Wednesday, October 23rd, 2024

Enjoy a nice cup of freshly brewed quantum news ☕️ 

Today’s issue includes:

• Quantum neural networks are used in demand forecasting for competitive predictive accuracy with fewer parameters and faster convergence.

• Daemonic work extraction from open quantum batteries is experimentally simulated.

• An accelerated quantum circuit Monte Carlo framework uses quantum amplitude estimation to simulate heavy quark thermalization in quark-gluon plasma.

• Plus, a request for ethical governance of quantum technology, another European quantum company expands into the US, a quantum chip collaboration, and more.

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

💵  Quantum Neural Networks For Demand Forecasting Show Faster Convergence with Fewer Parameters

QUICK BYTE: Researchers from SENAI CIMATEC and the Federal University of Bahia West use quantum neural networks in demand forecasting for competitive predictive accuracy with fewer parameters and faster convergence.

DETAILS

• Financial markets are complex but require companies to comprehend quickly in order to adapt and remain competitive. For predicting future customer demand, also known as demand forecasting, the researchers considered QNNs for the possibility of advantages such as reduced computational costs and faster convergence compared to classical neural networks. The study specifically uses a vehicle financing dataset to compare QNN performance with classical recurrent neural networks.

• Vehicle financing data was provided from a Brazilian bank, with features reduced through Principal Component Analysis to optimize the input for the models. QNNs encoded this data using quantum circuits

• QNNs demonstrated similar or better predictive accuracy compared to classical RNNs, especially when using fewer parameters and requiring fewer epochs for convergence. The QNN models, even with reduced features, maintained competitive error rates while showing better scalability.

• Where financial forecasting is involved, QNNs may more effectively handle complex financial data for predictive analytics, providing faster and more efficient solutions than classical machine learning models. This has potential implications for real-time decision-making and risk management in finance.

👿 Researchers Simulate Daemonic Work Extraction in Open Quantum Batteries

QUICK BYTE: Scientists from Algorithmiq, the University of Kurdistan, and the Universita degli Studi di Milano experimentally simulated daemonic work extraction from open quantum batteries.

DETAILS

• Daemonic work extraction from quantum systems occurs when measurement outcomes from the environment increase the amount of extractable work. This phenomenon, known as daemonic ergotropy, allows for more efficient work extraction.

• An open quantum battery was simulated using IBM's cloud quantum platform, using mid-circuit measurements and feedback loops to optimize the work extraction process. The battery interacted with auxiliary qubits in a collisional model to represent interactions with the environment at discrete times.

• Quantum batteries are systems governed by quantum mechanics, with ergotropy being the maximum amount of work extractable from a quantum state. The study shows how measuring environmental systems and applying feedback can increase this extractable work.

• This work demonstrates the potential of quantum devices to implement open quantum systems and feedback protocols. Not only does this advance our understanding of measurement-based quantum thermodynamics, but also provides insight into improving energy efficiency in quantum systems.

🌌 Accelerated Quantum Monte Carlo Framework Simulates Heavy Quark Thermalization in Quark-Gluon Plasma with Quantum Speedup

QUICK BYTE: A researcher from the Galician Institute of High Energy Physics developed an accelerated quantum circuit Monte Carlo framework that uses quantum amplitude estimation to simulate heavy quark thermalization in quark-gluon plasma.

DETAILS

• Understanding how heavy quarks behave in the extreme conditions of a quark-gluon plasma is central to uncovering the fundamental properties of the early universe. The thermalization of heavy quarks in a quark-gluon plasma can be modeled as a stochastic process with drag (energy loss) and diffusion (momentum broadening) components. These interactions are key to understanding quark dynamics in high-energy physics.

• The study introduces an accelerated quantum circuit Monte Carlo method, using quantum amplitude estimation to simulate thermalization with fewer resources compared to classical Monte Carlo methods. This strategy uses quantum registers to encode momenta and perform stochastic evolution based on the Langevin equation.

• Simulations of heavy quark thermalization in both isotropic (1D) and anisotropic (2D) media were performed using IBM's QASM simulator. The results show how quark momentum thermalizes, with elliptic flow characterizing anisotropization in 2D media, matching theoretical equilibrium predictions.

• The aQCMC method achieves a quadratic speedup in computational efficiency over classical methods, suggesting its potential for broader applications in high-energy physics.

A recent Nature article argues that while quantum technologies may provide innovations for national defense, such as quantum sensors for precision detection and quantum communications resistant to jamming, they also introduce significant ethical risks. These include potential threats to privacy, the creation of new weapons, and the undermining of encryption, which could have serious security implications. The authors insist on the urgent need to develop anticipatory ethical governance, ensuring that quantum technologies are designed and developed responsibly, addressing risks before they become unmanageable.

IBM has introduced new educational resources on its IBM Quantum Learning platform, including "learning pathways" that guide users through recommended courses and tutorials based on their experience levels. These pathways, such as the "Getting started with Qiskit" and "Scaling toward quantum utility," are designed to help learners progress in their quantum education. Additionally, IBM has made its Quantum Business Foundations course, designed for business executives, publicly available to increase quantum literacy and aid decision-making around quantum computing investments.

The growing integration of Quantum AI into industries is expected to drive innovation and efficiency, with Allied Market Research projecting a 36.6% CAGR for the Quantum AI market from 2024 to 2032. Quantum AI technology may have potential applications in industries such as finance, healthcare, and material science. Recent collaborations, including QuEra's partnership with Google Quantum AI and D-Wave's joint project with Japan Tobacco, are geared towards real-world applications and reflect market interest.

Researchers from the University of New South Wales, Diraq, imec, and KU Leuven successfully fabricated high-fidelity silicon spin qubits using 300mm CMOS foundry technology, achieving over 99% fidelity in qubit operations. This study demonstrates the scalability of silicon spin qubits for industrial production, and builds on previous research, addressing noise issues like nuclear spin noise through isotopic purification, resulting in longer coherence times. The research highlights the transition from academic prototypes to large-scale, industrial-grade quantum processors, which are essential for fault-tolerant quantum computing.

Nord Quantique has announced partnerships with the MiQro Innovation Collaborative Centre (C2MI) in Quebec and NY CREATES in New York to secure its supply chain for chip fabrication, focusing on superconducting and CMOS-based qubit production. These collaborations, part of the Northeast Semiconductor Manufacturing Corridor, aim to ensure reliable, scalable microchip production for Nord Quantique’s quantum computers, supporting their long-term growth strategy. The partnerships will boost the company’s fabrication, packaging, and testing processes, helping to mitigate supply chain risks and advance their quantum error-corrected computing architecture.

Multiverse Computing has announced an expansion to the U.S. with a new office in San Francisco, led by Chris Zaharias as Vice President of Sales. The expansion intends to accelerate the adoption of its AI and quantum-based solutions among U.S. customers, including businesses and government. Additionally, Multiverse Computing was selected for the 2024 AWS Generative AI Accelerator program, which will help enhance its CompactifAI software, designed to optimize large AI models, reduce energy demands, and lower development costs for applications like ChatGPT and Bard.

LISTEN

In the most recent episode of the Superposition Guy’s Podcast, host Yuval Boger, Chief Commercial Officer of QuEra, interviews whurley, co-founder and CEO of StrangeWorks. They discuss whurley’s journey from founding Honest Dollar to starting Strangeworks, the lessons learned in bridging science and business communication in quantum, and the transition from quantum-only to advanced computing. Whurley also shares his views on the symbiotic relationship between AI and quantum computing, the importance of partnerships in building the industry, and the company’s hiring plans. He reflects on the current state of collaboration in the quantum industry, his optimism for future breakthroughs, and much more.

WATCH

The panel discusses the potential applications of quantum computing in financial services, including post-quantum cryptography, open standardization challenges, and the projected advancements in the industry over the next five years: