Welcome to the Quantum Realm. 

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

Just look at all those practical applications. A sight for tired eyes!

🌊⚛️ ⚡️ 

🗓️ THIS WEEK

Wednesday, June 5 - Friday, June 14 | IBM Quantum Challenge 2024 — Register here!

Saturday, June 8 | Towards Practical Quantum Computing: Addressing Crosstalk and Circuit Optim w/ Washington DC Quantum Computing Meetup

Sunday, June 9 | QTM-X Weekly Quantum Education Session 3/10: Entanglement

📰 NEWS QUICK BYTES

💥 IBM and Pasqal are linking up: IBM and Pasqal are partnering to develop quantum-centric supercomputing, integrating quantum computing with advanced classical systems. Their goal is to create a software integration architecture to revolutionize high-performance computing in chemistry and materials science.

📆 2024: the year of (financial) quantum computing: Quantum computing is set to transform finance in 2024, integrating with blockchain and AI to revolutionize portfolio optimization, risk management, and cryptographic security. Financial institutions must prepare to avoid cybersecurity risks as this new era of efficiency and innovation unfolds.

🌌 Photon vortices not only sound cool, they could be qubits: Researchers at the Weizmann Institute discovered new photon vortices in dense gas clouds, which have significant implications for quantum computing. These vortices could serve as qubits and enhance quantum data processing.

🌎️ 2024's global quantum trailblazers: The World Economic Forum announced its 2024 cohort of 100 Technology Pioneers, including breakthroughs in nuclear fusion, biotech, and quantum computing. Notable mentions in quantum computing include AlgorithmIQ, PlanQC, and Qubit Pharmaceuticals.

🏆️ IonQ is Maryland's tech darling of 2024: IonQ has been named Technology Company of the Year by the Maryland Tech Council for 2024, recognizing its advancements in quantum computing and its new manufacturing facility in Seattle.

☎️ Quebec’s quantum communication boost: Toshiba and Numana are collaborating to enhance the Kirq quantum communication testbed in Quebec using QKD technology. This partnership will expand Kirq’s infrastructure, with new hubs in Montreal and Quebec City by early 2025.

🌉 ParTec is bridging the quantum/hpc gap: ParTec’s QBridge software, developed with Quantum Machines, integrates quantum computers into existing HPC setups, supporting both physical and emulated quantum systems. This "qbit agnostic" approach allows for hybrid classical-quantum systems and ultimately makes quantum computing more accessible and practical for various applications.

🌐 D-Wave & Aramco continue subsurface imaging innovation: partnership D-Wave Quantum Inc. has extended its agreement with Aramco to use quantum technologies for managing geophysical optimization problems, specifically in subsurface imaging. Over the past two years, Aramco's Research Center in Delft has used D-Wave's quantum systems to create subsurface maps from seismic data and shown significant performance gains over classical computers. The collaboration’s goal is to process even larger datasets using D-Wave’s Advantage2 system in 2024.

☕️ FRESHLY BREWED RESEARCH

Environmental impact assessment of ocean energy converters using quantum machine learning: Quantum machine learning (specifically QSVM) is used to assess the environmental impacts of ocean energy converters as compared to classical SVM. The study highlights the potential of QML to efficiently handle complex and large datasets and as a central part of a method for more accurate environmental impact predictions and sustainable design of marine energy technologies. Breakdown here.

Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations: An algorithm combining quantum computing quantum Monte Carlo (QC-QMC) with a hybrid tensor network is used for more accurate electronic structure calculations on current quantum devices. Breakdown here.

A Quantum Neural Network-Based Approach to Power Quality Disturbances Detection and Recognition: An improved quantum qeural network model is introduced for detecting and recognizing power quality disturbances in smart grids. Breakdown here.

Quantum Computing in Intelligent Transportation Systems: A Survey: This survey reviews current research efforts, challenges, and future directions in applying quantum computing to areas such as traffic optimization, logistics, routing, and autonomous vehicles.

A KPI framework to standardize the measurement of a country’s progress in bringing quantum computing into application: The Quantum Technology and Application Consortium proposes a framework with 24 key performance indicators to assess and compare the implementation and progress of quantum computing across countries. The goal is to provide transparency, reproducibility, and comparability so countries may identify strengths and weaknesses to drive improvements in their quantum computing ecosystems.

Simulation of open quantum systems on universal quantum computers: A scalable method to simulate open quantum systems using quantum computers is simulated by defining an adjoint density matrix that approximates the dynamics of dissipative systems. This approach eliminates the need for auxiliary qubits, allows for efficient long-time simulations, and has been demonstrated on dissipative quantum XY and disordered Heisenberg models to show its potential for studying many-body dynamics.

UNTIL TOMORROW.

BREAKDOWN

Environmental impact assessment of ocean energy converters using quantum machine learning

🔍️ SIGNIFICANCE: 

• The shift from fossil fuels to renewable energy sources is critical due to the environmental damage caused by fossil fuel consumption and the global push towards reducing greenhouse gas emissions. Ocean energy, especially from waves and tides, holds significant potential for renewable energy production. However, the deployment of large-scale ocean energy converters can have adverse environmental impacts on marine ecosystems. The study highlights the efficiency of QML over classical machine learning methods in handling the complexity as well as the volume data associated with environmental assessments of OECs.

🧪 METHODOLOGY: 

• Data from various sources, including NOAA and OES-Environmental 2020 State of the Science Report, was collected. This data included parameters such as temperature, water salinity, oxygen concentration, and physical characteristics of the converters.

• Initially, the data was clustered using the K-means algorithm, an unsupervised learning method. The K-means algorithm categorized the data into three clusters based on their similarities.

• The clustered data was then used as input for the support vector machine. The SVM model was trained to classify the environmental impacts of OECs into three categories: low, medium, and high impact. The quantum support vector machine was then implemented to further analyze the data.

📊 OUTCOMES & OUTLOOK: 

• The QSVM demonstrated a significant improvement in accuracy compared to the classical SVM. The QSVM achieved an accuracy of 98%, while the classical SVM achieved 87.5%.

• The silhouette score, used to validate the clustering quality, confirmed that three clusters were the optimal choice for the dataset. The highest silhouette score was 0.465 for three clusters, indicating appropriate data placement within the clusters.

• The sensitivity analysis identified depth and the specific range of horizontal movement of aquatic animals as critical factors impacting the environmental assessment. This was consistent with real data, highlighting the importance of these features in predicting the impact of OECs on marine life.

• The research shows that QML can provide more accurate predictions of the environmental impacts of new OECs. This can help in designing converters with better capabilities and lower environmental impacts, thus contributing to more sustainable marine energy production.

BREAKDOWN

Quantum computing quantum Monte Carlo with hybrid tensor network for electronic structure calculations

🔍 SIGNIFICANCE: 

• Traditional quantum Monte Carlo methods are limited by the computational resources required to achieve high accuracy in electronic structure calculations. Quantum computing quantum Monte Carlo improves upon these limitations by integrating quantum circuits to prepare trial states which improves the accuracy of ground state calculations. The differentiating contribution of this research is the combination of QC-QMC with a hybrid tensor network that extends the applicability of QC-QMC beyond the size of a single quantum device. This approach allows for larger trial wave functions than what a single device can handle, making it possible to perform highly accurate electronic structure calculations on large systems using current quantum devices.

🧪 METHODOLOGY: 

• The algorithm decomposes the original system's wave function into smaller tensors that can be managed by quantum or classical computation, significantly reducing the effective width and depth of the quantum circuits.

• A pseudo-Hadamard test is developed to efficiently calculate overlaps between a trial wave function and an orthonormal basis state.

• The algorithm is evaluated using various models such as the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole. Full configuration interaction QMC is used to benchmark the performance of the proposed algorithm.

• The HTN+QMC algorithm involves first optimizing the trial wave function using HTN, and then executing QMC using the optimized trial wave function. This two-step process ensures that the trial wave function is prepared with high fidelity and leads to more accurate QMC results.

📊 OUTCOMES & OUTLOOK: 

• The HTN+QMC algorithm achieves energy accuracy several orders of magnitude higher than traditional QMC methods. The hybrid tensor network version of QMC provides comparable energy accuracy to QC-QMC when the system is appropriately decomposed.

• The algorithm's performance was benchmarked on several models, each time demonstrating improvements in accuracy..

• The pseudo-Hadamard test technique demonstrated noise resilience in real device experiments. The results obtained from the real quantum device (ibmq_kolkata) were almost as accurate as those from statevector simulations.

• The results suggest that the HTN+QMC approach can extend QC-QMC to large-scale systems beyond the current quantum device size while maintaining high accuracy. This opens up new possibilities for electronic structure calculations and their related applications such as battery technology, catalysis, and photochemical materials.

BREAKDOWN

A Quantum Neural Network-Based Approach to Power Quality Disturbances Detection and Recognition

🔍 SIGNIFICANCE: 

• Smart grids increasingly integrate nonlinear loads and renewable energy sources, leading to complex grid signals and significant power quality issues. Traditional methods for power quality disturbance detection and recognition have limitations in processing efficiency and accuracy. The proposed QNN model leans on the parallel processing capabilities of quantum computing to offer superior detection and classification accuracy which reduces both runtime and space complexities compared to classical methods. This work is the first to apply QNN to PQD detection and recognition.

🧪 METHODOLOGY: 

• A quantum circuit is constructed with data and ancilla qubits, where classical data is transformed into quantum data via an encoding layer. This encoded data is processed through a variational layer composed of parametric quantum gates which facilitate qubit information transformation.

• The quantum circuit's final state is measured using ancilla qubits to classify disturbances based on the expected value. Extensive experiments we completed to validate the model’s resilience against noise and performance as compared to classical methods.

• Notable modifications include the use of S-transform for feature extraction and the application of the parameter shift rule for gradient computation to improve model's training efficiency and accuracy.

📊 OUTCOMES & OUTLOOK: 

• The improved QNN model significantly outperforms classical methods in PQD detection and recognition. The model achieves a detection accuracy of 99.75%, single disturbance classification accuracy of 97.85%, and mixed disturbance classification accuracy of 95.5%.

• Comparative experiments reveal that the QNN model requires fewer parameters while delivering higher accuracy as compared to traditional models like SVM, PNN, FFML, CNN, and LSTM.

• By addressing PQ issues more efficiently, this model can contribute to the advancement of smart grid technologies.