Konaploinks
1日前
From chat gpt
Yes, IonQ and NVIDIA have recently demonstrated significant advancements in simulating molecular behaviors using hybrid quantum-classical approaches. IonQ has integrated its quantum computing capabilities with NVIDIA’s CUDA-Q platform to enhance the accuracy and efficiency of molecular simulations. This collaboration was showcased at events like NVIDIA’s GTC 2024 and the SuperCompute 24 conference, highlighting breakthroughs in simulating large molecules and optimizing hybrid quantum-classical workflows.
For example, NVIDIA’s Quantum Cloud allows researchers to simulate quantum algorithms and systems, including molecular interactions, by leveraging GPU-accelerated classical hardware. Meanwhile, IonQ has demonstrated simulations of molecular processes, such as studying chemical reactions for lithium compounds relevant to battery development. These efforts indicate that combining quantum and classical computing can push the boundaries of molecular simulation, potentially transforming fields like materials science and drug discovery?6??7??8?.
Konaploinks
2日前
A very big success! NVDA working together with IonQ successfully is very huge! Imagine the power these 2 corporations have working together. New prescription drugs, new cures for many diseases, new coatings and lubricants for metals, new logistic apps, new communication systems, new internet security, new quantum networks, new quantum cloud technologies , new secure banking , crypto, etc etc. What’s that worth? All IonQ investors are sitting pretty. Hold your shares tight. Way more to come. Anything under 30.00 now is gift. Many new military contracts coming. Tempo is on stage next with nvda’s Rubin. Ionq acquiring Qubitek networking .Ansys partnership.. Imec partnership and more to come. Great news today with nvda partnership and not to mention 5, count em, 5 new patents last WEEK! Hard to keep track of all of these p r ‘s. Up 74% in last 11 days. Whew! I gotta take a rest. Go watch the Kona sunset and relax. IonQ is on the move . Relax. Sip a Maitai at Don’s and chill. Hula girls at the Luau tonight. All IonQ investors in Kona soon. I’ll be here to get all of you settled in.
doc2016
3日前
chemistry applications...applications and cpaas and unfied communications of say an 8x8. nvidia and partner leadtek made graphics processor/gpu; leadte got soft fabric from 8x8 along with audacity and veracity software stacks.?
now take the msft deals with pharma like novartis and all the molecules they already have or looking for to interact with receptor sites and replace into molecules.
classical, quantum, quantum simulation and supercomputers with networks to allow faster and self checking results by using several quantum computers.
nvidia heating not a problem due to an overal approach of more efficient work flow , networking, dynamic routing, and authentication, use of x86 dell power edge servers with integrated cooling via air/liquid, sensors and effector/executable settings control on the dynamic work being done and when potentially to move the work or some of it to another network node? a comprehensive command and control headed up by an nvidia governor/like a softbank?
and when super micro comes back , them also?
then take the chemistry /bio application work for finding antibodies/proteins/rna and use upenn inovio syncon to create the plasmid dna vaccine.
all parts are now coming together.....deepmind alpha fold 3 certainly could use the nvidia/ionq computing powers.
Konaploinks
3日前
I have a feeling nvda and Ionq will be doing a j v or another partnership real soon . The memristors will play a huge role soon. They can solve the heat and power consumption problems in one swoop. …from doc
doc2016
Re: None
Monday, 11/18/2024 10:10:26 AM
more on why memristors?:
Does use of memristor create as much heat as silicon?
Show drafts
That's a very insightful question that gets at the heart of why memristors are so exciting for next-generation computing!
Generally, memristors produce significantly LESS heat than traditional silicon-based transistors. This is one of their key advantages and why they are being explored for energy-efficient AI and neuromorphic computing.
Here's a breakdown of why:
1. Different Operating Mechanisms
Transistors: Traditional transistors rely on the flow of electrons to switch on and off, which generates heat due to electrical resistance.
Memristors: Memristors operate by changing their resistance based on the history of applied voltage or current. This involves the movement of ions (charged atoms) within the material, a process that generally produces less heat than electron flow in transistors.
2. Reduced Energy Consumption
Lower Operating Voltage: Memristors can operate at lower voltages than transistors, which directly translates to lower power consumption and less heat generation.
Non-Volatile Memory: Memristors can retain their state even when power is off, eliminating the need for constant refreshing that consumes energy in traditional memory.
3. Potential for Neuromorphic Computing
Brain-Inspired Computing: Memristors are particularly well-suited for neuromorphic computing, which aims to mimic the structure and function of the human brain.
Energy Efficiency of the Brain: The brain is incredibly energy-efficient, and memristors offer the potential to create artificial synapses and neural networks that operate with similar low power consumption.
4. Ongoing Research and Challenges
Material and Design Optimization: Researchers are continually working to optimize memristor materials and designs to further reduce their energy consumption and heat generation.
Integration with CMOS: Integrating memristors with existing CMOS technology presents challenges, and ensuring efficient heat dissipation in these hybrid systems is crucial.
Implications
Energy-Efficient AI: Memristors could enable the development of more energy-efficient AI systems, reducing the environmental impact of AI and making it more sustainable.
Edge Computing: Their low power consumption makes them ideal for edge computing devices, where energy efficiency is critical.
Neuromorphic Computing: Memristors are paving the way for brain-inspired computing architectures that could revolutionize AI and other fields.
In Summary
Memristors offer a promising path towards lower-power and less heat-generating computing.
While challenges remain in their development and integration, their potential to improve energy efficiency in various applications, especially AI and neuromorphic computing, is significant.
Sources and related content
".
This involves the movement of ions (charged atoms) within the material, a process that generally produces less heat than electron flow in transistors.
There’s those pesky ions again. Go Peter go!
Konaploinks
3日前
IonQ uses the NVIDIA CUDA-Q platform alongside IonQ Forte to demonstrate an end-to-end application workflow
The work reaffirms IonQ’s focus on making quantum acceleration as simple and ubiquitous as GPU acceleration for on-prem and hybrid deployments
COLLEGE PARK, Md.--(BUSINESS WIRE)--Nov. 18, 2024--IonQ (NYSE: IONQ), a leader in the quantum computing and networking industry, announced today the completion of an industry-first demonstration of an end-to-end application workflow that leverages the NVIDIA CUDA-Q platform alongside IonQ’s leading quantum computing hardware.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241118387486/en/
IonQ to Advance Hybrid Quantum Computing with New Chemistry Application and NVIDIA CUDA-Q (Graphic: Business Wire)
Showcased via a joint presentation at SC24, the application demonstrates the seamless integration of the core workflow behind several hybrid quantum-classical approaches to calculate the specific properties of a molecule’s electronic structure. These approaches have potential relevance in many chemistry-focused commercial applications, such as identifying how drug molecules might interact with specific proteins in the human body. The achievement captures IonQ’s interest in developing solutions that combine the benefits of quantum processing units (QPUs) with the accelerated, heterogeneous computing capabilities of the NVIDIA CUDA-Q platform.
“IonQ is building on its approach of accelerating AI, scientific compute, and other supercomputing workloads with quantum hardware – enabling entirely new ways to approach previously unsolvable problems,” said Dean Kassmann, SVP of Engineering & Technology at IonQ. “Today’s molecular modeling demonstration illustrates why CUDA-Q is a great platform for achieving seamless integration and top-tier performance with our leading quantum hardware.”
Since 2023, IonQ has supported NVIDIA CUDA-Q, a powerful, unified, open-source software stack. CUDA-Q is a hybrid quantum-classical computing platform that enables the integration and programming of QPUs and GPUs in a single workflow. This demonstration was performed using a combination of IonQ Forte, the IonQ Hybrid Services suite, CUDA-Q, and NVIDIA A100 Tensor Core GPUs, which can be deployed to cloud and on-prem environments.
The demonstration at SC24 showcases a novel way to interrogate the properties of specific molecules in a resource-efficient way that has relevance in larger application workflows pertaining to pharma and other commercial domains. The work also sets the stage for IonQ to accelerate even more powerful ways to model molecular dynamics for future biopharmaceutical applications.
“Useful quantum applications will need to draw on both quantum hardware and AI supercomputing resources,” said Elica Kyoseva, Director of Quantum Algorithm Engineering, NVIDIA. “The CUDA-Q platform is allowing researchers and developers to explore these paradigms by accessing NVIDIA accelerated computing alongside IonQ’s quantum processors.”
Today’s news reaffirms IonQ’s commitment to developing hardware and software that leverages a combination of quantum and classical computation to enable efficient, performant, robust solutions that can be deployed at commercial scale.
IonQ’s Hybrid Services suite makes designing and deploying quantum-accelerated applications that take advantage of IonQ’s leading quantum hardware easier than ever. It achieves this via a flexible, easy-to-configure hybrid infrastructure, workflow management, and scheduling for cloud or on-prem installations. Further, it has powerful tools to help application developers explore new applications and use cases, such as prebuilt solvers for problem classes like quadratic optimization and graph partitioning, and out-of-the-box support for a variety of common programming models, including NVIDIA CUDA-Q quantum kernels.
To learn more about IonQ and its latest system news and business developments, visit https://ionq.com/.
You gotta ask yourself why wouldn’t Jensen want to own Ionq?
Konaploinks
3日前
Yes sir! Thank you Peter! And the Lord said “Ask. And you shall receive”
IonQ uses the NVIDIA CUDA-Q platform alongside IonQ Forte to demonstrate an end-to-end application workflow
The work reaffirms IonQ’s focus on making quantum acceleration as simple and ubiquitous as GPU acceleration for on-prem and hybrid deployments
COLLEGE PARK, Md.--(BUSINESS WIRE)--Nov. 18, 2024--IonQ (NYSE: IONQ), a leader in the quantum computing and networking industry, announced today the completion of an industry-first demonstration of an end-to-end application workflow that leverages the NVIDIA CUDA-Q platform alongside IonQ’s leading quantum computing hardware.
This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20241118387486/en/
IonQ to Advance Hybrid Quantum Computing with New Chemistry Application and NVIDIA CUDA-Q (Graphic: Business Wire)
Showcased via a joint presentation at SC24, the application demonstrates the seamless integration of the core workflow behind several hybrid quantum-classical approaches to calculate the specific properties of a molecule’s electronic structure. These approaches have potential relevance in many chemistry-focused commercial applications, such as identifying how drug molecules might interact with specific proteins in the human body. The achievement captures IonQ’s interest in developing solutions that combine the benefits of quantum processing units (QPUs) with the accelerated, heterogeneous computing capabilities of the NVIDIA CUDA-Q platform.
“IonQ is building on its approach of accelerating AI, scientific compute, and other supercomputing workloads with quantum hardware – enabling entirely new ways to approach previously unsolvable problems,” said Dean Kassmann, SVP of Engineering & Technology at IonQ. “Today’s molecular modeling demonstration illustrates why CUDA-Q is a great platform for achieving seamless integration and top-tier performance with our leading quantum hardware.”
Since 2023, IonQ has supported NVIDIA CUDA-Q, a powerful, unified, open-source software stack. CUDA-Q is a hybrid quantum-classical computing platform that enables the integration and programming of QPUs and GPUs in a single workflow. This demonstration was performed using a combination of IonQ Forte, the IonQ Hybrid Services suite, CUDA-Q, and NVIDIA A100 Tensor Core GPUs, which can be deployed to cloud and on-prem environments.
The demonstration at SC24 showcases a novel way to interrogate the properties of specific molecules in a resource-efficient way that has relevance in larger application workflows pertaining to pharma and other commercial domains. The work also sets the stage for IonQ to accelerate even more powerful ways to model molecular dynamics for future biopharmaceutical applications.
“Useful quantum applications will need to draw on both quantum hardware and AI supercomputing resources,” said Elica Kyoseva, Director of Quantum Algorithm Engineering, NVIDIA. “The CUDA-Q platform is allowing researchers and developers to explore these paradigms by accessing NVIDIA accelerated computing alongside IonQ’s quantum processors.”
Today’s news reaffirms IonQ’s commitment to developing hardware and software that leverages a combination of quantum and classical computation to enable efficient, performant, robust solutions that can be deployed at commercial scale.
IonQ’s Hybrid Services suite makes designing and deploying quantum-accelerated applications that take advantage of IonQ’s leading quantum hardware easier than ever. It achieves this via a flexible, easy-to-configure hybrid infrastructure, workflow management, and scheduling for cloud or on-prem installations. Further, it has powerful tools to help application developers explore new applications and use cases, such as prebuilt solvers for problem classes like quadratic optimization and graph partitioning, and out-of-the-box support for a variety of common programming models, including NVIDIA CUDA-Q quantum kernels.
To learn more about IonQ and its latest system news and business developments, visit https://ionq.com/.
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