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Cierra

Today’s issue includes:

• QCBERT is a self-supervised pre-trained neural network designed to improve quantum natural language processing 

• Quantum echo-state networks predict chaotic systems and handle complex time-series prediction tasks more efficiently.

• A quantum computing-based method for structural reliability analysis may reduce computational costs for complex civil engineering problems, like structural failure.

QUANTUM APPLICATION HEADLINES

🤖 QCBERT: Natural Language Processing with Enhanced Scalability and Efficiency

APPLICATION: Researchers from the University of Copenhagen and Halmstad University present QCBERT, a self-supervised pre-trained neural network designed to improve quantum natural language processing.

SIGNIFICANCE: Current quantum natural language processing models face challenges due to the structure of quantum gates, which operate in a highly linear fashion. This linearity limits the ability of quantum systems to capture complex relationships and patterns in language, which are essential for tasks like sentiment analysis or text classification. Additionally, quantum systems struggle with scalability, meaning they cannot easily handle larger or more complex datasets. QCBERT addresses these issues by applying self-supervised learning. As a result, QCBERT can handle real-world tasks like detecting hate speech, understanding sentiment in text, or classifying documents.

HOW: A classical BERT encoder processes text into a fixed-dimensional representation, which is then converted into a quantum-compatible state via feature extraction using single-qubit rotations and entangling gates. These quantum circuits transform textual features into quantum encodings that can be fine-tuned for specific tasks such as sentiment analysis. This approach not only lends itself to performance but also makes the system adaptable to downstream tasks requiring deeper semantic understanding.

BY THE NUMBERS:

• 5 qubits — Used in the quantum circuits for QCBERT’s architecture.

• 69.8 GLUE score — Outperformed non-pre-trained quantum models by ~7 points. The GLUE benchmark measures a model's ability to handle diverse natural language tasks.

• 2 data sources — Pre-trained on large-scale datasets, BOOKCORPUS and English WIKIPEDIA, for linguistic knowledge acquisition.

• 200 epochs — Total pre-training iterations to optimize model performance on NLP tasks.

🔊 Quantum Echo-State Networks as Scalable Solutions for Predicting Chaotic Systems and Complex Time-Series Data

APPLICATION: Researchers from the University of South Carolina, Naval Surface Warfare Center, and RTX BBN Technologies developed quantum echo-state networks, a framework for predicting chaotic systems and handling complex time-series prediction tasks more efficiently than classical methods.

SIGNIFICANCE: Classical time-series prediction methods, such as echo-state networks, rely on a large "reservoir" of artificial neurons to capture the patterns and relationships in data over time. These reservoirs require significant computational resources, especially when dealing with complex and chaotic systems where trajectories can change unpredictably. Quantum echo-state networks may address this challenge, by, allowing information to be shared between qubits in a way that classical systems cannot replicate. This interconnectedness enables QESNs to create "richer feature spaces," meaning they can represent and process data in more nuanced and complex ways without requiring the same massive computational infrastructure. This innovation is significant because it makes QESNs more scalable and efficient, opening doors for practical applications in fields where chaotic systems must be modeled and predicted. Examples include weather forecasting, where small changes can lead to vastly different outcomes (the "butterfly effect"), financial modeling for complex market behaviors, and the analysis of dynamic systems in engineering or physics.

HOW: QESNs adapt the classical ESN model to quantum systems by implementing a sparsely connected reservoir of quantum states. A "context window" embeds input data into the quantum system through single-qubit rotations and two-qubit entangling gates, introducing nonlinear mappings crucial for complex predictions. The system measures only a subset of qubits, resets them, and reuses them, enabling continuous operation without stopping or reinitialization. This process generates a quantum-derived probability distribution, which is used in a classical regression model for predictive analysis.

BY THE NUMBERS

• 16 qubits — The maximum tested size of the quantum system; highlights the system’s ability to scale to larger quantum networks, which is relevant for addressing increasingly complex predictive tasks.

• ~30% RMSE reduction — Root mean square error measures the difference between predicted and actual values, with lower values indicating better accuracy. QESNs achieved up to 30% lower RMSE compared to classical models.

• 60,000 shots — Running 60,000 shots per prediction provides a statistically thorough dataset, minimizing errors due to noise and ensuring the reliability of the QESN's predictions.

🏗️ Quantum Computing May Improve Structural Reliability Analysis with Efficient Failure Probability Estimation

APPLICATION: A researcher from the Guangdong University of Technology proposes a quantum computing-based method for structural reliability analysis, using quantum amplitude estimation. This reduces computational costs and improves efficiency, particularly for complex civil engineering problems like structural failure probability assessments.

SIGNIFICANCE: Structural reliability analysis evaluates the likelihood of structural failure under uncertain conditions, a critical task in civil engineering. Traditional methods like Monte Carlo simulations require large-scale sampling and significant computational resources. The QAE method uses quantum properties, such as superposition and entanglement, to analyze all possible outcomes simultaneously, enabling faster and more efficient calculations. This innovation could be transformative for applications requiring high computational efficiency, such as designing safer buildings, bridges, and other critical infrastructure under uncertain conditions.

HOW: The QAE-based method reformulates structural reliability problems for quantum computing. It encodes uncertain parameters as qubits in a quantum circuit, creating a superposition of all possible scenarios. The Grover's algorithm is then used to isolate and amplify the failure probability embedded in the quantum state, enabling precise calculations with fewer samples. This method demonstrates a speedup over classical Monte Carlo simulations, reducing the computational effort required for reliability assessments.

BY THE NUMBERS: 

• Standard error slope — The QAE method demonstrated a steeper reduction in standard error (− 1.069) compared to Monte Carlo (− 0.4946), confirming its efficiency in converging to accurate results.

• 16,000 Samples vs. Millions — The study claims that QAE requires far fewer samples—on the order of thousands—compared to the millions typically needed for Monte Carlo methods. This efficiency is especially relevant for real-time or computationally constrained applications.

RESEARCH HIGHLIGHTS

💡 A team from Quandela and CNRS explores efficient resource-state generation for fusion-based quantum computing using hybrid spin-photon devices. Researchers evaluated three architectures for creating a (2,2)-Shor-encoded photonic resource state, leveraging deterministic single-photon sources, caterpillar graph states, and repeat-until-success modules. Results showed that hybrid approaches could significantly reduce resource overhead, enhance scalability, and improve loss tolerance.

💻️ Equal1 Laboratories investigates the use of a commercial 22-nm Fully Depleted Silicon-On-Insulator (FD-SOI) CMOS process to create and control quantum dots for scalable qubit architectures. By demonstrating precise electrical control over the formation and coupling of quantum dots, as well as charge sensing capabilities using single electron box sensors, the study highlights the potential of standard CMOS processes for integrating quantum computing components.

🧪 Infleqtion, NVIDIA, and others demonstrate fault-tolerant logical qubits on a neutral atom quantum computer using the [[4,2,2]] error detection code. The study achievederror reductions across various benchmarks, including Bell states and material science applications like the Anderson Impurity Model.

NEWS QUICK BYTES

🔥 Accenture Federal Services and Q-CTRL collaborated to enhance anomaly detection in network security using a hybrid quantum-classical approach, integrating Q-CTRL's Fire Opal software with IonQ hardware via Amazon Braket. The solution addressed the Max-Cut optimization problem, achieving a 3X improvement in success probability over classical methods during testing, demonstrating the potential of quantum optimization for cybersecurity.

⚛️ A consortium led by ParityQC, DESY, eleQtron, and DLR QCI is developing a full-stack quantum machine learning solution to process image data from CERN's Large Hadron Collider (LHC), funded by Hamburg’s Quantum Computing Funding Initiative. This project intends to address the computational challenges posed by massive data from particle physics experiments using quantum hardware and AI-enhanced algorithms, significantly accelerating data analysis.

💰️ Quantum Computing Inc. has secured $50 million in gross proceeds through a registered direct offering and a concurrent private placement, selling a combined total of 10 million shares at $5.00 per share. The funds will support the development and expansion of its U.S.-based thin-film lithium niobate (TFLN) Photonic Chip Foundry in Tempe, Arizona, set to be completed by Q1 2025, alongside ongoing R&D and scaling production of its quantum optimization and high-performance computing products.

✨ Classiq Technologies, Deloitte Tohmatsu Group, and Mitsubishi Chemical collaborated to explore quantum computing for developing advanced organic electroluminescent materials. The project achieved up to 97% compression in quantum circuit length for Quantum Phase Estimation and 54% for Quantum Approximate Optimization Algorithms, reducing computational noise and improving calculation accuracy.

🤝 Quobly and STMicroelectronics have announced a collaboration to scale quantum processor units using ST’s 28nm FD-SOI semiconductor process, targeting a 100-qubit machine with scalability to over 100,000 physical qubits. The partnership intends to make large-scale quantum computing feasible and cost-effective, with initial products expected by 2027 and a goal of surpassing 1 million qubits by 2031.

QUANTUM MEDIA

WATCH

PsiQuantum updates on its approach and progress toward scalable, practical quantum computing at the 2024 MIT EmTech conference:

THAT’S A WRAP.