Good morning, it’s August 12, 2024. You might have noticed a particular absence from the inbox yesterday — you missed me, right? — and that’s because TDQ was busy getting a little refresher.

When I started The Daily Qubit in March of this year, my goal was to find a way to get the best of quantum news and research into your inbox, daily. During this process, I’ve continued to evolve the layout according to feedback and key learnings.

Changes I’ve made — a more intentional design & more digestible headlines. The same quick byte news & research links remain, as well as community features/media from around the web. I’m retiring Sunday’s community TDQ in favor of highlighting community contributions daily, as I see them.

I’ve reduced the singular lengthy research breakdowns to include more succinct summaries and quick links (the number of papers published per publication daily is truly extraordinary). The time I gain from a more streamlined writing process I’ll invest back into the ultimate goal of Universum Labs, as well as other free tools and content for the quantum community.

All that being said, I hope you continue to enjoy TDQ — free, easily accessible, and in your inbox Monday through Friday.

Cheers,

Cierra

Today’s issue includes:

• NSF Center for Quantum Technologies funds research in using quantum computing for AI models to improve natural language processing and complex reasoning while reducing misinformation

• U.S. Federal Government details its $7.1 billion strategy for migrating to post-quantum cryptography

• Classical tensor networks may be the Olympian of compute — a TN algorithm simulates Gaussian boson sampling, challenging quantum advantage

And even more research, news, & events within quantum.

⏩️ Indiana University Bloomington is using QC to accelerate AI 

QUICK BYTE: Researchers at Indiana University Bloomington are working with the NSF-funded Center for Quantum Technologies to develop more energy-efficient AI models through quantum computing, particularly focusing on natural language processing.

DETAILS: 

• Supported by the NSF-funded Center for Quantum Technologies, which partners academic researchers with private sector companies to advance quantum technology

• Focuses on reducing the time and energy costs required to train AI in natural language processing using quantum computing by converting AI algorithms from traditional computing environments to quantum environments and testing these on cloud-based quantum hardware

• Still in the "proof of concept" stage, but the overall vision is to achieve breakthroughs in AI's ability to engage in more complex reasoning as well as avoid misinformation

• IU researchers will regularly collaborate with public and private sector partners, including entities such as the Air Force Research Laboratory, D-Wave, and Amazon Web Services

🧨 The pressure is on — US report stresses that a $7.1 billion strategy is necessary to prepare for PQC

QUICK BYTE: The U.S. Federal Government has released a report outlining its strategy and funding needs for migrating to post-quantum cryptography to protect against future quantum computer threats. The report emphasizes the urgency of starting this migration before quantum computers capable of breaking current encryption methods become operational.

DETAILS: 

• The Federal Government's PQC migration strategy involves a comprehensive cryptographic inventory, prioritizing high-impact systems, and addressing systems that cannot support PQC

• The government-wide cost for transitioning to PQC is approximately $7.1 billion over ten years

• NIST is responsible for optimizing and standardizing PQC algorithms, ensuring their strength and interoperability through an open international process; details coming soon

• NIST's current efforts include testing PQC implementations and addressing the backlog of cryptographic module validations to support the migration

🪑 NIST is keeping everyone on the edge of their seats. The National Institute of Standards and Technology is set to release finalized post-quantum cryptography standards this week to prepare U.S. cyber defenses against the future threat of quantum computing-powered attacks.

💡 Researchers from the University of Chicago have developed a tensor network algorithm that challenges recent claims of quantum advantage by efficiently simulating Gaussian boson sampling experiments, which were previously considered hard for classical computers.

🎨 BlueQubit Inc. and Quantum Art Ltd. have secured a $2.2 million grant from the Israel-U.S. BIRD Foundation to develop and optimize quantum machine learning algorithms and quantum processor configurations, with hopes to make quantum technology more accessible for enterprise users.

🔗 Ooredoo is providing QR 2,800,000 ($769,019) to Hamad Bin Khalifa University's Qatar Centre for Quantum Computing to support the development of Qatar's first quantum communication testbed, which is pursuing 100% security in the face of post-quantum cybersecurity concerns and the quantum future of the internet.

🛡️ A team at the Polytechnic University of Milan has created high-fidelity entangled four-photon GHZ states on a photonic chip, which significantly enhances secure quantum communication. These GHZ states enable quantum secret sharing, where any unauthorized access is instantly detectable, making them vital for securing sensitive data and integrating quantum systems into future communications and computing infrastructures.

⛔️ A new study from MIT, Ecole Normale Superieure, University of Virginia, and Freie Universität Berlin shows that quantum error mitigation techniques, though simpler to implement than full quantum error correction, become increasingly inefficient as quantum computers scale up, challenging their long-term viability.

🌐 QPSOFL is a novel energy-efficient clustering and routing protocol for wireless sensor networks, combining quantum particle swarm optimization with fuzzy logic to enhance network longevity and efficiency.

🌲 A hybrid quantum-classical classification model based on branching multi-scale entanglement renormalization ansatz improves the accuracy and robustness of data classification tasks and is compatible with current quantum devices.

LISTEN

On the most recent edition of The Superposition Guy’s Podcast, Yuval Boger, CMO of QuEra, interviews Chris Bishop, a non-linear multimodal careerist and former musician. They discuss his diverse career journey from music to technology, including his experiences as a studio musician, web producer, and IBM employee, as well as his transition into quantum technologies where he now engages as a freelance consultant, writer, and podcast host. Chris shares insights from hosting and emceeing quantum tech events, emphasizing the significance of adapting to emerging tech fields and the importance of interdisciplinary skills in navigating future careers.

PONDER

• Experts are still largely divided on where quantum advantage rests — is it in number of qubits or the strength of error correction code? Despite the divide and apparent disarray in the trajectory of the field, Jay Gambetta holds firm that now is still “the most exciting time in error correction theory” and who are we to argue?

WATCH

Speaking of error correction — IBM error correction scientists review a recent publication on low-density parity-check error-correcting codes and how these play into the scalability of quantum computation.

• On Thursday, August 15th, QED-C is hosting Quantum Marketplace: Quantum Computing Systems III, a webinar featuring industry leaders from NVIDIA, Microsoft, Pasqal and more — virtual

• On Tuesday, August 20th, Infleqtion is hosting A Different Kind of Quantum Circuit: Exploring Atomtronics with Oqtant, an explanation and demonstration on creating atomtronic circuits —virtual

• On Thursday, August 22nd, D-Wave is hosting Deeper Dive into the new Fast Anneal Feature, a webinar on how Fast Anneal is assisting in scientific discovery —virtual

• On Saturday, August 24th, Washington DC Quantum Computing Meetup is hosting PiQture - A Quantum Machine Learning Library for Image Processing, a discussion of the open-source Python and Qiskit-based library for QML models —virtual

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