Monday, October 7th, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Quantum Monte Carlo integration combined with simulation-based optimization addresses risk management and portfolio optimization problems in financial modeling.

• A hybrid approach combines the quantum approximate optimization algorithm and quantum-enhanced Markov Chain Monte Carlo to improve computational efficiency and accuracy in data assimilation for numerical weather prediction and climate modeling.

• A quantum algorithm uses second-order perturbation theory to calculate accurate energy estimates for quantum chemistry problems, reducing the number of qubits required.

• Plus, QKD with photonic integrated circuits, quantum autoencoders for anomaly detections, QKANs, and more.

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

🧮 Quantinuum and HSBC Use Quantum Monte Carlo for Financial Risk and Portfolio Optimization

QUICK BYTE: Quantinuum and HSBC integrate quantum Monte Carlo integration with simulation-based optimization to address risk management and portfolio optimization problems in financial modeling, demonstrating the potential of QMCI to improve accuracy in risk estimation and optimization under uncertainty, despite practical limitations on current quantum hardware.

DETAILS

• Quantum algorithms, particularly QMCI, may be valuable for financial optimization problems such as Value-at-Risk and Conditional-Value-at-Ris estimations. The team applied QMCI as a subroutine in SBO for financial problems, including mean-variance and mean-CVaR portfolio optimization, and compared it to classical methods.

• Through the study, the authors demonstrate that QMCI provides a quadratic advantage in convergence rates compared to classical Monte Carlo methods, though the implementation is sensitive to systematic errors, especially in state preparation and noise.

• The research is relevant for financial institutions looking to apply quantum computing to optimization problems, such as portfolio management, risk-return trade-offs, and asset allocation under uncertainty. Additionally, this work contributes to the growing body of research on quantum finance by exploring QMCI's practical use for real-world financial problems. It provides a systematic error analysis which evaluates the accuracy and possibility of QMCI in optimization tasks, suggesting that while promising, further improvements in quantum hardware and error correction are necessary to fully realize these benefits.

🌩️ Florence Quantum Labs Proposes Hybrid Quantum Approach to Boost Efficiency and Accuracy in Weather Forecasting

QUICK BYTE: Research from Florence Quantum Labs proposes a hybrid approach combining the quantum approximate optimization algorithm and quantum-enhanced Markov Chain Monte Carlo to improve computational efficiency and accuracy in data assimilation within the Four-Dimensional Variational Data Assimilation (4DVAR) method used in numerical weather prediction and climate modeling.

DETAILS.

• According to the study, integrating QAOA and QMCMC with particle filters in a 4DVAR framework can provide a quadratic speedup in convergence rates and improved accuracy when addressing high-dimensional and nonlinear systems like climate and weather prediction models.

• The QAOA is applied to optimize particle initialization for data assimilation, while QMCMC improves the sampling efficiency by using quantum amplitude amplification to accelerate the selection of high-likelihood particles.

• This effectively reduces the computational overhead typically associated with large-scale, nonlinear data assimilation problems, providing a practical framework for weather forecasting and climate modeling applications where accuracy and computational speed are most relevant.

• The research is especially relevant for improving the capabilities of numerical weather prediction models, exemplifying the potential of quantum algorithms to solve real-world problems in geophysical systems, space weather prediction, and other complex, high-dimensional fields.

🧪 Copenhagen and Zurich Scientists Develop Algorithm to Improve Accuracy and Reduce Qubit Use for Energy Calculations in Quantum Chemistry

QUICK BYTE: Scientists from the University of Copenhagen and ETH Zurich introduce a quantum algorithm that uses second-order perturbation theory to calculate accurate energy estimates for quantum chemistry problems, reducing the number of qubits required by using unperturbed Hamiltonians and time-evolution step — “more quantum chemistry with fewer qubits.”

DETAILS

• A quantum algorithm uses second-order perturbation theory to compute energy corrections for quantum chemistry problems. It evaluates the second-order energy correction by using time-evolution steps under an unperturbed Hamiltonian, providing a more accurate calculation of ground-state energies without requiring an increase in the number of qubits.

• The algorithm reduces the number of qubits required to include virtual orbitals in calculations, demonstrating that even with limited quantum hardware, accurate energy estimates can be obtained by going beyond the active-space approximation.

• The algorithm is applied to multireference perturbation theory and symmetry-adapted perturbation theory, demonstrating its ability to capture dynamic correlation effects and accurately model intermolecular interactions in complex chemical systems, using fewer resources compared to classical methods.

• Numerical simulations confirm that the algorithm achieves favorable scaling in runtime and qubit requirements, especially in cases where traditional quantum chemistry methods would be computationally intense.

The Japanese government plans to invest tens of billions of yen (10 billion yen is approximately $67.5 million) by 2030 to support the development of quantum encryption technology. The Ministry of Internal Affairs and Communications will collaborate with companies such as Toshiba and NEC, beginning public-private investments in fiscal 2025. Quantum encryption, which uses photon-based keys transmitted through fiber optics, is expected to secure sensitive data by making eavesdropping detectable. NICT will evaluate these encryption methods through a test network connecting government and private entities in Tokyo.

Scientists from the European Laboratory for Non-Linear Spectroscopy, Istituto Nazionale di Ottica, QTI and others advance quantum key distribution technology using photonic integrated circuits. The authors implemented a three-state BB84 protocol with a decoy-state method, achieving a secure key transmission over a 45 dB channel attenuation, equivalent to a distance of 281 km in standard low-loss optical fibers. By integrating a borosilicate glass-based Mach-Zehnder interferometer in the receiver, the system demonstrates low propagation losses, stability over 50 hours of continuous measurement, and significant potential for scalable, secure quantum communication.

QURECA has launched the Qureka! Box, an interactive educational resource designed to make quantum computing accessible and engaging for high school students, undergraduates, professionals, and the general public. The Qureka! Box simplifies complex quantum concepts through hands-on, game-based materials that encourage teamwork and critical thinking. It is available in both English and Spanish and comes with a training program led by experienced professionals, which equips educators and learners with practical tools and expertise to integrate quantum computing into their curricula.

Researchers from the Zurich University of Applied Sciences explored how quantum autoencoders can be used to identify anomalies in time series data. The researchers used two techniques: analyzing reconstruction errors and looking at the latent representations of the data. Their results show that quantum autoencoders outperform classical deep learning models, requiring far fewer parameters and training cycles. They also successfully tested their quantum autoencoder on real quantum hardware, achieving similar results to simulations, even with quantum noise. Methods for anomaly detection are relevant for applications related to fraud detection and pattern recognition.

TreQ, a UK-based quantum computing startup led by US Air Force Reserve Brigadier-General Mandy Birch, has secured $5 million in seed funding from investors including Lavrock Ventures, Creator Fund, firstminute capital, and Green Sands Equity. TreQ aims to develop open-architecture quantum computers by partnering with small and medium enterprises to integrate diverse technologies, enabling scalable and adaptable systems. Headquartered in Oxfordshire, TreQ plans to leverage the UK’s quantum talent and infrastructure to accelerate development.

Scientists from the Centre for Quantum Technologies at the National University of Singapore introduces a quantum version of the classical Kolmogorov-Arnold Network (KAN), designed for machine learning tasks on quantum hardware. QKAN uses quantum techniques such as block-encoding and quantum singular value transformation to implement flexible activation functions. It applies Chebyshev polynomials to handle high-dimensional inputs, making the model scalable and efficient. This architecture could have broad applications in scientific computing, particularly for solving complex problems that are difficult for classical methods.

LISTEN

On the most recent episode of Qubit Confidential, host Christopher Bishop sits down with Manfred Rieck, Vice President of Individual Solution Development at Deutsche Bahn. They discuss how Deutsche Bahn is optimizing train routes and schedules using a hybrid approach of quantum and high-performance computing, as well as the importance of senior management support in adopting quantum technologies.

ENJOY

Not quantum, but computer science — how do you prove something is true? For centuries, mathematicians have relied on step-by-step proofs. But in the 1980s, computer scientists began revolutionizing proof methods. Two major breakthroughs—zero-knowledge proofs, which verify a statement without revealing why it's true, and probabilistically checkable proofs, which let verifiers check tiny pieces of a proof—reshaped the field. Recently, researchers finally combined the ideal forms of these two proof types, solving a long-standing problem and opening up new possibilities for cryptography and theoretical computer science. These advancements could have far-reaching implications, reigniting interest in zero-knowledge PCPs and pushing the boundaries of what’s provable.

WATCH

Dr. Inés de Vega, VP of Quantum Solutions at IQM, discusses quantum technology's business benefits at Nordic Business Forum 2024 in Helsinki: