Welcome to the Quantum Realm. 

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

📚️ 🌌 Quantum as the ultimate polymath — catalyst design, nuclear astrophysics, carbon nanotube-based tech, accelerated hair care products, and asteroid detection. Just another day, another slurry of potential.

🗓️ THIS WEEK

Sunday, June 23 | QTM-X Quantum Education Series 5 of 10: Quantum Hardware

📰 NEWS QUICK BYTES

🤖 Quantum Benchmarking Achievements: Zapata AI announced they will present findings from DARPA’s Quantum Benchmarking program, highlighting the economic value and resource requirements of transformative quantum computing applications. Collaborating with Rigetti, USC, HRL, and L3Harris, advancements include catalyst design, fluid dynamics, and material simulation.

🔬 Quantum Computing at Los Alamos: A new report from Los Alamos National Laboratory explores applications in quantum computing — including improving simulations of magnetic materials, high-temperature superconductors, and nuclear astrophysics. The report supports DARPA's Quantum Benchmarking program and emphasizes quantum computing's role in scientific and national security missions at the laboratory.

🎧 Quantum Error Correction Challenges: Today's quantum processors need error correction schemes using many physical qubits to create fault-tolerant logical qubits. This podcast, with Stephanie Simmons of Photonic Inc., discusses their development of silicon spin qubits and advancements in quantum entanglement, highlighting the technology's scalability and integration potential.

🧪 Carbon Nanotube Quantum Computers: C12 has raised €18 million to advance its unique carbon nanotube-based quantum computing technology. The company is focusing on creating scalable, low-error quantum computers and has developed a patented nano-assembly process, currently producing and testing chips with one or two qubits.

🧼 Unilever Prepares for Quantum: Unilever, in partnership with Microsoft, is integrating Azure Quantum Elements into its R&D to accelerate product development through AI and quantum computing. These advancements are expected to drastically reduce the time for computational simulations and ingredient formulation, potentially turning decades of lab work into days.

☕️ FRESHLY BREWED RESEARCH

Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection: An ensemble deep learning model combined with quantum machine learning classifiers is used to improve the accuracy and efficiency of Alzheimer's disease diagnosis using MRI images and achieves a significant accuracy of 99.89%. By using QML, this model is able to address the limitations of classical methods and provide a scalable solution for early Alzheimer's disease detection. Breakdown here.

Multi-reference Quantum Davidson Algorithm for Quantum Dynamics: The QDavidson algorithm is shown to improve quantum simulations by iteratively growing the Krylov subspace, which results in faster convergence with fewer iterations and shallower circuit depths. This method outperforms traditional approaches while maintaining high accuracy and efficiency even in the presence of Trotter errors, making it relevant for simulating complex quantum systems on near-term quantum devices. Breakdown here.

Competition of decoherence and quantum speed limits for quantum-gate fidelity in the Jaynes-Cummings model: The research examines the competition between decoherence and quantum speed limits within the Jaynes-Cummings model to optimize quantum gate fidelity. It finds that a balance between entanglement-induced errors and minimal computation time is crucial for achieving high fidelity. Additionally, the study demonstrates that using a single driven evolution for logical qubits is more energy-efficient than splitting the computation into subroutines.

Quantum-Inspired Clustering for Hazardous Asteroid Prediction in Quantum Machine Learning: The title says it all — this research explores the application of quantum-inspired clustering for predicting hazardous asteroids using QML. By using the enhanced quantum-inspired evolutionary fuzzy C-means algorithm, it improves the accuracy and efficiency of clustering hazardous asteroid datasets and outperforms other models like K-medoid, spectral clustering, and quantum K-means. The study demonstrates the potential of QML contribute to improved planetary defense strategies.

Mitigation of channel tampering attacks in continuous-variable quantum key distribution: Mitigating channel tampering attacks, particularly the channel amplification attack, in continuous-variable QKD can be done using a machine learning-based decision tree classifier to detect these attacks and a postselection strategy to mitigate their effects. The study demonstrates a significant improvement in the secret key rate under attack.

UNTIL TOMORROW.

BREAKDOWN

Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection

🔍️ SIGNIFICANCE: 

• Early detection of Alzheimer’s disease is important for improving prognosis and quality of life for patients. Traditional machine learning are being used in diagnosing Alzheimer’s disease, but they often face challenges such as limited data and computational inefficiencies. By integrating deep learning models with quantum machine learning classifiers, we can address the limitations of classical methods and offer a potentially more scalable solution for early detection.

🧪 METHODOLOGY: 

• Researchers developed a deep learning model combined with quantum machine learning classifiers to classify different stages of Alzheimer's disease. MRI images from the Alzheimer's Disease Neuroimaging Initiative datasets ADNI I and ADNI II were used.

• The MRI images were preprocessed to eliminate irrelevant parts, resized to 128x128 pixels, and augmented to address class imbalance and enhance generalization.

• The ensemble model combined features extracted from customized versions of VGG16 and ResNet50 deep learning models. These features were then flattened and concatenated.

• The extracted features were fed into a quantum support vector machine for classification into non-demented, mild demented, moderate demented, and very mild demented categories.

• The model's performance was evaluated using metrics such as accuracy, F1-score, precision, recall, and the area under the curve. The ensemble model's performance was compared with individual models and state-of-the-art methods.

📊 OUTCOMES & OUTLOOK: 

• The ensemble model with QSVM achieved an accuracy of 99.89%, which outperforms individual models like VGGNet and ResNet, as well as other state-of-the-art methods.

• The proposed model also demonstrated superior performance across metrics such as precision, recall, F1-score, and AUC.

• The integration of QML reduced computational inefficiencies and ultimately made the model more suitable for practical applications in healthcare settings.

BREAKDOWN

Multi-reference Quantum Davidson Algorithm for Quantum Dynamics

🔍️ SIGNIFICANCE: 

• Two quantum Krylov subspace methods derived from the quantum Davidson algorithm are evaluated for simulating quantum systems. They address the current challenge in simulating medium to large quantum systems due to the limited coherence times and gate infidelities of current quantum devices. More traditional methods, like the Trotter-Suzuki decomposition, suffer from cumulative errors in rapidly changing dynamics.

🧪 METHODOLOGY: 

• The QDavidson algorithm was used to iteratively grow the Krylov subspace, ensuring it remains close to the exact eigenspace.

• It was benchmarked by simulating the time evolution of the Heisenberg model on 8 qubits, varying the number of iterations to evaluate the accuracy and fidelity of the fast-forwarded state.

• Additionally, the study compared the scaling of the QDavidson algorithm with multi-reference Krylov methods, demonstrating its sub-exponential scaling behavior and robustness against Trotter errors.

• The methodology involved constructing subspace matrices, solving the generalized eigenvalue problem, and using classical post-processing to determine time-evolved observables.

📊 OUTCOMES & OUTLOOK: 

• Ultimately, the QDavidson algorithm shows faster convergence with fewer iterations and shallower circuit depths as compared to traditional methods. It significantly outperforms the Trotter approximation in terms of state overlap and fidelity with the exact time-evolved state.

• The QDavidson algorithm also shows sub-exponential scaling of the Krylov dimension which makes it suitable for simulating larger quantum systems. It maintains high efficiency and accuracy even in the presence of Trotter errors.

• Integrating the QDavidson algorithm with the multi-reference Krylov method improves the efficacy and precision of the fast-forwarding process, balancing the Krylov subspace dimension and the number of iterations required.