Entangling Intelligence Ai Quantum Crossovers And Perspectives

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2026 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions. With the scale of AI systems growing and datasets growing more complex, legacy machine learning methods start to break down when matched with speed, scalability, and resource consumption challenges. Quantum Machine Learning (QML) is a novel way of looking at things, which looks to integrate the pattern recognition capabilities of machine learning and the probabilistic potential of quantum computing. Things like superposition, entanglement, quantum interference have the potential to change the way we do things in circumstances in which classical models really suck such as optimization, search, and data classification. This chapter is heavy on the theory behind QML and how it fits into the real world.

It covers the most pivotal concepts in quantum computing, the most influential machine learning models and why the two domains ought to work together. There are various other QML algorithms referred to in the literature such as Quantum Support Vector Machines (QSVMS), Quantum Neural Networks (QNN), and Variational Quantum Classifiers (VQC). It also addresses how to encode quantum data and systems that are quantum and classical. First, we will learn how these concepts are applied to the real world in environments such as healthcare, finance, and materials research. Then, we conduct a comprehensive case study to bridge the gap between theory and practice. The chapter concludes with a discussion and ethics and technology and with pointers for future research.

This is a preview of subscription content, log in via an institution to check access. Nielsen MA, Chuang IL (2010) Quantum computation and quantum information. Cambridge University Press Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195–202. https://doi.org/10.1038/nature23474

Schuld M, Sinayskiy I, Petruccione F (2015) An introduction to quantum machine learning. Contemp Phys 56(2):172–185. https://doi.org/10.1080/00107514.2014.964942 According to NightSkyNow on Twitter, researchers have achieved a major milestone by successfully teleporting quantum states of light over active internet fiber cables, even as these cables were carrying regular data traffic (source: NightSkyNow,... This experiment demonstrated quantum teleportation using existing telecommunications infrastructure, leveraging quantum entanglement to transfer quantum information securely. The breakthrough suggests that the development of a quantum internet may not require new hardware installations, presenting substantial business opportunities for telecom and cybersecurity sectors.

As quantum communication becomes viable over current networks, enterprises could soon deploy ultra-secure data transmission solutions protected by the laws of physics, reducing the need for traditional encryption and enhancing data privacy. This advancement marks a significant step toward the commercialization of quantum networking and next-generation secure internet services. This official DARPA account showcases groundbreaking research at the frontiers of artificial intelligence. The content highlights advanced projects in next-generation AI systems, human-machine teaming, and national security applications of cutting-edge technology. About Massachusetts Institute of Technology,Department of Physics,MA,USA,02139 About Massachusetts Institute of Technology,Department of Physics,MA,USA,02139

Please login to MyJ-GLOBAL to see full information. You also need to select "Display abstract, etc. of medical articles" in your MyJ-GLOBAL account page in order to see abstracts, etc. of medical articles. Copyright (c) 2009 Japan Science and Technology Agency. All Rights Reserved

Corresponding author. abhishek.gmit@gmail.comamukhopadhyay@kol.amity.edu Received 2025 Aug 9; Revised 2025 Nov 5; Accepted 2025 Nov 13; Collection date 2025. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords: Precision medicine, Quantum computing, Quantum machine learning, Explainable artificial intelligence This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine.

We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning models and XAI techniques applied to drug discovery, disease diagnosis, patient monitoring and biomarker identification. We introduce a taxonomy of hybrid and quantum-explainable approaches, evaluate NISQ hardware and encoding constraints, and compare interpretability methods (SHAP, LIME, QSHAP, QLRP, TSBA). Two case studies (doxorubicin cardiotoxicity prediction and pre-symptomatic IBD flare forecasting) demonstrate hybrid variational-quantum pipelines wrapped with SHAP-based explanations. We identify practical barriers (noise, data encoding, regulation, privacy) and outline research directions to benchmark clinical quantum advantage and develop scalable, transparent QXAI frameworks. The survey aims to guide interdisciplinary efforts toward trustworthy, scalable quantum-enabled precision healthcare. Quantum computing hardware has matured significantly over the years, with several noisy intermediate-scale quantum (NISQ) computers already being available for public use in the cloud.

Larger-scale hardware with capabilities such as quantum error correction and fault tolerant implementations of universal set of gates are currently being developed to support general purpose fault tolerant quantum computation. Quantum algorithms requiring general purpose FTQC, in the vein of the classic algorithms such as Shor’s algorithm for factorization, and Grover’s algorithm for search algorithm, continue to be developed. Additionally, in keeping with the trend of machine learning and AI in classical computing, quantum machine learning has emerged as a key application of quantum computers already in the NISQ era. Use cases range from quantum chemistry and drug design to financial portfolio optimization and fraud detection, to potentially more in the upcoming fault tolerance era. Quantum computing also poses a significant threat to public key cryptography such as RSA and elliptic curve cryptography, warranting the development of post quantum cryptography. It is likely that quantum computers for the foreseeable future will be housed in remote servers.

While cloud access for current quantum computers is limited to classical instructions and responses since they are hosted over the classical internet that is only capable of transporting classical information bits, quantum networks capable... The latter would enable distributed quantum information processing in general, including the exchange of quantum instructions or inputs and responses between cloud quantum servers hosting powerful quantum computers or trained QML models, and small-scale... Delegated and distributed quantum computation over both the present day classical networks and future quantum networks bring with it issues related to trust, privacy and security. Some examples include, from the client perspective, protection of intellectual property (IP) of quantum algorithms and quantum data; and from the server perspective, defense against as denial-of-service attacks, defense against tomographic attacks to steal... Examples of remedies include, on the software side, blind quantum computation, entanglement distillation in quantum networks, quantum network coding, and on the hardware side, forbidding access to pulse-level instruction inputs. It is imperative to develop such countermeasures against these issues right away while quantum computing is still in its early days, which informs the goal of this workshop.

We invite submissions of previously unpublished works broadly in the areas of quantum computing, quantum machine learning, quantum networks, cybersecurity, and their interplay. Topics of interest include but are not limited to the following: Distributed quantum computing architectures 🪜 A Quantum Leap in AI Investments! The past week has been remarkable for the #AI and #QuantumComputing landscape. 1️⃣ Multiverse Computing secured a €67 million co-investment from the Spanish government to scale its AI model compression technology, CompactifAI, designed to cut #datacenter energy usage by nearly 50% and improve AI deployment across...

2️⃣ IQM Quantum Computers, based in #Finland, is in talks to raise over €200 million. The funding will support its Radiance quantum systems, including a 54-qubit launch expected later this year and a 150-qubit system by end of 2026. 3️⃣ SandboxAQ, an #Alphabet spinout, added $150 million to its Series E, totaling $450 million. With support from Google and NVIDIA, it’s advancing its Large Quantitative Models (LQMs) to solve problems in biopharma, materials science, and financial services. In my PoV, the convergence of #GenAI and #quantum is accelerating, drawing major capital and reshaping entire industries. As we build, invest, and test new models, from inference to infrastructure, I’m curious to hear.

❓ How are you thinking about the crossover between quantum and AI in your portfolio or innovation roadmap? 🎙️ Let’s chat. Jack Hidary Ray Dalio Jan Goetz Mikko Välimäki Enrique Lizaso Olmos #AI #QuantumComputing #VentureCapital #TechTrends #GenAI #Innovation InventX SandboxAQ IQM Quantum Computers Multiverse Computing Reuters Reuters: https://lnkd.in/gu32HuRC TFN:https://lnkd.in/gJbhWNUc Love your content . Because of you I started using A.I.. I hope you’ll connect!

Merging Ai and Quantum Computing: Here's the Stock to Watch A new stock emerges at the intersection of AI and quantum computing, signaling a major industry shift. This fusion promises to accelerate computing power and innovation across sectors globally, highlighting early investment opportunities in next-gen tech. For more information: https://lnkd.in/gSJRBMjr #AI #SiliconValley #Tech #Quantum #Innovation From NAI to QENAI: The Hybrid Quantum AI Revolution Has Begun For years, AI has been “narrow”, optimising what already exists rather than redefining what’s possible. But a new frontier is emerging: Hybrid Quantum AI - the bridge between today’s Narrow AI (NAI) and tomorrow’s Quantum-Enhanced Narrow AI (QENAI). ⸻ What It Means Hybrid Quantum AI blends classical computation (CPUs, GPUs) with quantum processors (QPUs).

The classical side manages memory and logic, while the quantum layer explores millions of possibilities simultaneously. It’s not “quantum replacing AI”. It’s quantum amplifying AI; allowing reasoning across vast uncertainty, in real time. ⸻ For Telcos Telecoms will be among the first to feel the impact: • Quantum-Optimised Network Steering: dynamic, predictive load balancing at atomic precision. • Quantum-Resilient Security: QENAI with post-quantum encryption and key distribution. • Predictive Maintenance: AI + quantum anomaly detection preventing failures before they happen.

• Smart Spectrum Allocation: adaptive spectrum use driven by probabilistic quantum modelling. Hybrid Quantum AI makes networks self-learning, self-healing, and quantum-secure. ⸻ For Enterprises Across finance, healthcare, logistics, and energy, QENAI delivers the ability to optimise across uncertainty: • Instant portfolio or route optimisation • Molecular and material simulation • Real-time risk modelling • Quantum-grade... ⸻ The Hybrid Decade The next five years belong to Hybrid Quantum AI - where enterprises access quantum power through the cloud. AI models will decide when and how to use quantum resources, delivering new value through Quantum-as-a-Service (QaaS). ⸻ The Evolution NAI automated decisions.

QENAI will elevate them - merging the logic of silicon with the probability of quantum physics. This is the intelligence layer of the future; fast, secure, decentralised, and quantum-ready. ⸻ RevoLabs roadmap is laser focused on where Narrow AI meets Quantum to empower telcos, enterprises, and investors to lead in the hybrid era. #QuantumAI #HybridComputing #NAI #QENAI #Telco #EnterpriseAI #RevoLabs #QuantumAsAService #AIRevolution #FutureTech InnovateAI Shares Skyrocket on Quantum Computing Breakthrough The core of the story is InnovateAI's announcement of its 'QuantumLeap' chip, which has demonstrated processing speeds orders of magnitude faster than current industry standards. This breakthrough has the potential to redefine capabilities in fields like drug discovery, financial modeling, and artificial intelligence.

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