cadillacdave
3 시간 전
You seem to have a very one dimensional view point here.Anyone who doesn't march in lockstep with your viewpoints is spreading FUD or short. I am neither.
If you care to read any of my prior posts, I point out much of the upside here, the many patents that are held, leader in the field and government contracts.
However, it has dropped, for whatever reason from $54 - $34, in a very short period of time.
It did rise quickly from the $7 range, but seems to be dropping just as fast. The last week lr so, it has dropped $1.50 -$2 per day. Today it has dropped nearly $3. Those are facts (that you choose to ignore)
Just because I pointed out that this could revisit the $10 range doesn't make me short. If someone predicted 2 weeks ago this would drop $20 you would be name calling them. Yet we are here. You guys seem to be in denial about what has happened here.
Do I think it will bounce back - yes I do. But I'm not sure how low this will go and neither are you, hence the frustration and name calling by you guys.
Grow up and recognize the board is for everyone, not just those who 100% agree with your perspective and are cheerleaders here.
cadillacdave
4 시간 전
Hey buddy, just posting my opinion. You cant dispute the facts - it has dropped from a high of $54 to $34. You may be unhappy with that, as am I, but dont start getting rude because you frustrated and arent happy with the reality here.
I think this will turn around, but just making a point, that the drops have been further each day.
If you want to mute anyone who doesnt align with your perfect view here, go ahead and bury your head in the sand.
Good luck!
doc2016
6 시간 전
memory and generative ai
Memory plays a crucial role in the advancement and capabilities of generative AI. Here's how:
1. Enhancing Accuracy and Relevance:
Learning from the Past: Generative AI models with memory can learn from past experiences, data, and interactions. This allows them to generate more accurate and relevant outputs, as they can consider previous context and information.
Example: In a chatbot application, a generative AI model with memory can remember previous conversations with a user, allowing it to provide more personalized and coherent responses.
2. Improving Scalability and Handling Complex Information:
Storing and Accessing Information: Memory enables generative AI models to store and access vast amounts of information, including text, images, code, and other data types. This allows them to handle more complex tasks and generate more comprehensive outputs.
Example: A generative AI model used for writing assistance can access a large knowledge base stored in its memory to provide more accurate and detailed suggestions for writing.
3. Enabling Effective Utilization of Past Knowledge:
Building upon Previous Experiences: Generative AI models with memory can build upon previous experiences and knowledge, similar to how humans learn and develop over time. This allows them to generate more creative and insightful outputs.
Example: A generative AI model used for drug discovery can use its memory to store and analyze information about previously tested compounds, leading to more efficient and effective drug development.
4. Expanding Applications in Various Domains:
Personalized Experiences: Generative AI with memory can be used to create more personalized experiences in various applications, such as chatbots, virtual assistants, and recommender systems.
Content Creation: It can generate more creative and relevant content, including text, images, music, and even code.
Problem Solving: It can be used to solve complex problems in various domains, such as healthcare, finance, and engineering.
Specific Examples of Memory in Generative AI:
Long-Term Memory Models: These models can retain information over extended periods, allowing them to generate more coherent and contextually relevant outputs.
Vector Databases: These databases store information in a way that allows for efficient similarity search and retrieval, enabling generative AI models to quickly access relevant information from their memory.
Hybrid Learning: Combining different memory mechanisms, such as short-term and long-term memory, can lead to more robust and adaptable generative AI models.
Challenges and Future Directions:
Memory Management: Efficiently managing and accessing memory is crucial for the performance and scalability of generative AI models.
Forgetting: Developing mechanisms for forgetting irrelevant or outdated information is important to prevent memory overload and maintain accuracy.
Explainability: Understanding how generative AI models use their memory to generate outputs is important for transparency and trust.
Memory is a key component in the evolution of generative AI, enabling it to become more accurate, scalable, and versatile.
As research progresses, we can expect even more innovative applications of memory in generative AI, leading to more intelligent and capable systems.
Sources and related content
doc2016
6 시간 전
dark matter connections from this to everything, allowing faster than the speed of light connectivity?
is memristor helpful in bridging past to present/future?
memristor is memory...generative ai with memory of what you were doing adding to the context of the work?
gemini ai:"is memristor a tool to bridge quantum and classical computing?
That's an interesting question! Memristors are a fascinating technology with unique properties, and there's growing interest in their potential role in both classical and quantum computing. While they might not be a direct "bridge" between the two, they could play a significant role in connecting them. Here's a breakdown:
What are Memristors?
Memory Resistors: Memristors are electrical components that "remember" the amount of charge that has passed through them. Their resistance changes based on the history of current flow.
Unique Properties: This memory effect makes them different from traditional resistors, capacitors, and inductors. They have potential applications in memory, logic, and neuromorphic computing.
Memristors and Classical Computing
Next-Generation Memory: Memristors could revolutionize computer memory by offering higher density, faster speeds, and lower power consumption compared to current technologies.
Neuromorphic Computing: Their ability to mimic the behavior of synapses in the brain makes them promising for building neuromorphic computers that can learn and adapt like biological systems.
In-Memory Computing: Memristors can perform computations directly within the memory, potentially leading to significant performance improvements in AI and other data-intensive applications.
Memristors and Quantum Computing
Quantum Memristors: Researchers are exploring the concept of quantum memristors, which would exhibit quantum properties like superposition and entanglement.
Potential Applications:
Building Quantum Neural Networks: Quantum memristors could be used to create quantum neural networks, potentially leading to more powerful and efficient AI.
Connecting Quantum and Classical Systems: They could act as an interface between quantum and classical computers, facilitating the transfer of information between the two.
Improving Quantum Computing: Quantum memristors could enhance the capabilities of quantum computers by providing new ways to store and process quantum information.
Bridging the Gap: How Memristors Could Connect Quantum and Classical
Hybrid Architectures: Memristors could be used to build hybrid computing systems that combine the strengths of both classical and quantum computers.
Quantum-Classical Interface: They could facilitate the transfer of data and control signals between quantum and classical components, enabling efficient communication and coordination.
Simulating Quantum Systems: Memristors could be used to simulate quantum systems, helping researchers understand and develop quantum algorithms and applications.
Challenges and Considerations
Technology Development: Quantum memristors are still in the early stages of development. Overcoming technical challenges related to fabrication, control, and scalability is crucial.
Integration: Integrating memristors with both classical and quantum systems requires careful design and engineering to ensure compatibility and efficient operation.
Understanding Quantum Effects: More research is needed to fully understand the quantum properties of memristors and how they can be leveraged for quantum computing applications.
In Summary
While memristors might not be a direct "bridge" between quantum and classical computing, they have the potential to play a significant role in connecting these two domains. Their unique properties could enable hybrid architectures, facilitate quantum-classical communication, and contribute to the development of both classical and quantum computing technologies. As research progresses, we might see memristors become a key component in the future of computing."
Konaploinks
18 시간 전
Doc. No evidence to date. But. Stay tuned. A lot is happening behind the quantum computer scene. You can bet on that. No way we are hearing about all that is happening. No way. I’d like to see a remake of The Fly.
No, qubits in quantum computers do not harvest data from a parallel universe. The idea that quantum computation involves parallel universes comes from an interpretation of quantum mechanics called the Many-Worlds Interpretation (MWI), but this remains a theoretical framework rather than an experimentally confirmed reality.
How Qubits Actually Work
• Qubits leverage superposition, meaning they can exist in multiple states simultaneously until measured.
• They also exhibit entanglement, where the state of one qubit is dependent on another, even at a distance.
• Quantum computations explore multiple possible states simultaneously, but they do so within the mathematical framework of quantum mechanics rather than drawing information from another reality.
The “Parallel Universe” Misconception
• The Many-Worlds Interpretation (MWI) suggests that all possible outcomes of a quantum measurement actually occur in different branches of reality.
• However, other interpretations (such as the Copenhagen Interpretation) do not require parallel universes and still explain quantum computing’s success.
• No experimental evidence currently supports the idea that quantum computers extract data from other universes.
Where This Idea Comes From
Physicist David Deutsch, a pioneer in quantum computation, has argued that quantum algorithms like Shor’s algorithm (which factors large numbers exponentially faster than classical computers) suggest an underlying “multiverse” structure. However, this is a philosophical stance rather than a proven scientific fact.
Bottom Line
Quantum computers perform calculations based on the principles of quantum mechanics within our universe. While quantum theory remains mysterious, there is no evidence that they interact with or extract data from parallel universes.
Would you like insights into cutting-edge quantum computing theories that push the boundaries of our understanding?
Konaploinks
1 일 전
Re cap.
Insider Brief
IonQ demonstrated an industry-first end-to-end hybrid quantum-classical workflow, integrating its quantum hardware with NVIDIA’s CUDA-Q platform for advanced molecular modeling.
The collaboration showcases a resource-efficient approach to simulating molecular properties with potential applications in drug discovery and biopharmaceuticals.
IonQ’s Hybrid Services suite simplifies deploying quantum-accelerated applications, leveraging NVIDIA’s GPU capabilities to scale quantum-classical computing for commercial use.
PRESS RELEASE — 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.
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.”
Responsive Image
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.
Konaploinks
1 일 전
IonQ and NVIDIA have recently made significant strides in hybrid quantum-classical computing by integrating their technologies to enhance computational capabilities.
Integration of IonQ’s Quantum Hardware with NVIDIA’s CUDA-Q Platform
In a groundbreaking demonstration at SuperCompute 24 (SC24), IonQ showcased the seamless integration of its quantum computing hardware with NVIDIA’s CUDA-Q platform. This collaboration focused on molecular electronic structure calculations, a critical area for applications such as drug discovery and materials science. By combining IonQ’s Forte quantum processor with NVIDIA’s A100 Tensor Core GPUs, the demonstration highlighted the potential of hybrid systems to tackle complex scientific problems more efficiently. ?
Launch of IonQ Hybrid Services Suite
IonQ introduced its Hybrid Services suite, designed to facilitate the development and deployment of quantum-accelerated applications. This suite offers cloud-based services for managing hybrid workflows, including prebuilt solvers for quadratic optimization and graph partitioning. Notably, it supports various programming models, such as NVIDIA’s CUDA-Q, enabling developers to leverage both quantum and classical resources effectively. ?
Advancements in Hybrid Quantum-Classical Workflows
The collaboration between IonQ and NVIDIA emphasizes the growing importance of hybrid quantum-classical workflows. By integrating quantum processors with classical accelerated computing platforms, they aim to create scalable solutions for complex computational challenges. This partnership not only enhances performance but also streamlines the development of quantum applications, making them more accessible for real-world use cases. ?
These advancements underscore the commitment of both companies to push the boundaries of what’s possible in computational science through hybrid quantum-classical systems.
For a visual overview of this collaboration, you might find the following video informative:
Konaploinks
1 일 전
Homework: IonQ has recently expanded its intellectual property portfolio with several new patents, enhancing the performance and scalability of its trapped-ion quantum computing systems. These innovations focus on optimizing quantum gate operations, reducing errors, and improving overall system efficiency.
Recent Patents and Their Contributions:
1. Double Individual-Addressing Multi-Beam Raman System (U.S. Patent No. 12,088,351):
• Issued: September 10, 2024
• Advancement: This patent introduces a system that enables precise control over individual qubits using multiple laser beams. By addressing each ion separately, the system enhances gate operation flexibility and accuracy, leading to more reliable quantum computations. ?
2. Quantum Circuit Optimization (U.S. Patent No. 12,033,031):
• Issued: July 9, 2024
• Advancement: This patent covers an optimization tool that compiles quantum algorithms to run more efficiently on physical hardware. By streamlining quantum circuits, it reduces the computational resources required, thereby improving execution speed and fidelity. ?
3. Methods and Apparatuses for Cross-Talk Mitigation (U.S. Patent No. 12,217,882):
• Filed: July 18, 2022
• Advancement: This patent presents techniques to minimize cross-talk between qubits, a common source of errors in quantum systems. By implementing these methods, IonQ’s systems can achieve higher gate fidelities, essential for complex quantum computations. ?
4. Methods and Apparatuses for Performing Echo Sequence (U.S. Patent No. 12,217,133):
• Filed: September 20, 2022
• Advancement: This patent details techniques for implementing echo sequences that counteract decoherence in qubits. By preserving quantum states longer, these methods enhance the reliability of quantum operations. ?
Impact on Trapped-Ion Systems:
These patents collectively contribute to advancing IonQ’s trapped-ion quantum computing technology by:
• Enhancing Precision: Improved individual qubit addressing and control lead to more accurate quantum gate operations.
• Reducing Errors: Techniques for cross-talk mitigation and decoherence reduction are crucial for maintaining qubit integrity during computations.
• Optimizing Performance: Quantum circuit optimization tools streamline computations, making them more efficient and practical for real-world applications.
By integrating these innovations, IonQ is strengthening its technical foundation, paving the way for more robust and scalable quantum computing solutions.
Konaploinks
1 일 전
Check this out Doc
Memristors, while primarily studied for classical computing and neuromorphic architectures, are beginning to find potential applications in quantum computing, including trapped-ion systems. However, their direct integration into trapped-ion quantum processors is still an emerging area of research. Here’s how memristors could play a role:
1. Classical Control and Optimization in Trapped-Ion Systems
Trapped-ion quantum computers require precise control of laser pulses, microwave fields, and electric potentials to manipulate qubits. Memristors can contribute in the following ways:
• Analog Memory for Control Circuits: Memristors can store and fine-tune control parameters, such as laser intensities and phase shifts, reducing the need for frequent recalibration.
• Low-Power Neuromorphic Control: Adaptive memristor-based circuits could optimize pulse shaping dynamically, compensating for noise in real time.
2. Quantum Error Correction (QEC) and Feedback Loops
Quantum error correction relies on real-time data processing and feedback loops. Memristors could:
• Enable High-Speed, Low-Power Error Detection: Using memristors in classical controllers could speed up QEC operations without introducing excessive heat or noise into the quantum system.
• Facilitate Machine Learning for QEC Optimization: Memristors are ideal for hardware-based AI accelerators that could optimize QEC parameters based on historical error patterns.
3. Hybrid Quantum-Classical Computing
Trapped-ion systems often require classical coprocessors for optimization tasks like variational quantum eigensolvers (VQEs). Memristor-based neuromorphic chips could:
• Accelerate Hybrid Algorithms: By performing real-time optimization of quantum gate sequences.
• Reduce Latency in Quantum-Classical Loops: Speeding up decision-making for quantum teleportation and entanglement purification.
4. Potential Role in Cryogenic Environments
While trapped-ion quantum computers operate at room temperature (unlike superconducting qubits), integrating memristors in cryogenic control electronics for hybrid quantum processors could reduce power dissipation and improve scalability.
Current Research and Challenges
• Integration with Ion-Trap Hardware: There is limited research on directly embedding memristors within ion-trap quantum hardware, but their application in control systems is gaining interest.
• Noise and Stability: Memristors exhibit stochastic behavior, which needs to be carefully managed when used in high-precision quantum systems.
• Scalability: Large-scale trapped-ion processors will require highly efficient classical controllers, where memristors could provide an advantage.
Conclusion
While memristors are not yet a core component of trapped-ion quantum computers, they hold promise in improving control electronics, error correction, and hybrid quantum-classical processing. Their role will likely expand as researchers explore new ways to integrate them with quantum technologies.
Konaploinks
1 일 전
Homework: Recent experiments have significantly advanced quantum teleportation using trapped-ion systems, demonstrating high-fidelity state transfer and entanglement between spatially separated qubits.
Quantum Gate Teleportation Between Separated Qubits
In a notable study, researchers achieved deterministic teleportation of a controlled-NOT (CNOT) gate between two qubits located in distinct regions of an ion trap. This experiment combined ion shuttling, individual qubit addressing, and real-time conditional operations, resulting in an entanglement fidelity between 84.5% and 87.2%. This work showcases essential techniques for scaling trapped-ion quantum computers. ?
Fault-Tolerant Teleportation of a Logical Qubit
Another significant advancement involved the fault-tolerant teleportation of a logical qubit using Quantinuum’s H2 trapped-ion processor. The research demonstrated high-fidelity logical qubit teleportation with real-time error correction, achieving a process fidelity of 97.5% for transversal teleportation circuits. This experiment underscores the potential of trapped-ion systems in realizing robust, large-scale quantum networks. ?
These developments highlight the progress in utilizing trapped-ion platforms for reliable and scalable quantum teleportation, paving the way for advanced quantum computing and communication systems.
Konaploinks
1 일 전
Homework: no mercy on Presidents Day
Optimizing Sampling, Timing, and Energy Wavelengths for Trapped-Ion Qubits in Quantum Teleportation
Trapped-ion qubits are among the most promising platforms for quantum teleportation due to their long coherence times and high-fidelity operations. (Jack nailed it) Below is a breakdown of optimal parameters for qubit sampling, timing, and energy wavelengths in a trapped-ion system.
1. Optimal Qubit Sampling for Trapped Ions
• Error Mitigation: Trapped-ion systems have gate fidelities exceeding 99.9%, allowing for less frequent error correction compared to superconducting qubits. However, optimal sampling should still consider:
• Adaptive Sampling: Adjusting the frequency of qubit state readout based on real-time noise fluctuations.
• Qubit Measurement Rate: Typically, measurement times range from 10–100 microseconds, meaning a sampling rate of at least 10 kHz may be optimal for real-time feedback in teleportation circuits.
• Mid-circuit Measurements: Some systems support mid-circuit readouts, enabling real-time error detection and correction.
2. Timing Considerations for Quantum Teleportation with Trapped Ions
Gate Execution Times:
• Single-qubit gates: 1–10 µs
• Two-qubit gates: 10–200 µs (depending on laser power and entanglement fidelity)
• State measurement: 10–100 µs (depends on photon collection efficiency)
Coherence Time:
• T1 relaxation time: Nearly infinite (ions don’t spontaneously decay like superconducting qubits).
• T2 dephasing time: 1–10 seconds (main limitation is magnetic field fluctuations, which can be suppressed with dynamical decoupling).
• Teleportation Latency: Classical communication delay in quantum teleportation (for transmitting measurement results) should be sub-microsecond level for efficient real-time processing.
3. Optimal Energy Wavelengths for Trapped-Ion Quantum Teleportation
The energy wavelengths used in trapped-ion systems depend on the specific ion species. Common choices include:
Ion Species Laser Cooling Entangling Gates Readout
Yb? (Ytterbium-171) 369.5 nm (cooling) 355 nm (entanglement) 369.5 nm (detection)
Ca? (Calcium-40) 397 nm (cooling) 729 nm (entanglement) 397 nm (detection)
Ba? (Barium-137) 493 nm (cooling) 1762 nm (entanglement) 650 nm (detection)
Entanglement and Teleportation Wavelengths:
• Ultraviolet (UV) Lasers (~355 nm): Used for high-fidelity entangling gates.
• Infrared (IR) Lasers (729 nm or 1762 nm): Used for coherent state manipulation.
• Telecom Wavelengths (~1550 nm): Useful for quantum networking and long-distance teleportation via optical fibers.
Key Considerations for Optimizing Quantum Teleportation with Trapped Ions
1. Synchronizing Ion Motion with Lasers: Ensuring stable ion trapping and minimal motional heating is crucial for high-fidelity teleportation.
2. Reducing Magnetic Field Fluctuations: Using Dynamical Decoupling techniques (e.g., Carr-Purcell-Meiboom-Gill (CPMG) sequences) extends coherence times.
3. Optimizing Ion-Photon Interfaces: Efficient quantum teleportation over fiber networks requires high-fidelity photon-ion entanglement, typically achieved with cavity quantum electrodynamics (CQED) or photon conversion to telecom wavelengths.
Would you like specific implementation details, such as recent teleportation experiments using trapped ions?
Konaploinks
1 일 전
Exactly Jack! And here ya go Doc. I see your brain churning this info a mile a minute. Lol. Homework;
Optimizing the sampling of qubits, timing, and energy wavelengths for quantum teleportation depends on several key factors, including the quantum hardware, the entanglement mechanism, and the coherence time of the qubits. Below are the main considerations:
1. Optimal Qubit Sampling
• Quantum Error Correction (QEC): Sampling qubits at a rate that balances speed and error minimization is crucial. Typically, surface codes suggest an error threshold of around 1%, meaning qubits should be measured frequently enough to detect and correct errors before decoherence sets in.
• Adaptive Sampling: In noisy intermediate-scale quantum (NISQ) devices, adaptive measurement techniques can improve fidelity by dynamically adjusting sampling rates based on real-time noise levels.
2. Timing for Quantum Teleportation
• Synchronization: Quantum teleportation relies on precise synchronization of operations, especially for Bell-state measurements and classical communication of the measurement results.
• Gate Execution Times:
• Superconducting qubits: ~10-100 nanoseconds per gate
• Trapped-ion qubits: ~10 microseconds per gate (slower but higher fidelity)
• Coherence Time Considerations: The sampling interval must be shorter than the T2 decoherence time of the qubits to ensure accurate state transfer:
• Superconducting qubits: ~100 microseconds
• Trapped ions: ~1–10 seconds
• Photonic qubits: Effectively infinite (provided loss is managed)
3. Optimal Energy Wavelengths
• Photon-based Quantum Teleportation:
• 1550 nm (Infrared): Best for long-distance fiber-optic communication due to low loss in silica fiber.
• 810 nm & 890 nm: Common for free-space quantum teleportation experiments.
• Telecom-band entanglement (1310 nm & 1550 nm): Enables compatibility with existing fiber networks.
• Superconducting Qubit Frequencies:
• Typically operate in the 4-8 GHz microwave range.
• Lower frequencies (4-5 GHz) reduce thermal noise but may require larger components.
• Higher frequencies (6-7 GHz) can reduce qubit cross-talk.
Would you like insights on a specific platform, such as superconducting, trapped ions, or photonic qubits?
doc2016
1 일 전
dark matter and a neural net..at least intergalactic, they say.
quantum entanglements might need a virtual pbx for same?
the idea of a virtual pbx keeping track and a registry of quantum entangled and their states would seem to yield information about how best to maintain quantum coherence and uptime/qos? all of that could be put toward the yield of logical qubits from physical qubits being optimized? including the settings/programming of such and their photonic management? for instance what is the optimal sampling of the qubits, timing, energy wavelengths, magnetic fields, temperature, qubit types, silicon dioxide insulation use, etc.
intel previously announce a photonics research place in ? texas. now broadcom and others are said to be interested in the intel photonics r and d division.
cadillacdave
2 일 전
Interesting article on quantum computing.
D-wave sold quantum tech to Lockheed Martin in 2011.
Now they sold the largest quantum computer to a research entity, in Germany.
Apparently, D-Wave does not have size constraints with limiting how many qbits, based on their using different technology, called annealing.
It appears that IONQ, google and others have limitations on how many qbits they can attach, therefore limiting the size of their quantum computers at this time.
https://www.barrons.com/articles/d-wave-quantum-computer-sale-d1f5e2ae
Konaploinks
2 일 전
Scientists achieve world-leading quantum entanglement of molecules
15 January 2025
Media release
Physics
Share page:
Team picture of scientists carrying out this research
Scientists from our top-rated Physics department have set a global milestone by achieving quantum entanglement of individual molecules using cutting-edge magic-wavelength optical tweezers.
This achievement not only overcomes a fundamental challenge in quantum science but also opens up new possibilities in quantum computing, high-precision measurements, and physics research.
The researchers managed to create long-lasting entanglement between pairs of ultracold molecules, placing us among the elite few institutions worldwide capable of such work.
Unlocking new quantum capabilities
The innovation hinges on the magic-wavelength optical tweezers that allow the researchers to prepare molecules in a superposition of rotational states with exceptional stability.
The tweezers use light at a specific wavelength that creates identical energy shifts in two rotational states of a molecule, preserving coherence in the entangled state for a much longer time.
As a result, the research team achieved entanglement with record-high reliability, detecting molecular interactions previously too weak to observe.
Only three other research teams worldwide, at Harvard and Princeton, have achieved similar entanglement, underscoring the significance of our accomplishment.
New opportunities
Beyond immediate advances in quantum computing and quantum memory, the research unlocks a new level of sensitivity for exploring fundamental physics.
The stability of the magic-wavelength traps makes it possible to conduct ultra-precise measurements of molecular energy shifts, an ability that is valuable for probing beyond the Standard Model of physics.
Our pioneering research has the potential to contribute to national quantum technology initiatives, reinforcing the UK’s standing in the global quantum field and highlighting Durham’s role in leading scientific progress.
Researchers hope this achievement will attract further investment in Durham’s quantum science programme, positioning it as a vital contributor to the National Quantum Technology Programme. Ionq. Get in!
doc2016
2 일 전
kona, is it possible:
quantum computing /qubits are harvesting/sampling events from the future/past /present or something in between. i guess our current reality is somewhere in between.
instead of just learning from history , we now embark potentially on learning from the future of some metaverse/reality/entangled completely or to a lesser degree?
electrons of charged atoms maybe less shared than neutral atoms? do they have a more restricted em field of 'being'?
if so, charged ions might be more pertinent to 'us' as opposed to some more universal us(objects) neutral atoms?
if we use molecules, more mass and specifics to entangle and greater entanglement /granular tuning?
the more we sample from very granular tuning settings, the more specifically relevant to us or the model ?
quantum/supercomputing models used for humans/beings most like us and our biology/physics might be most relevant to/for us?
'granularity' , i think we've heard of before in discussions of intellectual property?
i just saw an article of data teleportation made better by light frequency/pulse rate..? more specificity?
IanFromSI
3 일 전
For those of us, especially myself included, who realize that we do not know everything about everything, AI is a very current source for getting the most current information.
Worst case, it’s a superior tool to Google, and one doesn’t have to go through all of the sponsored items to get to the desired info.
But congratulations to you. You may be the only person in the world who knows everything about everything including what’s been published today.
Hot Features
프리미엄
무료로 등록하여 실시간 주식 시세, 대화형 차트, 실시간 옵션 플로우 등을 받아보세요.
JJ8
17 분 전
Mr. Hathaway
4 시간 전
Jack Torrance
4 시간 전
