Thursday, October 3rd, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Researchers propose a hybrid quantum-classical framework integrating multi-omics data to classify lung cancer subtypes.

• Scientists present the automatic quantum circuit encoding (AQCE) algorithm to encode arbitrary quantum states onto quantum circuits.

• Researchers developed a new mathematical algorithm that enables quantum simulators to scale up more effectively.

• Plus remote ion-ion entanglement, Andreev qubits, optically connected, multi-node distributed quantum computing systems, and more .

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

🎉 RIKEN’s AQCE Optimizes Quantum Circuit Encoding Without Parameter Adjustment

QUICK BYTE: Scientists from RIKEN present the automatic quantum circuit encoding (AQCE) algorithm, a quantum-classical hybrid approach designed to encode arbitrary quantum states onto quantum circuits without relying on parameter optimization.

DETAILS

• The AQCE algorithm constructs a quantum circuit that approximates a given quantum state using a sequence of optimal two-qubit unitary operators determined via singular value decomposition of the fidelity tensor, eliminating the need for parameterized quantum gate optimization. This approach allows for the efficient encoding of quantum states with controlled accuracy.

• The AQCE algorithm is demonstrated through noiseless numerical simulations on quantum many-body systems, such as the spin-1/2 antiferromagnetic Heisenberg model and the XY model. Additionally, it extends to classical data encoded as quantum states, suggesting practical use cases in quantum machine learning, particularly in preparing quantum states from classical data for input in quantum circuits.

• The study compares AQCE with predefined quantum circuit structures like Trotter-like and MERA-like circuits, showing that AQCE provides a more flexible and often more efficient representation of quantum states without assuming a specific ansatz.

• The AQCE algorithm is validated on a real quantum device provided by IBM Quantum, where it successfully represents quantum states with reasonable accuracy, demonstrating its applicability to near-term quantum devices and potential use in quantum applications beyond simulations.

🩺 Hybrid Quantum-Classical Framework Outperforms Traditional Methods in Lung Cancer Subtype Classification Using Multi-Omics Data

QUICK BYTE: Researchers form Purdue University and North Carolina State University propose a hybrid quantum-classical framework integrating multi-omics data to classify lung cancer subtypes, demonstrating that quantum neural networks outperform traditional machine learning models in identifying key molecular features.

DETAILS

• The study introduces the Multi-Omic Quantum Machine Learning Lung Subtype Classification (MQML-LungSC) framework, which integrates DNA methylation, RNA-seq, and miRNA-seq data from The Cancer Genome Atlas to distinguish between LUAD and LUSC subtypes. The researchers used QNNs to encode features using amplitude encoding, with models evaluated across three different feature sets (32, 64, and 256 encoded features).

• The framework applied classical feature selection methods, including mutual information, chi-square, PCA, and random forest, followed by AUC-ROC analysis and hierarchical clustering. The integration of these omic features was crucial in enhancing the accuracy of subtype classification, revealing key biomarkers for lung cancer subtypes through feature engineering and statistical analysis (e.g., t-tests and p-value thresholds).

• The QNN models showed higher classification accuracy compared to classical machine learning methods, achieving testing accuracies of 0.90 for the model with 256 features, 0.86 for the model with 64 features, and 0.85 for the model with 32 features. The QNN model with 256 features was the most effective, showing high precision (0.92), recall (0.94), and F1-score (0.92) for LUAD subtype classification.

• This framework highlights the potential of quantum machine learning in biomedical research to handle complex, high-dimensional multi-omics data for cancer diagnosis. The MQML-LungSC framework provides a foundation for future applications in personalized medicine and biomarker discovery, particularly in the early diagnosis and classification of cancer subtypes.

⚛️ Quantum Algorithm Increases Drug Simulation Efficiency, Promising Faster Pharmaceutical Advances

QUICK BYTE: Researchers at the University of Copenhagen have developed a new mathematical algorithm that enables quantum simulators to scale up more effectively, allowing for more precise simulations of complex quantum systems such as drug molecules.

DETAILS

• Current quantum computers can only simulate a few atoms due to hardware limitations, making it difficult to model the large molecules found in drug development. This bottleneck has been a significant obstacle for pharmaceutical applications of quantum computing.

• The University of Copenhagen team introduced a mathematical algorithm that optimizes the software of quantum simulators, allowing for more computing power to be extracted from existing hardware. This controlled use of noise helps simulations run more efficiently and could be applied to any type of quantum hardware, including systems based on atoms, ions, or superconducting qubits.

• If quantum simulators can accurately predict the behavior of drug molecules in the human body before lab testing, the process of drug development could be dramatically accelerated. This could reduce the time and cost of bringing new medications to market from years and billions of euros to months.

• he researchers will now test their algorithm on quantum hardware to validate its effectiveness. If successful, this advancement could have a transformative effect on industries such as pharmaceuticals by enhancing drug simulation capabilities, potentially speeding up clinical trials and improving drug efficacy.

IonQ has demonstrated remote ion-ion entanglement, a notable achievement in its development of photonic interconnects. The team achieved this by entangling two trapped ion qubits from separate trap wells using entangled photons, establishing a point-to-point quantum communication link. This advances IonQ's goal of integrating photonic interconnects into commercially available quantum computers, enhancing scalability and performance.

Physicists from the University of Basel have successfully coupled two Andreev qubits over a macroscopic distance of 6 millimeters using microwave photons generated in a superconducting resonator. This coupling, achieved between qubits localized in semiconducting nanowires, allows the qubits to share a quantum state. The experiment demonstrates that Andreev qubits, which are formed through Andreev reflection at metal-superconductor interfaces, are resilient and scalable, making them suitable for use in solid-state quantum systems.

U.S. Senators Maggie Hassan and Marsha Blackburn are pressing the Pentagon for details on its quantum sensing initiatives, emphasizing the need for a strategic plan to maintain the U.S.'s technological advantage over China. In a letter to Defense Secretary Lloyd Austin, the senators highlighted concerns that China may be leading in quantum communications and sensing, areas with near-term applications like jam-resistant navigation systems. The senators asked the Pentagon to outline its coordination efforts, emerging threats addressed by quantum sensing, and what resources it requires from Congress to accelerate these technologies.

Nu Quantum and the National Quantum Computing Centre (NQCC) have announced Project IDRA, the first phase of a 4-year initiative to build a pioneering optically connected, multi-node distributed quantum computing system. This project, based at NQCC's Harwell facility, will seek to scale quantum computers by networking multiple quantum processing units, bringing them together to act as a larger, more powerful system. Nu Quantum will develop key components like high-efficiency qubit-photon interfaces and quantum networking units, with the goal of achieving higher entanglement rates and fidelities than current academic benchmarks.

Norwegian ministers have committed NOK 70 million (approximately $66 million) annually in the state budget to advance quantum technology research, focusing on its potential benefits and risks. The initiative, launched at OsloMet's Quantum Hub, intends to strengthen national and Nordic efforts in quantum technology, addressing challenges like climate change, medicine development, and encryption security. Minister of Research and Higher Education Oddmund Hoel emphasized the need for this investment, while Minister of Defense Bjørn Arild Gram highlighted the importance of securing communication systems against quantum-based encryption threats. OsloMet, a leader in quantum data processing, will contribute through research and education.

D-Wave has introduced service-level agreements (SLAs) for customers using its Leap™ quantum cloud service, which supports production-level quantum and hybrid applications. These SLAs guarantee high availability, reliability, and scalability, with Leap consistently exceeding 99.9% uptime over the past two years. D-Wave's Leap service, which offers real-time access to its annealing quantum computers and hybrid solvers, has processed nearly 200 million jobs since 2018, with recent enhancements improving processing speeds by 30%. The introduction of SLAs is designed to support businesses as they integrate quantum computing into their IT infrastructure.

NordVPN has released its first app supporting post-quantum encryption, currently available for Linux and compliant with NIST standards, to protect against future quantum computing threats. The company aims to implement post-quantum algorithms across all platforms by the first quarter of 2025. NordVPN's CTO, Marijus Briedis, highlighted the growing risk of "harvest now, decrypt later" attacks, where cybercriminals gather encrypted data to decrypt once quantum technology matures. This proactive move is part of the VPN industry's efforts to safeguard privacy and security in anticipation of quantum computing advancements.

LISTEN

In the most recent episode of the TED Tech podcast, Hartmut Neven, founder and lead of Google Quantum AI discusses quantum computers and the roadmap for building the ultimate quantum computer and explores its potential to solve major challenges in medicine, sustainable energy, AI, neuroscience, and beyond.

ENJOY

Quantum computing has come a long way, evolving from theoretical concepts to the developments that are shaping the field today and onward. From Paul Benioff’s early ideas to Google’s claim of quantum supremacy, this article reviews the key milestones that have propelled the field forward. Brush up on quantum history here.

WATCH

Quantum technology explained: