Error Corp: Shaping the Future of Error Control for Quantum Computers

Quantum computing promises to revolutionize numerous fields, from developing new materials and breaking encryption to solving complex optimization problems more efficiently than classical computers. 

However, a significant hurdle remains: error correction. With every step of a quantum computation, errors accumulate, ultimately leading to inaccurate results. This limits today’s quantum computers to performing only shallow calculations with a few qubits and steps, significantly constraining their potential.

Error Corp pioneers a new approach to detect and correct quantum errors more efficiently, promising to reduce the number of qubits required for quantum error correction drastically. Founded by Dennis Lucarelli, in early 2022, it has the potential to propel quantum computing beyond its current limitations and address some of the most challenging problems in science and technology.

Future of Computing News

🤖 Ensuring that sensitive information is never exposed: Launching confidential LLM platform Continuum AI (Edgeless Systems)

🤖 Amazon reverse acquihiring Adept: This is Big Tech’s playbook for swallowing the AI industry (The Verge)

🤖 Some commentary on the Adept acquihire: Why AI Infrastructure Startups Are Insanely Hard to Build (Substack)

🤖 News out of France: Kyutai Open Sources Moshi: A Real-Time Native Multimodal Foundation AI Model that can Listen and Speak (MarkTechPost)

🤖 Speedy LLMs: Google Introduces Gemma 2: Elevating AI Performance, Speed and Accessibility for Developers (UniteAI)

🧠 Toward energy-efficient AI: An Analog Network of Resistors Promises "Machine Learning Without a Processor," Researchers Say (Hackster)

⚛️ Helping quantum developers: Haiqu Releases Open-Source Rivet for Quantum Workflow Execution (HPCwire)

Funding News

🦾 Vaire Computing raises $4.5M for ‘reversible computing’ moonshot which could drastically reduce energy needs (TechCrunch)

Check out our previous interview with the founders of Vaire Computing: Shaping the Future of Near-Zero Energy Computing

⚛️ Danish start-up Kvantify secures EUR 10 million seed round to unlock quantum computing potential (Kvantify)

⚡️ CogniFiber raises $5 million to develop processors that perform AI tasks 1,000 times faster than Nvidia (CTech)

🤖 Peter Thiel's Founders Fund Leads $85M Seed Investment Into Open-Source AI Platform Sentient (CoinDesk)

🤖 Dust grabs another $16M for its enterprise AI assistants connected to internal data (TechCrunch)

🤖 Generative AI coding assistant startup Magic AI looking to raise $200M at $1.5B valuation (Silicon Angle)

Deep Dive: Smashing Google’s Quantum Supremacy Claims

In 2019, Google ran a particular calculation on its Sycamore quantum computer in 200 seconds that would have taken a classical supercomputer 10,000 years to complete. Thus, it claimed to have demonstrated quantum supremacy—a computation only a quantum computer could achieve in a reasonable timeframe. The results were published in Nature.

Now, a group of researchers from the Shanghai Artificial Intelligence Laboratory in China has shown that they can outperform Google’s sycamore processor for a similar computation in terms of speed and efficiency using a GPU cluster.

As detailed in their arXiv paper: “Notably, we have achieved a time-to-solution of 14.22 seconds with energy consumption of 2.39 kWh … outperforming Google's quantum processor Sycamore in both speed and energy efficiency, which recorded 600 seconds and 4.3 kWh, respectively.”

It shows how tricky it is to demonstrate quantum advantage, even when picking problems that quantum computers should be particularly good at solving. Computing is a moving frontier, as both classical algorithms and supercomputers keep improving. For more context, check out the articles by The Quantum Insider and NewScientist.

Good Reads

⚛️ Technology and Performance Benchmarks of IQM’s 20-Qubit Quantum Computer (IQM)

“Our spotlight is on a 20-qubit quantum computer, featuring the IQM Garnet QPU, which we will scale up to 150 qubits. Additionally, we share benchmarks for both the QPU and system levels, highlighting achievements such as a median 2-qubit gate fidelity of 99.5% and the genuine entanglement of all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.”

🤖 AI’s $600B Question (Sequoia Capital)

“In September 2023, I published AI’s $200B Question. The goal of the piece was to ask the question: “Where is all the revenue?” … If you run this analysis again today, here are the results you get: AI’s $200B question is now AI’s $600B question.”

🤖 RouteLLM: An Open-Source Framework for Cost-Effective LLM Routing (Lmsys org)

“LLM routing offers a solution to this, where each query is first processed by a system that decides which LLM to route it to. Ideally, all queries that can be handled by weaker models should be routed to these models, with all other queries routed to stronger models, minimizing cost while maintaining response quality.”

🤖 APIGen: Automated PIpeline for Generating Verifiable and Diverse Function-Calling Datasets (arXiv)

“This paper presents APIGen, an automated data generation pipeline designed to synthesize verifiable high-quality datasets for function-calling applications. We leverage APIGen and collect 3,673 executable APIs across 21 different categories to generate diverse function-calling datasets in a scalable and structured manner.”

🍀 The Hardware Lottery (arXiv)

“This essay introduces the term hardware lottery to describe when a research idea wins because it is suited to the available software and hardware and not because the idea is superior to alternative research directions.”

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