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Get the latest in top quantum news and research Monday through Friday, summarized for quick reading so you stay informed without missing a qubit.

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Happy reading and onward!

Cierra

Today’s issue includes:

• A space-bound quantum annealing project has finally arrived at the International Space Station—permission to come aboard, captain.

• Atomic clocks, fueled by quantum interactions (and therefore fit the “quantum tech” definition), will be used in space to test the theory of relativity as well as for precision timekeeping applications.

• A quantum graph neural network outperforms classical methods on certain datasets relevant to particle interactions coming out of the LHC.

Editor’s note: I’ve made some changes to the formatting. For those who have read since the early days—my deepest gratitude to you for outlasting those early issues I dare not revisit—you know how I cling to iterative improvement. The end goals remains the same: bring you the latest in quantum happenings with an emphasis on (potential) practical application. If you have comments, feedback, requests, ideas, just want to chat about quantum over virtual coffee, never hesitate to reach out at the email above, or connect with me on LinkedIn.

QUANTUM APPLICATION HEADLINES

🌌 TESTING THE BUILDING BLOCKS FOR SPACE-BASED QUANTUM COMMUNICATION

Several science projects recently set out on a penultimate adventure to the International Space Station. Their arrival was announced this morning, and the crew aboard the station is now tasked with unboxing and setup. Along for the ride? NASA’s quantum annealing experiment — SEAQUE. Since it’s been a minute since the original announcement of the project, a brief 2 years and some change, see below for a refresher:

APPLICATION: The SEAQUE experiment, developed by NASA's Jet Propulsion Laboratory, tests technologies for enabling long-distance quantum communication between quantum computers through a network of space-based nodes.

SIGNIFICANCE: As quantum computers and sensors develop, the need for a secure, high-speed quantum communication network grows. Quantum entanglement allows information to be shared instantly across distances, but establishing this at a global scale requires technologies that can withstand the extreme conditions of space conditions and perform self-repair maintenance—no space walks required here. SEAQUE’s success could be foundational for future quantum cloud computing, secure quantum communications, and a scalable global quantum network.

HOW: SEAQUE tests two main technologies: an entangled photon source based on waveguides, which is smaller and more resilient than previous setups, and a radiation-damage repair technique using lasers to "anneal" defects in detectors. Once attached to the ISS, SEAQUE will produce and count entangled photons and periodically use a laser to mitigate radiation damage, assessing the practicality and general ability of maintaining usable quantum connections in space.

BY THE NUMBERS:

• 6,000+ pounds – Total weight of scientific investigations and cargo delivered to the ISS on the SpaceX CRS-31 mission.

• 2 technologies – Key quantum communication technologies SEAQUE will test: an entangled photon source based on waveguides and a laser-based radiation repair technique.

• 1000s of miles – Potential distance over which SEAQUE’s technology could enable quantum communication between nodes, laying groundwork for a global quantum network.

• 1st-of-its-kind – SEAQUE is the first spacecraft experiment to use a waveguide-based entangled photon source and a laser “annealing” method for radiation repair.

⌚️ TESTING RELATIVITY AND REDEFINING TIME

If it’s been a while since you last brushed up on relativity, consider this: gravity is a warping of spacetime itself, stretching and bending like a cosmic fabric. This means that clocks positioned closer to Earth’s gravity tick more slowly than those farther away, a phenomenon known as gravitational redshift. It’s a prediction made by Einstein’s theory of general relativity, but how do we verify it? The European Space Agency has some ideas that may prove the theory of relativity once and for all, and, in the process, set new standards for global timekeeping—just a minor detail, really.

APPLICATION: The ACES mission, led by the European Space Agency, Airbus Space and Defence, Safran Timing Technologies, and others, will use atomic clocks on the ISS to test Einstein’s theory of general relativity, compare international clocks, and investigate phenomena such as gravitational redshift and potential dark matter effects.

SIGNIFICANCE: Atomic clocks rely on the quantum mechanical principles of atoms to measure time with extreme accuracy. Understanding time and frequency stability in space is important for advancing metrology, testing fundamental physics, and improving global timekeeping. The mission's outcomes may also support the development of in geopotential measurements and potentially redefining the SI second. Comparing space- and ground-based clocks provides also provides unique insights into gravitational effects and time variations.

HOW: ACES integrates two high-stability atomic clocks—PHARAO and SHM—alongside a frequency comparator and microwave/optical links to maintain synchronization between space and ground clocks. Using a microwave link for dual-frequency comparisons and laser-based optical timing, the mission achieves high-precision clock comparisons and time transfer stability, allowing ACES to serve as a relay for ground clock networks worldwide.

BY THE NUMBERS:

• 1 x 10⁻¹⁶ – Target fractional frequency stability for the ACES clocks, providing precision that enables highly accurate space-to-ground comparisons.

• 100 MHz – Frequency of the signal produced by both the PHARAO and SHM clocks, which forms the basis for the ACES clock signal​.

• 6 months – Duration of the commissioning phase after launch, during which the system will be fine-tuned and calibrated.

• 2 years – Operational phase length planned for ACES, during which it will gather extensive data on time and frequency stability.

📊 QUANTUM DATA INTELLIGENCE FOR PARTICLE PHYSICS

In a world driven by data, the mantra 'data is king' resonates across fields. AI models insatiably demand massive datasets—but what about when data is scarce? And just because we like a good challenge, what if the data is both limited and incredibly complex? This is the very scenario where quantum machine learning shines. Despite architectural constraints limiting large datasets, QML thrives in high-complexity domains, making it an ideal tool for data-scarce tasks in fields like high-energy physics.

APPLICATION: Researchers from the Okinawa Institute of Science and Technology Graduate University, Jahangirnagar University, and others have developed a Lorentz-Equivariant Quantum Graph Neural Network (Lorentz-EQGNN) to address the challenge of efficiently processing very large datasets from the Large Hadron Collider. This network integrates symmetry principles, allowing it to better capture complex interactions between particles.

SIGNIFICANCE: The Lorentz-EQGNN relies on quantum computing’s high-dimensional processing power, while reducing parameters and increasing noise resilience. Its design is especially valuable for data-scarce tasks in high-energy physics, potentially leading to more precise jet tagging, event classification, and other particle physics applications.

HOW: The network replaces traditional neural layers with parameterized quantum circuits, using just 4 qubits for testing, which achieved competitive accuracy in identifying particle interactions. Lorentz-EQGNN’s quantum components also ensure it can capture complex particle interactions more efficiently than classical models.

BY THE NUMBERS:

• 4 qubits – Number of qubits used by Lorentz-EQGNN to achieve effective particle classification accuracy.

• 74% accuracy – The Lorentz-EQGNN achieved 74% accuracy on the quark-gluon dataset, exceeding the 57% achieved with classical methods.

• 800 events – The quark-gluon dataset size that achieved the above accuracy, as compared to 8000 for the classical method, demonstrating higher accuracy with less data.

• 38.94s inference time – The model's inference time for the quark-gluon dataset was 38.94 seconds.

RESEARCH HIGHLIGHTS

✨ A quantum rationale-aware graph contrastive learning model improves particle jet tagging in high-energy physics by integrating a quantum rationale generator, enhancing feature extraction and classification accuracy. The model achieved a 77.53% AUC in distinguishing particle types. (AUC is a performance metric in classification models where less than 50% indicates worse than random chance, 50% indicates random chance, and 100% is a perfect model.)

✅ IBM Quantum led a study on a benchmarking method for quantum simulations using universal scaling laws, specifically in Hamiltonian simulations of quantum critical dynamics with up to 133 qubits. By examining defect density scaling in quantum annealing circuits, the study identifies thresholds for reliable circuit depths before noise prevails.

🌊 A variational quantum algorithm is designed to solve multi-dimensional Poisson equations with mixed boundary conditions. Poisson equations are often used to model physical phenomena such as gravitational fields, heat transfer, and fluid dynamics. Results indicate the algorithm’s potential for effectively calculating electric field distributions in semiconductor systems.

NEWS QUICK BYTES

🌐 IonQ has announced its acquisition of Qubitekk, a leader in quantum networking, to strengthen its position in the quantum networking market. This acquisition brings Qubitekk’s team and over 100 patents under IonQ, advancing efforts toward a quantum-enabled internet and expanding IonQ’s offerings in secure communication and distributed quantum computing.

🖥️ IonQ also announced a partnership with Ansys to bring quantum computing into the computer-aided engineering (CAE) industry, aiming to enhance simulation accuracy via quantum-powered solvers. This collaboration is expected to accelerate product development timelines across industries, opening new possibilities for complex simulations that were previously unachievable with classical computing.

⚛️ D-Wave has completed calibration and benchmarking of its new 4,400+ qubit Advantage2 processor, showing performance gains over the current Advantage system. According to the company, the Advantage2 processor solves certain problems in materials science up to 25,000 times faster, doubles qubit coherence time as compared to the previous processor, and improves upon connectivity to support larger problem-solving capabilities.

🔨 QuTech and Fujitsu have partnered to develop a comprehensive blueprint for a scalable quantum computer, covering all essential components from qubit modules to error-correction algorithms. This collaboration leverages advanced technologies like cryo-CMOS electronics and optical connections for modular communication, aligning with Fujitsu's long-term strategy to lead in quantum innovation and sustainability, according to their recent 2024 Integrated Report.

IN CASE YOU MISSED IT:

⚇ USC Professor Sees Quantum Computing as a Specialized Tool Rather Than Consumer Technology by James Dargan

⚇ Taming Chaos: IBM Quantum And Algorithmiq-led Scientists Report Today's Quantum Computers Can Simulate Many-Body Quantum Chaos by Matt Swayne

⚇ Winter is Coming for Quantum Tech: Precision RF Power Achieved at Cryogenic Temperatures by Cierra Choucair

QUANTUM MEDIA

LISTEN

Note: resharing as the embed did not send correctly in the previous issue. In the most recent episode of the Quantum Divide podcast, host Dan Holme sits down with Lorenzo Leandro, a product solution specialist at Quantum Machines. They discuss his work on qubit control and single-photon sources using quantum dots for quantum computing and networking, highlighting advancements in quantum hardware and software aimed at accelerating computation.

WATCH

Nick Harrigan speaks on quantum computing, CUDA-Q, and workforce development:

THAT’S A WRAP.