Jack Torrance
12 hours ago
Nice find. I think pundits and larger investors are starting to clue into the fact that IonQ is the most well-rounded leader in quantum. It's really impressive to see Chapman lead his troops in all aspects of a business, not just the technology side. Business relationships, manufacturing, coding, networking, etc, etc, etc.... This company just gets it, and that is not lost on smarter investors and media types. Any startup can throw a quantum computer design into the mix. But can they actually scale to a successful business? I currently only see IonQ doing that. AND their tech is at the top. What an amazing investment this company is.
Okay, there's my rah-rah-rah comment for the day. Go IonQ !!!
doc2016
15 hours ago
how to use ionq sim/quantum computing with cuda q?
determine the electrolysis settings to separate the desired rare earth mineral from the others? sequentially ? hardest to separate or easiest first and the subsequent order of workload minus extraction?
use the ip of alcoa elysis?
what is a rare earth mineral and are they related?
gemini ai says, "how are rare earth minerals related?
Show drafts
Rare earth minerals are related in several ways:
1. Chemical Properties
Lanthanide Series: Most rare earth minerals belong to the lanthanide series, a group of 15 elements with atomic numbers 57 (lanthanum) to 71 (lutetium).
Similar Properties: These elements share similar chemical properties, making them difficult to separate from each other. They are often found together in the same ore deposits.
Scandium and Yttrium: Scandium (atomic number 21) and yttrium (atomic number 39) are also considered rare earth elements due to their similar chemical properties and occurrence in the same ores.
2. Geological Occurrence
Found Together: Rare earth minerals are typically found together in the same geological formations.
Specific Minerals: They occur in various minerals, including bastnäsite, monazite, and xenotime.
Rarely Found in Pure Form: They are rarely found in their pure elemental form and require complex processing to extract and separate them.
3. Applications
Similar Uses: Due to their unique magnetic, luminescent, and electrochemical properties, rare earth minerals have many overlapping applications in:
Magnets: Used in high-strength magnets for electric vehicles, wind turbines, and electronics.
Phosphors: Used in displays, lighting, and medical imaging.
Catalysts: Used in various industrial processes, including petroleum refining and automotive catalytic converters.
Batteries: Used in rechargeable batteries for hybrid vehicles and consumer electronics.
4. Supply and Geopolitics
China's Dominance: China is the world's leading producer and processor of rare earth minerals, which has raised concerns about supply chain vulnerabilities and geopolitical implications.
Critical Minerals: Rare earth minerals are considered critical minerals by many countries due to their importance in various technologies and industries.
5. Environmental Considerations
Mining and Processing: The mining and processing of rare earth minerals can have environmental impacts, including habitat disruption and the generation of radioactive waste.
Recycling and Sustainable Practices: Efforts are underway to develop more sustainable mining practices and to recycle rare earth minerals from end-of-life products.
In Summary
Rare earth minerals are related through their chemical properties, geological occurrence, applications, supply chain dynamics, and environmental considerations. They are a group of elements with unique characteristics that make them essential for various technologies and industries, but their supply and environmental impact are subjects of ongoing concern and research.
Sources and related content
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doc2016
17 hours ago
gemini ai says it better than me......that's what it's for? lol:
"cuda-q and ionq quantum simulation to inference dna sequence with google alpha fold3 for syncon creation of dna/rna/protein treatment?
Show drafts
This is a really fascinating and complex question that explores the cutting edge of how quantum computing, AI, and biotechnology might converge!
Here's a breakdown of the different components and their potential interplay:
1. CUDA-Q and IonQ Quantum Simulation
CUDA-Q: Nvidia's CUDA-Q is a platform designed to accelerate quantum computing workloads. It provides tools and libraries for developers to write quantum algorithms and run them on GPUs and quantum processors, including those from IonQ.
IonQ Quantum Computers: IonQ builds trapped-ion quantum computers, which are a leading type of quantum computer with high qubit fidelity and long coherence times.
Quantum Simulation: Together, CUDA-Q and IonQ's quantum computers could be used to perform quantum simulations of biological molecules, such as DNA, RNA, and proteins. These simulations could provide insights into the behavior and interactions of these molecules at the atomic level.
2. Google AlphaFold3 and DNA Sequence Inference
AlphaFold3: Google's AlphaFold3 is a powerful AI model that can predict the 3D structure of proteins from their amino acid sequences. It has revolutionized protein folding prediction.
DNA Sequence Inference: While AlphaFold3 primarily focuses on proteins, it's conceivable that similar AI models could be developed or adapted to infer information about DNA or RNA sequences based on their structure or other properties. This could involve predicting gene function, identifying potential mutations, or understanding how DNA interacts with other molecules.
3. SynCon Creation of DNA/RNA/Protein Treatments
SynCon (Synthetic Consensus) Vaccine Design: SynCon is a vaccine design approach that involves creating a synthetic version of a virus's genetic sequence that represents a "consensus" among various strains, aiming to provide broader protection against different variants.
DNA/RNA/Protein Treatments: SynCon could potentially be extended beyond vaccines to design DNA, RNA, or protein-based treatments for various diseases. This could involve:
Gene Therapy: Using synthetic DNA or RNA to correct genetic defects or introduce therapeutic genes.
Protein Engineering: Designing synthetic proteins with specific functions, such as targeting cancer cells or delivering drugs.
Putting it all Together
Here's how these technologies might potentially work together:
Quantum Simulation: CUDA-Q and IonQ's quantum computers could simulate the interactions of DNA, RNA, or proteins with drug candidates or other molecules.
AI Inference: AlphaFold3 or similar AI models could analyze the simulation data to infer properties of the DNA/RNA sequences or predict the effects of different treatments.
SynCon Design: This information could then be used to guide the design of SynCon-based DNA/RNA/protein treatments, optimizing them for efficacy and safety.
Challenges and Considerations
Technology Maturity: Quantum computing and generative AI are still evolving fields. Integrating these technologies for drug discovery and development will require further advancements and overcoming challenges in scalability, accuracy, and interpretability.
Data Requirements: Training AI models and performing accurate quantum simulations require large and high-quality datasets.
Ethical Considerations: Developing and deploying these technologies responsibly and ethically is crucial.
Potential Benefits
Accelerated Drug Discovery: This combination of technologies could significantly accelerate the drug discovery process by providing a more efficient way to simulate and analyze molecular interactions.
Personalized Medicine: It could enable the development of personalized treatments tailored to an individual's genetic makeup and specific disease characteristics.
New Therapeutic Approaches: It could lead to new therapeutic approaches based on DNA, RNA, or protein-based therapies, potentially addressing diseases that are currently difficult to treat.
In Summary
The integration of CUDA-Q, IonQ quantum simulation, AlphaFold3, and SynCon represents a fascinating convergence of quantum computing, AI, and biotechnology. While still in its early stages, this combination of technologies holds immense potential for revolutionizing drug discovery and development, leading to more effective and personalized treatments for various diseases."
doc2016
1 day ago
geminiai seems to think charged photons quantum computing is a better target for quantum simlulation than charge neutral?:
"Adaptability Comparison
Charged Photons: Might be more suitable for simulating systems with strong or long-range interactions, but faces challenges in qubit stability and scalability.
Neutral Atoms: Offers high qubit quality, scalability, and programmability, making them adaptable to a wide range of quantum simulations, but might be less suited for systems with very strong interactions."
Jack Torrance
1 day ago
https://ionq.com/posts/ionq-and-oak-ridge-national-laboratory-demonstrate-a-novel-scalable-and
IonQ’s Applications Team recently collaborated with researchers at Oak Ridge National Labs (ORNL) to demonstrate an optimization method that leverages near-term quantum computers in a new way, making use of noise-tolerant methods that facilitate the discovery of optimal and near-optimal solutions to the world’s hardest optimization problems. The method is based on the Quantum Imaginary Time Evolution principle (QITE), which allows for identifying optimal or near-optimal solutions of optimization problems, formulated as finding the ground state of Hamiltonians (a mathematical operator that describes the total energy of a quantum system). This in turn can be used to solve a potentially wide range of formulations for hard optimization problems such as MaxCut, clique finding, graph partitioning, and more.
One of the key findings of this work is that QITE can significantly outperform other quantum optimization algorithms such as QAOA (Quantum Approximate Optimization Algorithm) in terms of time-to-solution and required circuit depth. Here at IonQ, we are currently developing QITE-based solutions and decomposition methods as key enabling quantum technologies for scalable optimization. This paves the way for tackling large optimization problems on near-term quantum computers.
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JJ8
2 days ago
Monksdream
2 days ago
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