Alexander Tsyplikhin, PhD

My latest guest on the Superposition Guy's Podcast is Tal David, CEO and co-founder of Quantum Art, a quantum computing company based in Israel focused on full-stack quantum computing with trapped ions. We discuss Quantum Art's unique architecture, which emphasizes sophisticated multi-qubit gate operations, optical tweezers for segmenting ion chains, and dynamic reconfigurability with the potential of scaling up to millions of qubits. Tal highlights the company's roadmap, including launching a 50-qubit system by 2025 and a 1,000-qubit system by 2027, with a long-term goal of achieving a million-qubit system. Tal also addresses the challenges and advantages of their approach, particularly regarding error correction and speed, and mentions collaborations with companies like NVIDIA and BlueQubit, emphasizes the importance of focusing on scalable architectures, and much more. Transcript: https://lnkd.in/dqCrDkDC Spotify: https://lnkd.in/dmP5WF9Y

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Reflections from the Silicon Horizon Last Thursday, Berkeley SkyDeck and Synopsys Inc brought together founders, investors, operators, and researchers for “The Silicon Horizon: The Next Decade for the Chip Industry.” It was energizing to help emcee a conversation that stepped back from the next node or the next funding round and focused on where this industry is really heading. What stood out most wasn’t any single prediction, but the consistency of the message across very different vantage points. We’re entering a decade where progress is less about brute-force scaling and more about how intelligently we design, integrate, and deploy silicon—from architecture and memory to packaging and system-level thinking, especially for AI-driven workloads. For startups in particular, speed to tape-out is becoming existential, not optional. And just as important, no one advances alone. The strongest insights reinforced how tightly coupled this ecosystem has become—academia, EDA and cloud platforms, foundries, and capital all moving together, increasingly organized around specific problems and workloads rather than general-purpose chips. It feels like a moment where collaboration and alignment matter more than sheer scale. Thank you to the speakers and panelists for sharing their perspectives so candidly: Borivoje Nikolic (UC Berkeley) Sridhar Panchapakesan (Synopsys) Krste Asanovic (SiFive) Ravi Subramanian (Synopsys) Vijay R. (Mayfield) Paul Rousseau (TSMC) And many thanks to Chon Tang for guiding a thoughtful panel discussion. It was great to see so many thought leaders join us and help make the event what it was. Looking forward to continuing these conversations as the silicon horizon comes into clearer focus. #SiliconHorizon #Semiconductors #ChipStartups #EDA #AIHardware #BerkeleySkyDeck #Synopsys

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The D-Wave / Quantum Circuits deal is another sign that the capital markets in quantum are finally maturing: public companies are no longer relying on single, monolithic architectures or vertically-integrated business models, but instead assembling best‑in‑class capabilities via acquisition to build useful offerings faster than pure R&D could. This mirrors what we’re seeing elsewhere: IonQ buying Oxford Ionics was a bold pivot (and a good one) to captialize and enable the latest in trapped‑ion scalability and semiconductor integration, Google aqui-hiring Atlantic Quantum's superconducting control stack, and now D‑Wave finally getting into gate-based systems and away from its questionable annealing approach. For investors, it is an exciting time and a great opportunity. The old model of “build everything in‑house” is giving way to a new, outward-facing-capital playbook: acquire and integrate specialized teams and IP that accelerates the path to commercial relevance (and revenue). We’re entering an era where the winners in quantum will be those that can seamlessly blend internal execution with external capability. D‑Wave announcement: https://lnkd.in/eQUmPKW3

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Learn to build conversational AI voice agents in "Building AI Voice Agents for Production", created in collaboration with LiveKit and RealAvatar, and taught by Russ d'Sa (Co-founder & CEO of LiveKit), Shayne Parmelee (Developer Advocate, LiveKit), and Nedelina Teneva, PhD (Head of AI at RealAvatar, an AI Fund portfolio company). Voice agents combine speech and reasoning capabilities to enable real-time conversations. They're already being used to support customer service, to improve accessibility in healthcare, for entertainment applications, and for talk therapy. In this course, you’ll learn to build voice agents that listen, reason, and respond naturally. You’ll follow the architecture used to create the "AI Andrew" Avatar, a collaborative project between DeepLearning.AI and RealAvatar that responds to users in what sounds like my voice. You’ll build a voice agent from scratch and deploy it to the cloud, enabling support for many simultaneous users. What you’ll learn: - Understand the fundamentals of voice agents, including key components like speech-to-text (STT), text-to-speech (TTS), and LLMs, and how latency is introduced at each layer. - Explore voice agent architectures and the trade-offs between modular pipelines and speech-to-speech APIs. - Explore how platforms like LiveKit mitigate latency issues with optimized networking infrastructure and low-latency communication protocols. - Learn how to connect client devices to voice agents using WebRTC—and why it outperforms HTTP and WebSocket for low-latency audio streaming. - Incorporate voice activity detection (VAD), end-of-turn detection, and context management to detect turns, handle interruptions, and manage conversational flow. - Understand the trade-offs between latency, quality, and cost in an example in which you build a voice agent and change its voice. - Equip your agent with metrics to measure latency at each stage of the voice pipeline and learn the key levers you can pull to make your agent faster and more responsive. The voice agents built in this course also incorporate voice technology from ElevenLabs, a supporting contributor to the project. By the end of this course, you'll have learned the components of an AI voice agent pipeline, combined them into a system with low-latency communication, and deployed them on cloud infrastructure so it scales to many users. I’m looking forward to seeing what voice agents you build from this course! Please sign up here: https://lnkd.in/gEBXAGH8

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MDPI, Eldar Sultanow, Dr. Mark Tehrani, Siddhant Dutta and Muhammad Shahbaz Khan, “Defining Quantum Agents: Formal Foundations, Architectures, and NISQ-Era Prototypes” This work introduces a formal definition of quantum agents and outlines architectures that integrate quantum computing with agent-based systems. As concrete proof-of-concept implementations, the authors develop and evaluate three quantum agent prototypes: (i) a Grover-based decision agent for quantum search-driven action selection, (ii) a variational quantum reinforcement learning agent for adaptive policy learning in a multi-armed bandit setting, and (iii) an adaptive quantum image encryption agent that autonomously selects encryption strategies based on entropy-driven feedback. These prototypes demonstrate practical realizations of quantum agency in decision-making, learning, and security contexts under NISQ-era constraints. CONCURRENCE  

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Reinforcement Fine-Tuning (RFT) is a technique that adapts Large Language Models (LLMs) to intricate reasoning tasks. Dive deep into a comprehensive exploration of RFT with GRPO in this course. Learn how to construct reward functions crucial for steering LLMs towards enhanced performance on complex, multi-step reasoning tasks during the GRPO training process.

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Aidoc just closed $150 million in funding to scale its clinical AI platform beyond radiology. Led by General Catalyst and Square Peg, with investment from Nvidia’s venture arm and four major U.S. health systems (Hartford HealthCare, Mercy, Sutter Health, WellSpan), this brings their total raised to $370M, including a $40M credit facility. Founded in 2016, Aidoc built its reputation on FDA-cleared radiology tools, pushing into intracranial hemorrhage, pulmonary embolism, and rib fractures. Now they’re expanding into cardiology, neurovascular, vascular, and even oncology use cases, all powered by their new CARE™ foundation model. It’s designed to build new clinical solutions up to 20x faster, with FDA-cleared examples already under its belt. #HealthTech #ClinicalAI #Startups

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Quantum technologies are moving from theory to practice, and standards will be critical for scaling safely and effectively. ETSI’s newly approved Technical Committee on Quantum Technologies (TC QT) is an important step toward building that foundation. As global standards evolve, collaboration will be key to ensuring secure and scalable adoption of quantum technologies. Explore how SandboxAQ is contributing to this effort: https://lnkd.in/etjSQh9W

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https://lnkd.in/giYzZHHa CES 2026 made one thing clear: the line between science fiction and real engineering is disappearing fast. From robots leaving the lab, to wearables disappearing into daily life, to AI systems running into unexpected bottlenecks – engineering and test teams are being asked to turn flashy demos into reliable, scalable products. I’ve put together a 3-part CES 2026 series for engineers and technology leaders: Part 1: Robots Are Leaving the Lab Part 2: Wearables Are Becoming Invisible Part 3: The AI Bottleneck Nobody Is Talking About Each article covers what’s emerging and what it really takes to deploy these technologies at scale.

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