QAOA).
The authors demonstrate the effectiveness of the proposed method by applying it to a set of benchmark problems. In the context of QAOA, the paper shows that errors can be significantly mitigated through this new approach, leading to an increased accuracy of the results. As stated by Wolfgang Lechner, co-founder and co-CEO of ParityQC and professor at the
University of Innsbruck: “This method can find meaningful applications in the real world to solve a wide range of optimization problems by significantly improving the performance of QAOA. This can close the gap between the imperfect and “noisy” near-term hardware and fully error-corrected codes.” Click here to read the original article in-entirety.

Riken researchers utilize machine learning for efficient quantum computer error correction

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these devices practical—using an autonomous correction system that despite being approximate, can efficiently determine how best to make the necessary corrections. Quantum News Briefs summarizes from September 7 Phys.org.
The main challenge towards putting quantum computers into practice stems from the extremely fragile nature of quantum superpositions. Indeed, tiny perturbations induced, for instance, by the ubiquitous presence of an environment give rise to errors that rapidly destroy quantum superpositions and, as a consequence, quantum computers lose their edge.
In this work, the researchers leveraged machine learning in a search for error correction schemes that minimize the device overhead while maintaining good error correcting performance. To this end, they focused on an autonomous approach to , where a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements.
They also looked at “bosonic qubit encodings”, which are, for instance, available and utilized in some of the currently most promising and widespread machines based on superconducting circuits.
The group found that a surprisingly simple, approximate qubit encoding could not only greatly reduce the device complexity compared to other proposed encodings, but also outperformed its competitors in terms of its capability to correct errors.
Yexiong Zeng, the first author of the paper, says, “Our work not only demonstrates the potential for deploying machine learning towards quantum error correction, but it may also bring us a step closer to the successful implementation of quantum error correction in experiments.” Click here to read complete September 7 article in Phys.org

Q-CTRL, Diraq partner to secure millions for three public-sector quantum projects

Q-CTRL, a global leader in developing useful quantum technologies through quantum control infrastructure software, and Diraq, a leading innovator in Silicon-based quantum computing, today announced they will be partnering on three multi-million-dollar projects to expand the commercial adoption of quantum computing. It represents the first stage of an anticipated partnership delivering new, high-impact quantum computing capabilities to the global market, from Australia.
The two Australian quantum technology companies will deliver three projects together: two from the NSW Office of the Chief Scientist and Engineer’s Quantum Computing Commercialisation Fund (QCCF) and one from the U.S. Army Research Office. Q-CTRL and Diraq are sharing responsibilities on the projects: Diraq will develop and provide access to its Silicon quantum computing hardware and Q-CTRL will build and integrate its quantum infrastructure software solutions to deliver maximum end-user value from the hardware.
Diraq is a world leader in building quantum processors using silicon ‘quantum dot’ technology, leveraging over two decades in engineering and research expertise at UNSW Sydney and backed by an extensive IP portfolio.
“The partnership between Diraq and Q-CTRL exemplifies our shared commitment to driving the next era of innovation in the quantum computing industry, both locally in Australia and globally,” said Diraq CEO and Founder, Andrew Dzurak. “We are delighted to collaborate with Q-CTRL, leveraging our specialised areas of expertise to jointly drive successful outcomes across these transformative projects.”
Australian companies and University teams have long engaged with the US Army Research Office in support of quantum computing capability development. In the current project led by Diraq, the two teams will focus on developing novel techniques to operate and optimize next-generation Silicon quantum processors. The ARO R&D program now aligns with quantum technology initiatives supported under the trilateral AUKUS agreement’s Pillar II. AUKUS Pillar II is aimed at enhancing capabilities and interoperability with a focus on cyber capabilities, AI, quantum technologies and undersea capabilities.

Sandra K. Helsel, Ph.D. has been researching and reporting on frontier technologies since 1990.  She has her Ph.D. from the University of Arizona.

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An artistic representation of the Golden Rule of Three, proposed by researchers from NUS, showing how to align different molecules to create a supermoire lattice.