Monday, October 21st, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Near-optimal-time quantum algorithms were developed for approximate pattern matching, specifically for pattern matching with mismatches and edits.

• Q4RPD uses quantum annealers to solve a real-world package delivery routing problem that incorporates complex constraints.

• A quantum-classical hybrid convolutional neural network model for breast cancer diagnosis demonstrates superior classification accuracy across three datasets.

• Plus, charge-preserving VQD algorithm, the teleportation of a Toffoli gate, the latest QPU-integration with a supercomputer, and more.

And even more research, news, & events within quantum.

🧬 Quantum Algorithms Improve Approximate Pattern Matching for Potentially Faster Genomic and Text Searches

QUICK BYTE: Researchers from the Max Planck Institute for Informatics, ETH Zurich, and others developed near-optimal-time quantum algorithms for approximate pattern matching, specifically for pattern matching with mismatches and edits.

DETAILS

• Approximate pattern matching involves finding occurrences of a pattern within a text that differ slightly by mismatches (differences between characters) or edits (insertions and deletions). More formally, given a text T, a pattern P, and a threshold k, the task is to identify occurrences in T that differ from P by at most k mismatches (Hamming distance) or edits (edit distance, which includes insertions and deletions).

• The authors devised new methods that improve upon the efficiency of current solutions, presenting quantum algorithms for both pattern matching with mismatches and pattern matching with edits. Both algorithms achieve better time complexity compared to previous methods, especially in cases where the number of allowed differences k is small relative to the length of the text and pattern.

• A key technical contribution is a more efficient method for handling compressed representations of text and pattern strings. The new algorithm makes it faster to solve systems of substring equations, allowing for quicker and more efficient processing, even when dealing with large, compressed data.

• Overall, these algorithms provide quantum speedups over classical algorithms for pattern matching with mismatches and edits, particularly for large-scale text and pattern comparisons where only a few mismatches or edits are allowed—relevant in fields like genomics and text searching, where approximate matching is frequently used due to noise and imperfections in data.

🚚 Quantum Annealing for Optimized Last-Mile Delivery Routing with Complex Constraints

QUICK BYTE: Q4RPD, developed TECNALIA and Serikat-Consultoría y Servicios Tecnológicos scientists, uses quantum annealers to solve a real-world package delivery routing problem that incorporates complex constraints.

DETAILS

• A complex last-mile delivery routing problem defined by a Spanish logistics company, Ertransit, involved heterogeneous vehicle fleets, priority deliveries, and constraints based on package weight and dimensions. The objective was to calculate cost-efficient routes that satisfy all delivery demands within one working day while minimizing total distance and vehicle rental costs.

• The team proposed the Quantum for Real Package Delivery (Q4RPD) method, which combines classical computing for problem setup and workflow management with quantum annealing via D-Wave’s Leap Constrained Quadratic Model Hybrid Solver for route calculations to calculate sub-routes based on constraints and iterating until all deliveries are completed.

• Q4RPD solves a single routing problem at each iteration, using a binary node-based representation for deliveries and optimizing a cost function that minimizes total route distance while maximizing the number of deliveries per truck. The system ensures all constraints, such as truck capacities and delivery time windows, are met.

• The experimental results demonstrate Q4RPD’s ability to solve routing problems that closely resemble real-world logistics scenarios, handling up to 29 deliveries across multiple routes. The method performs comparably to classical solutions like Google OR-Tools, while potentially providing quantum speedups for scenarios with more complex constraints, such as priority deliveries.

🩺 Quantum CNN Improves Accuracy in Breast Cancer Diagnosis Across Multiple Datasets

QUICK BYTE: A quantum-classical hybrid convolutional neural network model for breast cancer diagnosis, developed at the Chengdu University of Technology, demonstrates superior classification accuracy across three datasets compared to traditional convolutional neural networks and logistic regression models.

DETAILS

• As stated by the World Health Organization, early breast cancer diagnosis is essential for recovery, but difficult to catch. Classical methods are often limited in handling the complex, large-scale datasets often required in medical diagnostics. A quantum hybrid convolutional neural network is developed that integrates quantum computing into traditional CNN architectures to improve diagnostic accuracy and efficiency.

• The QCCNN model incorporates a quantum convolutional layer into the classical CNN architecture. Quantum computing’s ability to process high-dimensional data and perform parallel operations is utilized to improve the feature extraction process. This hybrid approach is tested on three breast cancer datasets (GBSG, SEER, WDBC), where QCCNN outperforms CNN and logistic regression in terms of classification accuracy.

• Across all datasets, QCCNN achieved higher classification accuracy, with results showing up to 97.099% on the WDBC dataset, compared to CNN (92.932%) and logistic regression (91.354%). The study demonstrates the potential of quantum computing to improve generalization and processing efficiency for complex medical data.

LuxProvide has been selected to host MeluXina-Q, a new EuroHPC quantum computer that will integrate quantum capabilities with Luxembourg’s existing MeluXina supercomputer. Initially equipped with a 10-qubit quantum processing unit and scalable to 80 qubits, MeluXina-Q will leverage silicon manufacturing techniques to ensure secure and scalable quantum infrastructure within the EU. Co-funded by EuroHPC and the Luxembourg government, this project is part of a broader European strategy to build a federated quantum infrastructure, supporting advancements in cryptography, climate modeling, and material science.

Researchers from NVIDIA, Stony Brook University, and Brookhaven National Laboratory developed the charge-preserving VQD algorithm to efficiently compute excited states in quantum systems by leveraging conserved charges and symmetry to reduce system dimensionality. Tested on systems with up to 24 qubits using NVIDIA’s CUDA-Q platform and the NERSC Perlmutter system, CPVQD showed faster convergence and improved computational efficiency, making it applicable to quantum chemistry and nuclear physics.

Oxford Ionics is participating in a UK government-led quantum trade mission to the United States, organized by the Department of Business & Trade. David Allcock, Director of Science for North America, will represent the company on this five-day mission, which involves meetings with US government officials, research institutions, and quantum companies in Chicago and Colorado. Oxford Ionics, which recently opened an office in Boulder, CO, intends to expand its presence in North America and collaborate on advancing commercial quantum computing solutions.

Southern University of Science and Technology researchers have demonstrated the teleportation of a Toffoli gate, a multi-qubit quantum logic gate, across three spatially separated nodes in a photonic quantum network. The experiment, which used photons to encode quantum information and achieved a fidelity of at least 0.706, is relevant for distributed quantum computing as it reduces the complexity of remote quantum operations.

INOX Group and the Indian Institute of Science (IISc) have signed a Memorandum of Understanding to establish the INOX Quantum Materials Lab at IISc’s Centre for Nano Science and Engineering. The lab will focus on developing topological semiconductors for the pursuit of fault-tolerant quantum computing. The collaboration also intends to build an indigenous Molecular Beam Epitaxy unit to support making advanced quantum and semiconductor technologies more accessible and cost-effective in India, while encouraging research and talent development in quantum materials. INOX Group's involvement will support lab infrastructure, technology transfer, and industry benefits through this CSR initiative.

The Dubai Integrated Economic Zones Authority (DIEZ) has partnered with Builder.ai to enhance the AI and Quantum Cluster at Dubai Silicon Oasis, a special economic zone focused on innovation. This strategic partnership, signed at GITEX Global 2024, will establish a "Living Lab" to test, showcase, and scale AI and quantum technologies, as well as encourage collaboration through research, workshops, and educational initiatives. Builder.ai will be the ecosystem partner, offering custom software development services at preferential rates to companies in DSO.

Three scientists at Argonne National Laboratory were individual recipients of recent grants from the the Department of Energy to advance their quantum computing research. Paul Hovland, Jeffrey Larson, and Zain Saleem will lead projects focused on quantum algorithms and software development to demonstrate quantum computing’s utility for solving complex scientific problems. Hovland’s project is set on developing a modular software framework addressing quantum error issues, Larson’s research will advance hybrid quantum-classical algorithms, and Saleem’s work focuses on creating new algorithms for real-world applications.

LISTEN

On the post recent episode of the Post-Quantum World podcast, host Konstantinos Karagiannis is joined by Brian DeMarco and Harley Johnson of the University of Illinois Urbana-Champaign. They discuss the upcoming Quantum & Microelectronics Park, DARPA, and the future of fault-tolerant quantum computing.

WATCH

Scott Aaronson discusses the capabilities of quantum computing, the current state of AI, and the nature of mathematics beyond human constructs in a conversation moderated by Brian Greene: