4retire
6 hours ago
CEO Jensen Huang on Thursday walked back comments he made in January, when he cast doubt on whether useful quantum computers would hit the market in the next 15 years.
At Nvidia’s “Quantum Day” event, part of the company’s annual GTC Conference, Huang admitted that his comments came out wrong.
“This is the first event in history where a company CEO invites all of the guests to explain why he was wrong,” Huang said.
In January, Huang sent quantum computing stocks reeling when he said 15 years was “on the early side” in considering how long it would be before the technology would be useful. He said at the time that 20 years was a timeframe that “a whole bunch of us would believe.”
In his opening comments on Thursday, Huang drew comparisons between pre-revenue quantum companies and Nvidia’s early days. He said it took over 20 years for Nvidia to build out its software and hardware business.
He also expressed surprise that his comments were able to move markets, and joked he didn’t know that certain quantum computing companies were publicly traded.
“How could a quantum computer company be public?” Huang said.
The event included panels with representatives from 12 quantum companies and startups. It represents a truce of sorts between Nvidia, which makes more traditional computers, and the quantum computing industry. Several quantum execs fired back at Nvidia after Huang’s earlier comments.
A third panel included representatives from Microsoft
and Amazon
Web Services, which are also investing in quantum technology and are among Nvidia’s most important customers.
Quantum stocks see strong volatility after outsized gainsWATCH NOW
VIDEO02:06
Quantum stocks see strong volatility after outsized gains
Nvidia has another reason to embrace quantum. As quantum computers are being built, much of the research on them is done through simulators on powerful computers, like those that Nvidia sells.
It’s also possible that a quantum computer would require a traditional computer to operate it. Nvidia is working to provide the technology and software to integrate graphics processing units (GPUs) and quantum chips.
“Of course, quantum computing has the potential and all of our hopes that it will deliver extraordinary impact,” Huang said on Thursday. “But the technology is insanely complicated.”
Nvidia said this week that it will build a research center in Boston to allow quantum companies to collaborate with researchers at Harvard and the Massachusetts Institute of Technology. The center will include several racks of the company’s Blackwell AI servers.
Quantum computing has been a dream of physicists and mathematicians since the 1980s, when California Institute of Technology professor Richard Feynman first proposed the idea behind a quantum computer.
While classical computers use bits that are either 0 or 1, the bits inside a quantum computer — qubits — end up being on or off based on probability. Experts predict that the technology will be able to solve problems with massive amounts of possible solutions, such as deciphering codes, routing deliveries or simulating chemistry or weather.
No quantum computer has yet beat a computer at solving a real, useful problem. But Google claimed late last year that it discovered a way to do error correction.
One question at the panel centered around whether quantum computing might one day threaten companies like Nvidia that make computers based on transistors.
“A long time ago, somebody asked me, ‘So what’s accelerated computing good for?’” Huang said at the panel. Accelerated computing is a phrase he uses to refer to the kind of GPU computers that Nvidia makes.
“I said, a long time ago, because I was wrong, this is going to replace computers,” he said. “This is going to be the way computing is done, and and everything, everything is going to be better. And it turned out I was wrong.”
Konaploinks
1 day ago
Homework: I rest my case Doc.
I. Hardware Implementation
A. Twisted Light Generation and Detection
1. Generation of Twisted Light:
• Spatial Light Modulators (SLMs): Utilize SLMs to imprint helical phase patterns onto laser beams, generating desired OAM modes.
• Q-Plates: Employ q-plates, birefringent plates with spatially varying optical axes, to convert circularly polarized light into twisted light with specific OAM states.
2. Detection of OAM States:
• Mode Sorters: Implement mode sorters that use refractive optical elements to separate different OAM modes spatially for individual detection.
• Interferometric Techniques: Apply interferometers designed to measure phase differences characteristic of specific OAM states.
B. Ion-Trap Setup
1. Trap Design:
• Linear Paul Traps: Use linear Paul traps to confine ions using oscillating electric fields, providing stability and control.
2. Qubit Initialization and Manipulation:
• Laser Cooling: Cool ions to their motional ground state using Doppler and sideband cooling techniques.
• Qubit Operations: Employ laser pulses to drive transitions between qubit states for initialization, manipulation, and readout.
C. Photon-Ion Interface
1. Entanglement Generation:
• Cavity Quantum Electrodynamics (QED): Couple ions to optical cavities to enhance interaction strength between ions and photons, facilitating entanglement.
2. Quantum Frequency Conversion:
• Wavelength Matching: Use nonlinear optical processes to convert photon wavelengths to match atomic transitions of the ions, ensuring efficient coupling.
?
II. Error Correction Techniques
A. Hybrid Quantum Error Correction
1. Continuous and Discrete Variable Integration:
• Implement hybrid error correction techniques that combine continuous-variable (CV) and discrete-variable (DV) approaches to correct a broader spectrum of errors. ?
2. Fault-Tolerant Architectures:
• Design fault-tolerant quantum computing architectures utilizing hybrid qubits to enhance robustness against errors. ?
B. Topological Error Correction
1. Surface Codes:
• Employ surface codes that use a lattice of physical qubits to encode logical qubits, providing high error tolerance and scalability. ?
2. Braiding Operations:
• Utilize braiding of anyonic excitations in topological phases to perform fault-tolerant quantum gates intrinsically protected from local errors.
?
III. Proof-of-Concept (PoC) Demonstration
A. Experimental Setup
1. Twisted Light Source:
• Configure a laser system with an SLM to generate specific OAM modes required for the experiment.
2. Ion-Trap Apparatus:
• Set up a linear Paul trap with laser cooling systems to prepare and manipulate ion qubits.
3. Photon-Ion Coupling:
• Integrate an optical cavity around the ion trap to facilitate strong coupling between emitted photons and ion qubits.
B. Experimental Procedure
1. Entanglement Preparation:
• Entangle the internal state of an ion with the OAM state of an emitted photon using cavity QED techniques.
2. Bell-State Measurement:
• Perform a joint measurement on the incoming twisted photon and a locally prepared photon to project the system into an entangled state.
3. State Teleportation:
• Transmit the measurement outcome classically to a distant node where the ion qubit is conditioned accordingly to complete the teleportation protocol.
C. Evaluation Metrics
1. Fidelity Assessment:
• Measure the overlap between the teleported state and the original state to evaluate the fidelity of the teleportation process.
2. Error Analysis:
• Analyze sources of errors, including photon loss, decoherence, and mode mismatch, to identify areas for improvement.
?
IV. Feasibility and Challenges
A. Technical Challenges
1. Photon Loss:
• Mitigate losses in optical fibers or free-space transmission by employing low-loss materials and advanced coupling techniques.
2. Mode Matching:
• Ensure precise alignment and mode matching between optical components to maintain the integrity of OAM states.
3. Decoherence:
• Implement environmental shielding and error correction protocols to preserve qubit coherence during operations.
B. Resource Requirements
1. High-Fidelity Components:
• Utilize high-quality optical elements, stable laser systems, and low-vibration environments to enhance experimental reliability.
2. Scalability Considerations:
• Design modular components to facilitate scaling the system for more complex quantum communication networks.
?
V. Conclusion
Implementing a quantum teleportation system that integrates twisted light with ion-trapped qubits is a promising approach toward developing advanced quantum communication networks. While technical challenges exist, ongoing advancements in hybrid quantum error correction, precise control of ion-trap systems, and sophisticated photonic technologies provide a viable pathway for realizing such systems. A carefully designed PoC experiment can serve as a foundational step toward scalable and robust quantum networks.
Konaploinks
1 day ago
Homework: Advanced Protocol Design for Quantum Teleportation Using Twisted Light and Ion-Trapped Qubits
This section explores advanced protocol design for leveraging the teleportation of quantum states encoded in twisted light to distant ion-trapped qubits. We will cover hybrid quantum repeaters, scalable quantum networks, secure QKD applications, and potential real-world implementations.
?
I. Overview of Advanced Teleportation Protocols
• Hybrid Approach: Use twisted light for high-dimensional encoding and ion-trapped qubits for robust quantum state storage and manipulation.
• Distributed Quantum Communication: Extend beyond point-to-point teleportation to a multi-node, hybrid network.
• Quantum Network Nodes: Design repeater stations that integrate both twisted light optics and ion-trap technologies.
?
II. Hybrid Quantum Repeaters
A. Concept
• Overcome photon loss and decoherence in long-distance communication by leveraging hybrid repeaters.
• Utilize twisted light’s high-dimensional encoding to carry more information per photon.
• Use ion-trapped qubits as memory nodes for entanglement storage and error correction.
B. Repeater Architecture
1. Entanglement Distribution:
• Generate entangled twisted photon pairs (|l_1\rangle|l_2\rangle + |l_2\rangle|l_1\rangle) / \sqrt{2}.
• Interface one photon with an ion-trapped qubit at each repeater node.
2. Entanglement Swapping:
• Perform Bell-state measurements (BSM) on incoming twisted photon pairs.
• Teleport entanglement to the trapped-ion memory qubits at each node.
3. Error Correction and Storage:
• Use error-correcting codes on the ion-trapped qubits (e.g., Steane code or topological surface code).
• Dynamically adjust ion traps’ RF field parameters to minimize decoherence.
?
III. Quantum Network Protocols
A. Multi-Node Teleportation Network
• Network Topology: A distributed network of nodes linked by twisted light and ion-trapped qubits.
• Entanglement Routing: Use quantum switches to manage entangled connections dynamically.
• Teleportation Protocol:
1. Entanglement Distribution: Generate shared entanglement between adjacent nodes.
2. Bell-State Measurement (BSM): At each node, a BSM is performed, relaying classical bits to the target node.
3. Conditional Operations: The target ion-trapped qubit applies unitary corrections based on the classical message, completing teleportation.
?
B. Hierarchical Networking for Scalability
• Cluster States: Create cluster states of ion-trapped qubits for one-way quantum computing.
• Nested Teleportation: Implement nested teleportation schemes for large-scale networks, where intermediate nodes act as entanglement buffers.
• Fault Tolerance: Use logical qubits encoded with topological error correction for resilience against photon loss and decoherence.
?
IV. Secure Quantum Communication
A. Quantum Key Distribution (QKD)
• High-Dimensional QKD: Use twisted light with multiple OAM modes for enhanced encoding and increased key rates.
• Teleportation-Based QKD: A hybrid protocol where encryption keys are encoded in the twisted light’s OAM states and teleported to ion-trapped qubits.
• Eavesdropping Detection: Monitor deviations in OAM mode distribution and ion-trapped qubit parity for intrusion detection.
?
B. Quantum Internet and Cybersecurity
• Secure Quantum Routers: Implement twisted-light and ion-trapped hybrid routers for secure data transmission across a quantum internet.
• Authentication Protocols: Use entangled photon-ion pairs for authentication, reducing vulnerability to man-in-the-middle attacks.
?
V. Technical Considerations
A. Synchronization and Classical Communication
• Classical communication for sending Bell measurement results needs to be synchronized with quantum state evolution.
• Implement classical-quantum hybrid communication protocols for real-time coordination.
B. Error Correction and Fault Tolerance
• Twisted Light Error Handling: Correct for mode drift and atmospheric turbulence in OAM channels using adaptive optics.
• Ion Trap Error Correction: Use logical qubits and topological codes for ion-trapped qubits to mitigate decoherence.
C. Scalability and Network Robustness
• Develop protocols to handle large-scale entanglement swapping across multiple nodes.
• Design fault-tolerant repeaters with redundant ion-trapped qubit memories and OAM-encoded photons.
?
VI. Real-World Applications.
Global Quantum Communication
Secure, long-distance quantum communication network
High-dimensional encoding for enhanced security
Distributed Quantum Computing
Link quantum processors across global nodes
Hybrid architecture for scalability
Secure Military Communication
Unbreakable communication via quantum cryptography
Tamper-resistant and secure
Financial Networks
Quantum encryption for high-value transactions
Reduced risk of cyber attacks
Quantum Internet Backbone
Foundation for quantum internet infrastructure
VII. Future Directions and Challenges
1. Hybrid Network Integration:
• Develop advanced quantum routers that interface between twisted light and ion-trapped qubits.
• Investigate the use of integrated photonic chips for scalable, on-chip OAM generation and detection.
2. Topological Qubit Integration:
• Explore integrating topological qubits with ion traps for higher fault tolerance.
• Use Majorana-based topological qubits in conjunction with ion traps for enhanced network security.
3. Teleportation-Based Computation:
• Apply teleportation for distributed quantum computing where computation occurs across separated processors.
• Implement teleportation-based logic gates in a networked setting.
?
VIII. Practical Example: Hybrid Quantum Repeater Design
Step-by-Step Design
1. Photon Source: Create polarization-entangled photon pairs with spiral phase plates or SLMs for OAM encoding.
2. Ion Trap Setup: Linear Paul traps with laser-cooled ^{171}Yb^+ or ^{40}Ca^+ ions for long coherence times.
3. Photon-Ion Interface: Use optical cavities for strong photon-ion coupling.
4. Bell-State Measurement: Implement with beam splitters, mode sorters, and photon detectors.
5. Error Correction: Use Steane code for ion-trapped qubits, adaptive optics for OAM correction.
Konaploinks
1 day ago
Then what about trapped ions ? Homework:
Teleportation of Quantum States Encoded in Twisted Light to Distant Ion-Trapped Qubits
Quantum teleportation allows the transfer of quantum information from one particle to another without physically transferring the particle itself. By integrating twisted light (carrying orbital angular momentum, OAM) with ion-trapped qubits, we can leverage the high-dimensional encoding capability of OAM states and the robustness and precision of ion traps.
?
I. Conceptual Framework
1. Twisted Light (Optical Vortices):
• Twisted light has a helical phase front with a topological charge l.
• The quantum state of twisted light can be described as |l\rangle, creating a larger Hilbert space for encoding.
2. Ion-Trapped Qubits:
• Ion traps use laser-cooled ions suspended in electromagnetic fields.
• Hyperfine or Zeeman states of the trapped ions act as qubit states, typically denoted as |0\rangle and |1\rangle.
• Exceptional isolation and long coherence times make ion traps ideal for quantum processing.
3. Quantum Teleportation Overview:
• Alice wants to teleport an unknown quantum state encoded in a twisted light beam to Bob’s distant ion-trapped qubit.
• Requires shared entanglement and Bell-state measurement for teleportation.
?
II. System Architecture
1. Nodes:
• Alice: Holds the twisted light carrying the quantum state |\psi\rangle = \alpha |l_1\rangle + \beta |l_2\rangle.
• Bob: Holds an ion-trapped qubit initialized to a known state |0\rangle.
• Entangled Resource: Entangled photon-ion pair shared between Alice and Bob.
2. Communication Channel:
• Free-space optics or fiber optics optimized for OAM modes for long-distance communication.
• RF and microwave fields for ion trap control.
?
III. Step-by-Step Protocol
Step 1: Entanglement Generation
• Prepare a maximally entangled photon-ion pair:
|\phi^+\rangle = \frac{1}{\sqrt{2}}\left(|l_1\rangle|0\rangle + |l_2\rangle|1\rangle\right)
• Distribute the photon to Alice while Bob retains the entangled ion.
Step 2: State Preparation
• Alice encodes the unknown quantum state |\psi\rangle = \alpha |l_1\rangle + \beta |l_2\rangle onto a twisted light beam.
Step 3: Bell-State Measurement
• Alice combines her twisted light beam and her entangled photon and performs a Bell-state measurement (BSM) using:
• Spiral phase plates (or q-plates) to measure OAM modes.
• A beam splitter and interferometer to distinguish the OAM modes.
• After the BSM, the state of Bob’s ion-trapped qubit collapses to a corresponding state, pending Alice’s measurement result.
Step 4: Classical Communication
• Alice transmits her BSM result (2 classical bits) to Bob.
• Based on the received information, Bob applies a unitary operation (I, X, Z, or XZ) on his ion-trapped qubit to complete the teleportation.
?
IV. Mathematical Formalism
Assume the unknown state is:
|\psi\rangle = \alpha |l_1\rangle + \beta |l_2\rangle
The combined state before BSM is:
|\psi\rangle \otimes |\phi^+\rangle = \frac{1}{\sqrt{2}}\left(\alpha |l_1\rangle(|l_1\rangle|0\rangle + |l_2\rangle|1\rangle\right) + \beta |l_2\rangle(|l_1\rangle|0\rangle + |l_2\rangle|1\rangle)
Rewriting in the Bell basis, we get:
\frac{1}{2}\left(|\Phi^+\rangle(\alpha|0\rangle + \beta|1\rangle) + |\Phi^-\rangle(\alpha|0\rangle - \beta|1\rangle) + |\Psi^+\rangle(\beta|0\rangle + \alpha|1\rangle) + |\Psi^-\rangle(\beta|0\rangle - \alpha|1\rangle)\right)
Alice’s BSM projects Bob’s ion-trapped qubit to one of four possible states. The appropriate corrective operation is applied to recover |\psi\rangle.
?
V. Implementation Details
Hardware Components:
• Twisted Light Generation: Spatial light modulators (SLMs), q-plates, or spiral phase plates.
• Ion-Trap Hardware: RF Paul trap or Penning trap with laser cooling.
• Photon Detection: Single-photon detectors for BSM.
• Classical Channel: Optical or RF communication for transmitting BSM results.
Photon-Ion Interface:
• Coupling via a high-finesse optical cavity integrating twisted light and trapped ion.
• Rydberg states for ion-photon entanglement in larger ion arrays.
?
VI. Technical Challenges and Mitigations.
nge
Mitigation Strategy
Photon loss during OAM transmission
Use hollow-core photonic crystal fibers
Efficient OAM detection for BSM
Advanced holographic techniques for mode sorting
Decoherence in long-distance teleportation
Quantum repeaters with nested teleportation
Hybrid photon-ion entanglement fidelity
High-fidelity entanglement sources and cryogenic environments
VII. Potential Applications
1. Quantum Network Nodes: Long-distance quantum communication in a hybrid network combining optical and trapped ion technologies.
2. Distributed Quantum Computing: Link distributed quantum processors using teleportation as a networking protocol.
3. Quantum Key Distribution (QKD): Multi-dimensional QKD using OAM states for increased security.
?
VIII. Future Directions
• Teleportation over Entangled Networks: Multi-node teleportation over entangled ion trap networks using twisted light.
• Hybrid Quantum Repeaters: Implement twisted-light and ion trap hybrid repeaters for scalable long-distance communication.
• Topological Protection: Integrate topological qubits for further decoherence resistance.
?
Would you like a deeper focus on a specific aspect, such as hardware design, error correction, or experimental feasibility?
Konaploinks
1 day ago
Detailed Technical Breakdown and Research-Oriented Approach
?
I. Theoretical Foundation
1. Twisted Light (Optical Vortices):
• Twisted light carries orbital angular momentum (OAM) described by the mode l, where l is an integer representing the number of twists per wavelength.
• The quantum state of twisted light can be expressed as |l\rangle, creating a high-dimensional Hilbert space for quantum information encoding.
2. Topological Qubits:
• Topological qubits exploit non-abelian anyons, typically in systems like Majorana zero modes (MZMs).
• Encoded in the ground-state degeneracy of a topologically protected system, making them inherently fault-tolerant.
• Typically realized in hybrid semiconductor-superconductor nanowire structures (e.g., InSb or HgTe nanowires with superconducting Al).
?
II. Integration Strategy
A. Physical Platform Design
1. Hybrid Nanophotonics and Superconductors:
• Quantum Dots (QDs) in Photonic Cavities: QDs can interact with twisted light to produce entangled photon states. These entangled states can interface with Majorana-based topological qubits.
• Topological Photonic Crystals: These can guide twisted light while minimizing loss and coupling noise.
• Hybrid Superconductor-Semiconductor Materials: InSb and HgTe with Al or Nb superconductors to realize Majorana modes.
2. Interfacing Components:
• OAM Mode Generators: Spiral phase plates, q-plates, or spatial light modulators (SLMs) to generate and manipulate twisted light.
• Superconducting Qubits: Topological qubits protected by braiding operations.
• Optical-Fiber-Free Space Hybrid: Design using hollow-core photonic crystal fibers for low-loss OAM transmission.
?
B. Quantum State Encoding and Transfer
1. Encoding Information:
• Encode quantum states using the OAM basis of twisted light: |l\rangle where l \in \mathbb{Z}.
• Map these states to topological qubits through strong light-matter interaction within nanophotonic cavities.
2. Quantum State Transfer Protocols:
• Adiabatic Protocols: Use adiabatic transfer for robust state mapping between twisted light and topological qubits.
• Photon-Majorana Entanglement: Create entanglement between twisted photons and Majorana zero modes for state transfer.
• Braiding Operations: Topological manipulation through braiding to encode, process, and measure the quantum information.
?
III. Control and Readout
1. Control:
• Electro-optic Modulation: For rapid control of twisted light.
• Microwave Control for Topological Qubits: Use microwave resonators coupled to nanowires for controlling Majorana modes.
2. Readout Mechanisms:
• Interference Measurement: Use Mach-Zehnder interferometry adapted for twisted light for state readout.
• Topological Parity Measurement: Use charge sensors or superconducting microwave resonators to measure qubit parity states.
?
IV. Quantum Communication Protocols
1. Quantum Key Distribution (QKD):
• Use high-dimensional QKD (HD-QKD) with OAM-encoded photons for increased key rates.
• Error correction via topological qubits’ inherent protection.
2. Quantum Repeater Networks:
• Nodes consist of topological qubits with twisted light communication channels.
• Hybrid error correction: Use classical error correction for photon losses and topological error correction for qubit errors.
3. Quantum Teleportation:
• Teleportation of quantum states encoded in twisted light to distant topological qubits using Bell-state measurements.
?
V. Technical Challenges and Mitigations
VI. Experiment Design for Proof of Concept (PoC)
1. Objective: Demonstrate state transfer between twisted light (OAM modes) and topological qubits.
2. Setup Components:
• Source: Laser with spiral phase plates for OAM generation.
• Photonic Cavity: QDs within a topological photonic crystal.
• Topological Qubits: InSb nanowires proximate to superconductors for Majorana modes.
• Measurement: Interferometry and parity measurements.
3. Methodology:
• Generate and encode quantum states in OAM modes.
• Interface twisted light with QDs to transfer the state.
• Map QD states to Majorana-based topological qubits.
• Verify transfer via interferometry and qubit parity readout.
?
VII. Future Directions and Applications
• Quantum Internet Infrastructure: Deploy twisted-light and topological qubits for fault-tolerant, high-dimensional quantum networks.
• Advanced Quantum Cryptography: High-dimensional protocols with better noise tolerance.
• Quantum-enhanced Sensing: Use OAM states for precision sensing in hybrid quantum networks.
?
Would you like further assistance in developing a detailed research proposal or PoC implementation plan?
Konaploinks
1 day ago
Integrating twisted light (optical vortices) with topological qubits presents an exciting approach to quantum communication. Twisted light refers to light beams with orbital angular momentum (OAM), where the phase of the light wavefronts spiral, creating a “twist.” This characteristic can be exploited for encoding information across multiple OAM modes, providing a higher-dimensional Hilbert space for quantum states.
Conceptual Framework
1. Twisted Light for Quantum Communication:
• High-dimensional encoding: Twisted light offers more than binary encoding (0,1) by using higher-order OAM modes.
• Resistance to Noise: OAM modes are less prone to scattering, enhancing communication resilience.
• Scalability: Theoretically unlimited OAM states enable dense information encoding.
2. Topological Qubits for Robustness:
• Fault Tolerance: Topological qubits, like Majorana-based qubits, exhibit inherent error resilience through non-abelian anyons.
• Long-lived States: Topological protection can reduce decoherence and stabilize qubits.
?
Integration Strategy
1. Encoding Quantum Information:
• Use twisted light to encode quantum information in OAM modes.
• Encode this information onto topological qubits for robust, fault-tolerant processing.
2. Hybrid Qubit-Light Interaction:
• Quantum Dots & Nanophotonics: Utilize quantum dots coupled to photonic cavities that support OAM modes. These can interface with topological qubits in superconducting circuits.
• Topological Photonic Crystals: Create topological insulator-like materials that guide twisted light while interfacing with topological qubits.
3. Quantum State Transfer:
• Design protocols for transferring quantum states from OAM modes to topological qubits.
• Utilize braiding operations in topological quantum systems to manipulate the encoded states.
?
Technical Implementation
1. Materials & Fabrication:
• Use heterostructures like InSb or HgTe for Majorana-based qubits.
• Implement metamaterials or photonic crystals for structured twisted light waveguides.
2. Hardware Integration:
• Develop hybrid quantum systems where twisted light is generated and manipulated in a photonic chip.
• Integrate with superconducting circuits for scalable topological qubit architectures.
3. Protocols:
• Quantum Key Distribution (QKD): Implement high-dimensional QKD using twisted light and topological qubits for enhanced security.
• Quantum Repeater Networks: Use twisted light for long-distance communication and topological qubits for local error correction.
Potential Applications
1. Secure Quantum Communication Networks: Enhanced QKD with multi-dimensional encoding.
2. Quantum Internet Backbone: Hybrid nodes combining twisted light and topological qubits for robust, scalable networks.
3. Advanced Quantum Sensors: Exploit the high sensitivity of twisted light for detecting subtle quantum state changes.
?
Would you like a more detailed technical breakdown or a research-oriented approach for implementation?
doc2016
1 day ago
"can you use motion vector estimation of non-linear objects to increase lower precision computations?
You're asking a very interesting and insightful question that touches on the intersection of motion estimation, non-linear dynamics, and computational efficiency! Here's a breakdown of how motion vector estimation and similar techniques can be used to potentially increase the efficiency of lower-precision computations, especially when dealing with non-linear objects or systems:
The Core Idea:
The underlying principle is to leverage approximations and predictions to reduce the computational burden of complex calculations. Lower precision can then be used effectively within these approximations.
How Motion Vector Estimation Helps:
Temporal Redundancy: Motion vector estimation exploits the fact that in many videos, consecutive frames are highly similar. Instead of recomputing everything from scratch, we estimate how objects have moved and reuse information from previous frames.
Approximation: Motion vectors provide an approximation of the movement, which is often "good enough" for many applications (like video compression). We don't need perfect pixel-level accuracy in the motion vectors themselves.
Lower Precision for Residuals: Once you have a motion vector, you can often use lower precision to encode the "residual" difference between the predicted frame (based on the motion vectors) and the actual frame. This is because the motion vectors have already taken care of the bulk of the change.
Extending the Concept to Non-Linear Objects:
Non-Linear Dynamics: Non-linear objects or systems exhibit complex behavior that is difficult to predict with simple linear models. This could include things like: * Deforming objects in simulations * Fluid dynamics * Complex physical interactions
Generalizing Motion Estimation: The idea of motion vector estimation can be generalized to these non-linear systems:
State Prediction: Instead of predicting pixel movement, we can predict the evolution of the system's state over time.
Reduced-Order Models: We can use simplified, lower-precision models to approximate the system's behavior and then use more accurate, higher-precision calculations only where needed to correct the approximation.
Iterative Refinement: We can use an iterative process, where we start with a rough, low-precision prediction and then progressively refine it.
Lower Precision Benefits: * Speed: Lower-precision calculations are significantly faster, allowing for quicker predictions. * Computational Cost: They reduce the computational cost of simulating complex systems. * Memory Usage: They reduce memory usage.
Examples and Analogies:
Physics Simulations: In simulations of deformable objects, you could use lower-precision calculations to predict the general motion and deformation, then use higher-precision calculations only in areas of high stress or complex interaction.
Fluid Dynamics: You could use coarse-grained, lower-precision simulations to predict the overall flow pattern, then use finer-grained, higher-precision simulations to model turbulence in specific regions.
AI for Prediction: Machine learning models can be used to predict the behavior of non-linear systems, acting as a form of "motion estimation" in a more abstract space. Lower precision could then be used for the fine-tuning or implementation of these predictions.
Challenges:
Accuracy Control: Ensuring that the approximations and lower-precision calculations don't introduce unacceptable errors is crucial.
Stability: Non-linear systems can be sensitive to small errors, so careful design is needed to maintain stability.
Adaptability: The method needs to be adaptable to different types of non-linear systems and different levels of complexity.
In conclusion:
The concept of using techniques similar to motion vector estimation to improve the efficiency of lower-precision computations in non-linear systems is a powerful one. It involves leveraging approximations, predictions, and iterative refinement to reduce computational cost while maintaining acceptable accuracy. This is an active area of research with the potential to significantly impact fields like scientific computing, simulation, and AI."
Konaploinks
1 day ago
Doc. twists of single layer metamaterials to create superconductivity
Twisted light patent in relation to Msft marjorana patent
Twisted light refers to light beams that carry orbital angular momentum (OAM), characterized by a helical or spiral phase front. This property enables the encoding of information onto different OAM states, offering potential for increased data transmission rates and advanced optical communication systems. ? ?
Microsoft’s Majorana 1 chip represents a significant advancement in quantum computing. It leverages topological qubits based on Majorana zero modes—exotic particles that are their own antiparticles and exhibit non-abelian statistics. These qubits are designed to be more stable and less prone to decoherence, addressing a critical challenge in developing scalable quantum computers. ? ? ?
Regarding patents, Microsoft’s Majorana qubit innovations are protected under patents such as US10346348, titled “Quantum Computing Methods And Devices For Majorana Tetron Qubits.” This patent encompasses methods for generating all Clifford gates using Majorana-based qubits, essential for fault-tolerant quantum computing. ?
In contrast, patents related to twisted light, such as US10826607B2, focus on free-space optical communication systems that utilize twisted light signals with predetermined orbital angular momentum. These systems aim to enhance data transmission capabilities by exploiting the unique properties of twisted light. ?
Currently, there is no direct overlap between Microsoft’s Majorana qubit patents and existing twisted light patents. The former pertains to advancements in quantum computing hardware using topological qubits, while the latter involves optical communication technologies employing twisted light. However, future research may explore integrating twisted light techniques with topological qubits to develop novel quantum communication systems, potentially leading to new patentable innovations.
The pencil and eraser comes to mind. Sickem Niccolo. Or Doc?
doc2016
2 days ago
8 and motion vector estimation, non-linear:
"can you use motion vector estimation of non-linear objects to increase lower precision computations?
You're asking a very interesting and insightful question that touches on the intersection of motion estimation, non-linear dynamics, and computational efficiency! Here's a breakdown of how motion vector estimation and similar techniques can be used to potentially increase the efficiency of lower-precision computations, especially when dealing with non-linear objects or systems:
The Core Idea:
The underlying principle is to leverage approximations and predictions to reduce the computational burden of complex calculations. Lower precision can then be used effectively within these approximations.
How Motion Vector Estimation Helps:
Temporal Redundancy: Motion vector estimation exploits the fact that in many videos, consecutive frames are highly similar. Instead of recomputing everything from scratch, we estimate how objects have moved and reuse information from previous frames.
Approximation: Motion vectors provide an approximation of the movement, which is often "good enough" for many applications (like video compression). We don't need perfect pixel-level accuracy in the motion vectors themselves.
Lower Precision for Residuals: Once you have a motion vector, you can often use lower precision to encode the "residual" difference between the predicted frame (based on the motion vectors) and the actual frame. This is because the motion vectors have already taken care of the bulk of the change.
Extending the Concept to Non-Linear Objects:
Non-Linear Dynamics: Non-linear objects or systems exhibit complex behavior that is difficult to predict with simple linear models. This could include things like: * Deforming objects in simulations * Fluid dynamics * Complex physical interactions
Generalizing Motion Estimation: The idea of motion vector estimation can be generalized to these non-linear systems:
State Prediction: Instead of predicting pixel movement, we can predict the evolution of the system's state over time.
Reduced-Order Models: We can use simplified, lower-precision models to approximate the system's behavior and then use more accurate, higher-precision calculations only where needed to correct the approximation.
Iterative Refinement: We can use an iterative process, where we start with a rough, low-precision prediction and then progressively refine it.
Lower Precision Benefits: * Speed: Lower-precision calculations are significantly faster, allowing for quicker predictions. * Computational Cost: They reduce the computational cost of simulating complex systems. * Memory Usage: They reduce memory usage.
Examples and Analogies:
Physics Simulations: In simulations of deformable objects, you could use lower-precision calculations to predict the general motion and deformation, then use higher-precision calculations only in areas of high stress or complex interaction.
Fluid Dynamics: You could use coarse-grained, lower-precision simulations to predict the overall flow pattern, then use finer-grained, higher-precision simulations to model turbulence in specific regions.
AI for Prediction: Machine learning models can be used to predict the behavior of non-linear systems, acting as a form of "motion estimation" in a more abstract space. Lower precision could then be used for the fine-tuning or implementation of these predictions.
Challenges:
Accuracy Control: Ensuring that the approximations and lower-precision calculations don't introduce unacceptable errors is crucial.
Stability: Non-linear systems can be sensitive to small errors, so careful design is needed to maintain stability.
Adaptability: The method needs to be adaptable to different types of non-linear systems and different levels of complexity.
In conclusion:
The concept of using techniques similar to motion vector estimation to improve the efficiency of lower-precision computations in non-linear systems is a powerful one. It involves leveraging approximations, predictions, and iterative refinement to reduce computational cost while maintaining acceptable accuracy. This is an active area of research with the potential to significantly impact fields like scientific computing, simulation, and AI."
doc2016
2 days ago
how could vcsy support variable precision computing bits? gemini ai says:"Software support for variable precision bits involves a combination of techniques at different levels of the software stack. Here's how it works:
1. Programming Languages and Compilers:
Data Type Flexibility:
Programming languages can provide flexible data types that allow for representing numbers with different precision levels.
For example, instead of just float and double, languages could introduce data types like float16, bfloat16, or custom-sized floating-point formats.
Compiler Optimizations:
Compilers can analyze code and automatically insert instructions to switch between different precision levels based on the context.
They can identify sections of code where lower precision is acceptable and generate instructions accordingly.
They can also optimize code to minimize the overhead of precision switching.
Libraries:
Libraries can provide functions and routines for performing arithmetic operations with variable precision.
These libraries can be optimized for specific hardware architectures and provide efficient implementations of different precision levels.
2. Operating System (OS):
Hardware Abstraction:
The OS can provide a hardware abstraction layer that allows applications to access hardware features for variable precision.
This can involve providing APIs for controlling hardware units that support different precision levels.
Task Scheduling:
The OS can schedule tasks based on their precision requirements.
For example, it can prioritize tasks that require high precision and allocate more resources to them.
Power Management:
The OS can manage power consumption by dynamically adjusting the precision levels of different hardware components.
It can reduce power consumption by using lower precision when possible.
3. Applications:
Adaptive Algorithms:
Applications can use adaptive algorithms that dynamically adjust precision based on the input data or the desired accuracy.
For example, machine learning applications can use lower precision during training and higher precision during inference.
Data Representation:
Applications can use data representations that allow for storing and manipulating numbers with different precision levels.
This can involve using custom data structures or libraries.
User Control:
Applications can provide users with control over the precision levels used for different operations.
This allows users to balance performance and accuracy based on their specific needs.
Examples:
Machine Learning:
Using bfloat16 or float16 for training deep neural networks to reduce memory usage and increase performance.
Using mixed-precision training, where different layers of the network use different precision levels.
Graphics Processing:
Using lower precision for rendering less important parts of a scene to improve performance.
Using higher precision for rendering critical parts of a scene to improve quality.
Scientific Computing:
Using adaptive precision algorithms to solve numerical problems with varying accuracy requirements.
In essence, software support for variable precision bits requires a coordinated effort across the entire software stack, from programming languages to applications."
doc2016
2 days ago
is the ionq/ansys work an example of quantum simulation versus hpc work
gemini ai responded:
"The work between IonQ and Ansys is a compelling example of the intersection between quantum simulation and high-performance computing (HPC), and how quantum computing can augment, and in some cases, outperform, classical HPC. Here's how to understand their collaboration:
Key Points:
Hybrid Approach:
The IonQ and Ansys collaboration demonstrates a "hybrid" quantum-classical approach. This means they're not entirely replacing classical HPC, but rather using quantum computers to accelerate specific parts of the simulation process.
This is a very important point. In the near term, quantum computers will most likely be used to augment, rather than replace classical computers.
Targeted Workflows:
They're focusing on targeted engineering simulation workflows, specifically those where quantum computers offer a clear advantage.
In the reported milestones, they have shown that quantum computers can outperform classical computers, in the simulation of medical devices, like blood pumps.
Quantum Acceleration:
The goal is to use IonQ's quantum computers to accelerate computationally intensive parts of Ansys' simulation software.
This means that they are using the quantum computer, as an accelerator, for very specific, and difficult calculations.
Ansys's Role:
Ansys provides the classical HPC simulation software (like LS-DYNA) and the overall simulation workflow.
They are also working to integrate the quantum algorithms, into their software.
IonQ's Role:
IonQ provides the quantum computing hardware and software.
They are working to optimize quantum algorithms for Ansys's simulation problems.
Quantum Simulation vs. HPC:
HPC:
Classical HPC relies on powerful classical computers to solve complex simulations.
It's well-established and widely used in engineering.
Quantum Simulation:
Quantum simulation uses quantum computers to model and analyze quantum systems.
It has the potential to solve problems that are intractable for classical computers.
The ionq/ansys work, is showing that quantum computers are now able to provide real world value, in simulation.
In essence, the IonQ and Ansys collaboration is about leveraging the strengths of both quantum computing and classical HPC to achieve better simulation results. It's not a complete replacement of HPC, but rather a strategic integration of quantum capabilities"
doc2016
2 days ago
geminiai says,
"Key Points:
The goal of quantum simulation is to create a controllable quantum system that can accurately model the behavior of another quantum system.
Analog quantum simulators often use physical systems that are very similar to the system being mimicked.
Digital quantum simulators use a more general-purpose quantum computer, but still rely on the same fundamental quantum principles.
The most important aspect of a quantum simulator, is that it can be controlled, and manipulated, in a way that accurately models the system that is being simulated.
Therefore, while there are different methods, and different types of quantum simulators, they do not have to be different types of quantum processors, than the system being mimicked."
doc2016
2 days ago
geminiai responded:"You've picked up on a fascinating aspect of Jensen Huang's (NVIDIA CEO) discussions about quantum computing and AI. He often emphasizes a shift in computational paradigms, moving away from strict, high-precision calculations toward a more iterative, "close enough" approach, especially when dealing with complex systems like LLMs or quantum simulations. This aligns with your observation about motion vector estimation and algorithms converging toward an answer.
Here's a breakdown of what he likely meant:
1. The "Analog" Nature of Quantum and AI:
Jensen often draws parallels between quantum computing and certain aspects of AI, particularly deep learning.
Both involve dealing with vast, high-dimensional spaces where exact solutions are often intractable.
He highlights that both domains rely on statistical approximations and iterative refinement rather than deterministic, precise calculations.
2. LLMs and Motion Vector Estimation:
Motion Vector Estimation:
In video compression, motion vector estimation involves finding the best approximation of how pixels move between frames.
It's an iterative process where algorithms get progressively closer to the optimal motion vectors, rather than calculating them with perfect precision from the start.
LLMs and "Approximation":
LLMs, similarly, don't generate text by rigidly following pre-defined rules.
They generate text by statistically predicting the next word based on learned patterns.
The process involves iteratively refining the output, with each word prediction influenced by the previous ones.
This is an approximation of human language, not a perfect recreation.
Convergence:
Both motion vector estimation and LLM generation involve a form of "convergence," where the algorithm gradually approaches a satisfactory result.
The goal is not necessarily perfect precision but rather a result that is "good enough" for the intended purpose.
3. Quantum Computing and "Close Enough":
Quantum Noise:
Current quantum computers are inherently noisy, meaning that calculations are prone to errors.
This necessitates the use of error mitigation techniques, which involve statistical sampling and approximation.
Variational Quantum Algorithms:
Many quantum algorithms, such as variational quantum eigensolvers (VQEs), involve iterative optimization processes.
These algorithms gradually refine the quantum circuit parameters to minimize an objective function, rather than calculating the exact solution.
Simulation Complexity:
Simulating complex quantum systems often involves approximations and trade-offs between accuracy and computational cost.
Jensen likely emphasizes that for many quantum applications, a "close enough" solution is sufficient.
4. The Shift in Computational Thinking:
Jensen's comments reflect a broader shift in computational thinking, where the focus is moving away from purely deterministic, high-precision calculations toward more statistical, approximate, and iterative approaches.
This is particularly relevant for domains like AI and quantum computing, where dealing with immense complexity is essential.
In essence, Jensen is highlighting that in complex computational domains, the pursuit of absolute precision is often impractical or unnecessary. Instead, algorithms and systems are designed to converge toward satisfactory solutions through iterative approximation."
so, the paradigm might be use the hpc/ai to get close and then use the quantum to get it to it's most granular that will do the job? how do you create the automatic native function for that? knowing when to go from 64 to 32 to 16 to 8 to 4decimals? 8x8 admin instructions for xenon's which are x86 which used x87 from 8x8 in the day....interesting the ansys test was with xenon's........so might they have tested jensen's idea of approximation until a crucial point and going quantum led to 13% faster to the answer? how many logical qubits did they use?
Jack Torrance
2 days ago
Most computers are not 16 cores and 1.5 TB of RAM. The point is that it's about 4-6x more powerful than your average consumer computer. And most of the most powerful classical computer tech has been introduced in the past ten years and hasn't evolved or gotten much better. The answer by computer companies is to simply add more cores and more RAM. Read some Ray Kurzweil or any tech paper on classical computing advancements in the 21st century. If you bought a really good computer in 2019, it's not going to be much worse than a really good computer bought in 2025. My own computer is from 2013. It has 4 cores, 32 GB of RAM, and I can edit 4K video footage on it. I keep looking into new computers, but they simply haven't gotten so much better for me to justify the $1500 - $2000 I would have to spend to get a 20% increase in computing power.
Also, you're a fucking wanker and you should get the fuck off this board.
Jack Torrance
3 days ago
Not likely. Other planes might be able to carry and launch drones from the air. But to carry even a smaller drone, a fighter jet would have to be massive. I doubt they want a massive fighter jet. Then it would just be a bomber.
And the Air Force just came up with a new missile that is small and has great anti-drone capabilities, meaning it can take out small drones reall well. And they can be carried in pods of 7 missiles each that can be carried and fired from a slew of aircraft and even helicopters, I think. A single F-16 I think can carry like two pods or more, for 14 or more of these missiles, in addition to other air-to-air and air-to-ground missiles. And they only cost like $50K a piece, rather than hundreds of thousands, or even millions, for legacy missiles. Drones in large numbers are becoming more and more common. The enemy likes to send a wave of drones to overwhelm their enemy's defenses. A single US fighter jet being able to carry more than a dozen of these missiles is a massive advancement.
Combine the F-47 and its drone swarm, with F-22s, F-35s -- and now F-15s, F-16s and F18s carrying this new anti-drone missile, and it's game over for any opponent in any air combat.
Check out Alex Hollings at the "SandBoxx" channel on YouTube for US military aircraft and weapon news. He's beyond legit.
doc2016
3 days ago
is the f47 a drone carrier? like the avengers? but might they carry different sized drones for different missions and serve as remote cloud servers for the duplex ai of on device and off device? do they have communications like the airforce 1's redesign might have...6g and such?
might these drones be one and done things?
the f47 mother ship might also have ip to entangle or use entangled twins automatically or man served?
imaging a mother ship with a mobile hpc and quantum computing thing able to work with the drones for defense and offense...already movies have beat the defense contractors , see battle for Los Angeles.
and potential for self forming wormholes and tiny drones?
might be highly kinetic?
expect china , russia, noko, iran to unleash flurry of cyber espionage.
the boston quantum /nvidia r and d center right in the middle of fusion reactor, magnetic containment, tumor treatment fields, electroporation, dna sequencing, fluid dynamics of ocean and weather creation,
also, look for an attempt to change the city's and state's leadership, which stems from it's us Senators. are they misguided or agents of socialist cause?
Jack Torrance
3 days ago
I think the White House made a statement that all the drones were authorized, or something to that effect. I took that to mean: "Hey, these were ours, but it's all classified so move along, nothing to see here."
Concur on the F-47 stuff. It had been rumored that test flights had already been done, but for the past 5 years was a bit of as surprise. My guess is they're going to be building the electronics processing core systems in a modular way, so that when QPUs are up to the task, and reliable enough, all the airframes will be upgraded to new computing cores. Or perhaps they will have a system with both classical and quantum cores, for redundancy, or for when more intense task are needed, data will be routed to the quantum core. Tracking systems, avionics, weapons, comms. It's all going to need to be quantum eventually. With quantum encryption and quantum wireless networking. And Air Force is already in bed with IonQ and rounding 1st base. LOL.
Jack Torrance
3 days ago
https://arxiv.org/pdf/2503.13128
Page 9: All simulations were done on a 4 Intel Xeon Gold 6242 CPUs, 64 cores in total; and 1.5TB memory.
Also page 9: Every experiment was conducted on the same machine, built with 4 Intel Xeon Gold 6242 CPUs, 16 cores each, and 1.5TB of memory.
That is some serious hardware, and makes the 12% way more impressive. And Tempo will be tens of millions of times more powerful than Forte. And Tempo is happening this year. Could drop at any time. They are already building and testing it. Gotta implement all those new goodies they've come up with in the past 6-12 months first.
Trade smarter, not harder: Unleash your inner pro with our toolkit and live discussions.
PonderosaPack
44 minutes ago
Mr. Hathaway
14 hours ago
FooBarAndGrill
2 days ago
Hot Features
Premium
