Welcome to the Quantum Realm.

Enjoy today’s breakdown of news, research, & events within quantum.

🔋 The Fermi-Hubbard lattice model may provide insights into high-temperature superconductivity — relevant to quantum technology, medical imaging, and energy-efficient power generation. Researchers from Quantinuum and the Joint Center for Quantum Information and Computer Science explore time-series algorithms on a quantum computer to determine the finite-energy properties of the FH model. Plus, IonQ’s new partial error correction technique, JILA’s $15 million NASA quantum sensors, and Quantum Odyssey on an Apple device near you.

🗓️UPCOMING

Thursday, August 15th | QED-C Quantum Marketplace: Quantum Computing Systems III

Tuesday, August 20th | A Different Kind of Quantum Circuit: Exploring Atomtronics with Oqtant by Infleqtion

Thursday, August 22nd | D-Wave Deeper Dive into the new Fast Anneal Feature

Saturday, August 24th | PiQture - A Quantum Machine Learning Library for Image Processing

📰QUANTUM QUICK BYTES

🎩 IonQ’s newest trick — Clifford Noise Reduction: IonQ’s new partial error correction technique, Clifford Noise Reduction, improves quantum computing accuracy using a 3:1 qubit overhead and reduces noise in Clifford gate operations, outperforming existing error correction techniques that require far more qubits. This qubit count and solution time balance also shows improved fidelity for applications with up to 85 qubits. IonQ plans to integrate CliNR into the upcoming IonQ Tempo system as part of its ongoing efforts to advance quantum computing performance and scalability.

🔬 JILA researchers use machine learning and atom interferometry for NASA-funded quantum sensors: At the University of Colorado Boulder, JILA researchers Catie LeDesma and Kendall Mehling developed advanced quantum sensors by integrating machine learning with atom interferometry. Laser light crystals move atoms and measure accelerations, improving precision and efficiency compared to traditional techniques. Supported by a $15 million NASA grant, they hope to improve quantum metrology for applications such as climate monitoring.

💼 Microsoft’s departure from Sydney quantum lab was not the end of an era: Earlier this year, Microsoft closed its Microsoft Quantum Sydney laboratory at Sydney University’s Nanoscience Hub, offering the team a chance (and monetary compensation) to relocate to Seattle. Instead, Professor David Reilly and his team chose to stay in Australia and combine their various expertise to form a startup. This move allows them to maintain independence and potentially greater impact within the global quantum computing ecosystem. Meanwhile, Microsoft will consolidate its quantum resources globally, moving the Sydney lab's work to its core team.

🏥 New fellowship program integrates quantum tech and AI into biomedical research and patient care: The Novo Nordisk Foundation and Cleveland Clinic have launched the Cleveland Clinic – Denmark: Quantum-AI Biomedical Frontiers Fellowship Program to integrate quantum technologies and AI into biomedical research and healthcare. By combining Denmark’s quantum and AI expertise with Cleveland Clinic’s medical research capabilities (and a gratuitous $6.2 million over three years), 12 researchers will be fully funded to work in either Cleveland or Denmark. Researchers will focus on applications for improving diagnostic precision, drug discovery, clinical trial optimization, and personalized medicine. The initiative also includes industrial placements to translate research into practical medical solutions.

📱 Quantum Odyssey: Essentials is officially available on Apple devices: Quantum Odyssey: Essentials is a mobile adaptation of the acclaimed PC game Quantum Odyssey, (coming to Steam soon) designed to teach quantum computing through interactive puzzles and learning modules. It features over 250 quantum computing challenges and a dozen fully narrated tutorials, covering concepts from qubits to complex algorithms like Grover's Quantum Search. No prior knowledge of math or coding is required, as the game guides users through designing quantum algorithms. Players can convert their visual solutions into executable quantum code, optimizing or creating new algorithms on IBM's quantum hardware.

🧠 Quantum Interactive Learning Tutorial improves students' understanding quantum computing: A Quantum Interactive Learning Tutorial (QuILT) designed to improve students' grasp of foundational quantum computing principles targets key quantum mechanical concepts, differences between classical and quantum computers, properties of single- and multiqubit systems, and basic single-qubit quantum gates. It uses guided inquiry-based sequences and addresses common student difficulties identified through cognitive task analysis. The QuILT was implemented in two university courses, showing significant improvement in student performance on quantum computing concepts after engagement with the tutorial. The results highlight the effectiveness of the tutorial in establishing a deeper understanding of quantum computing, which is important now more than ever in preparing students for careers in QIS.

☕️FRESHLY BREWED RESEARCH

MEASURING THE LOSCHMIDT AMPLITUDE FOR FINITE-ENERGY PROPERTIES OF THE FERMI-HUBBARD MODEL ON AN ION-TRAP COMPUTER

QUICK BYTE: Quantinuum's 32-qubit digital computer and a time-series algorithm were used for computing the Loschmidt amplitude in the Fermi-Hubbard model. Using the time-series algorithm can determine the finite-energy properties of the model using a quantum computer with high gate fidelity, low state preparation and measurement error, and all-to-all connectivity. While classical simulations showed minimal impact of residual errors on predictions, scaling to classically intractable problems remains challenging due to significant resource requirements, necessitating technological advancements for practical implementation on NISQ devices.

PRE-REQs:

• The Fermi-Hubbard model is a theoretical model used in condensed matter physics to describe the behavior of interacting fermions on a lattice. It is especially useful in understanding phenomena in strongly correlated electron systems, such as high-temperature superconductivity, magnetism, and metal-insulator transitions.

• A time-series algorithm analyzes data points collected or recorded at specific time intervals to identify patterns and trends over time. These are specifically useful for forecasting, anomaly detection, trend analysis, and seasonality detection.

SIGNIFICANCE: Calculating equilibrium properties in condensed matter physics provides insight into the basic behavior of materials at rest, which serves as a starting point for understanding more complex phenomena, such as thermodynamic stability and phase transitions. The Fermi-Hubbard model is of particular interest in condensed matter physics because it describes fermion interactions on a lattice, capturing the essential physics of strongly correlated electron systems. Many scientists believe that understanding this model will lead to understanding high-temperature superconductivity, which has potential benefits for quantum technology, energy-efficient power generation, medical imaging, and more.

While analog quantum simulators have made progress in studying the equilibrium properties of fermions, they face challenges in reaching sufficiently low energies. Digital quantum computers have shown promise for high-dimensional fermionic problems, especially in ground-state studies. However, there have been limited demonstrations of scalable finite-energy or finite-temperature quantum algorithms.

This research investigates the use of a time-series algorithm's quantum subroutine to compute the Loschmidt amplitude for the Fermi-Hubbard model on a 16-site ladder geometry (32 spin orbitals). Using Quantinuum's 32-qubit digital computer, the study analyzes noise effects, implements error-mitigation techniques, and assesses resource requirements for scaling. The researchers found that time-series algorithms can determine the Fermi-Hubbard model's physical properties at finite energies using a quantum computer with specific characteristics: 0.998 two-qubit gate fidelity, low state preparation and measurement error, and all-to-all connectivity. Classical simulations showed that residual errors had minimal impact on finite-energy property predictions at the current scale.

However, scaling to classically intractable problems remains challenging due to large shot overhead, error mitigation requirements, and Monte Carlo simulation costs. As system size increases, so does the Trotter error. While the algorithm's scaling would be favorable on a fault-tolerant quantum computer, noise in NISQ devices causes the signal strength to decrease exponentially with the number of gates. Advancements in hardware, Monte Carlo sampling efficiency, and time-evolution algorithms would contribute to making this approach more practical.

RESULTS: 

• Time-series algorithms can determine the physical properties of the Fermi-Hubbard model at finite energies using a quantum computer with high two-qubit gate fidelity (0.998), low state preparation and measurement error, and all-to-all connectivity

• Classical simulations show that remaining errors have a low impact on the final prediction of finite-energy properties

• Scaling to classically intractable problems remains expensive due to large shot overhead, error mitigation requirements, and Monte Carlo simulation costs

HONORABLE RESEARCH MENTIONS:

An adaptive learning algorithm is introduced to improve the precision vector encoding in quantum linear regression, solved via quantum annealing. The approach allows each regression coefficient to be expressed with tailored precision, improving solution quality. The algorithm was tested on large synthetic datasets using D-Wave's quantum annealer, showing improved accuracy compared to fixed precision methods. Despite the challenges of limited qubit connectivity and precision, the adaptive method consistently outperformed traditional approaches in solution quality. —> link to Adaptive Learning for Quantum Linear Regression

A method to improve the precision and accuracy of quantum process tomography mitigates errors from state preparation and measurement, readout, and shot noise. Tested through simulations and experiments on IBM Quantum systems, this method demonstrates superior accuracy and precision compared to standard QPT and other benchmarking methods.. —> link to Multipass quantum process tomography

A practical approach to quantum matrix multiplication uses optimized quantum adders and multipliers based on the quantum Fourier transform. The authors demonstrate significant gate reductions compared to classical methods. They extend this to a quantum version of the Strassen algorithm, achieving further optimization. Comparative experiments highlight the acceleration and resource efficiency of their methods, suggesting substantial potential for quantum machine learning applications. —> link to Matrix Multiplication on Quantum Computer

UNTIL TOMORROW.