@quantumcomputing @perimeterinstitute @uwaterloo @scicomm @foss @privacy @openscience @eff @fsf
#QuantumComputing #MachineLearning #QuantumML #FederatedLearning #AI #QuantumPhysics #ReinforcementLearning #Entanglement #QuantumEntanglement #TechPodcast #SciComm #ComputerScience #QuantumAlgorithms #DeepLearning #NISQ #SciencePodcast #EvidenceBasedPodcast #ScienceCommunication #OpenScience #Privacy #EthicalAI #DistributedSystems
The innovation: Using quantum entanglement—Einstein's "spooky action at a distance"—as the actual coordination mechanism for multi-agent AI systems.
No classical communication needed. No data sharing required. Just physics doing the work at the subatomic level.
#QuantumML #FederatedLearning
The innovation: Using quantum entanglement—Einstein's "spooky action at a distance"—as the actual coordination mechanism for multi-agent AI systems.
No classical communication needed. No data sharing required. Just physics doing the work at the subatomic level.
Llion Jones, co-creator of transformers, says AI is stuck in a transformer loop.
// AI research is now too narrowly focused, making the field risk-averse and less creative.
// Researchers rush to publish similar work, stifling innovation.
// Jones calls for a return to bold exploration and research freedom.
Read more: https://buff.ly/l72yFAi
The field needs to fund risky, creative research—like Quantum Machine Learning. Let’s empower the best minds to explore what’s next!
Neural Networks for Programming Quantum Annealers
https://arxiv.org/abs/2308.06807
MIT, UWaterloo, Seth Lloyd (https://en.wikipedia.org/wiki/Seth_Lloyd)
* Both { quantum | classical } ML map information to high-dimensional vector spaces (Hilbert space: https://en.wikipedia.org/wiki/Hilbert_space) w/o explicitly calculating their numerical values
* tested wh. additional quantum layer could boost performance of classical NN
* no provide sig. benefit
#MachineLearning #NeuralNetworks #QuantumComputing #QuantumAnnealers #QuantumNN #QuantumML
Quantum machine learning has the potential to enable advances in artificial intelligence, such as solving problems intractable on classical computers. Some fundamental ideas behind quantum machine learning are similar to kernel methods in classical machine learning. Both process information by mapping it into high-dimensional vector spaces without explicitly calculating their numerical values. We explore a setup for performing classification on labeled classical datasets, consisting of a classical neural network connected to a quantum annealer. The neural network programs the quantum annealer's controls and thereby maps the annealer's initial states into new states in the Hilbert space. The neural network's parameters are optimized to maximize the distance of states corresponding to inputs from different classes and minimize the distance between quantum states corresponding to the same class. Recent literature showed that at least some of the "learning" is due to the quantum annealer, connecting a small linear network to a quantum annealer and using it to learn small and linearly inseparable datasets. In this study, we consider a similar but not quite the same case, where a classical fully-fledged neural network is connected with a small quantum annealer. In such a setting, the fully-fledged classical neural-network already has built-in nonlinearity and learning power, and can already handle the classification problem alone, we want to see whether an additional quantum layer could boost its performance. We simulate this system to learn several common datasets, including those for image and sound recognition. We conclude that adding a small quantum annealer does not provide a significant benefit over just using a regular (nonlinear) classical neural network.
Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. This work benchmarks the runtime and accuracy for a representative sample of specialized high-performance simulated and physical quantum processing units. Results show the QMware simulator can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The AWS SV1 simulator offers a runtime advantage for larger circuits, up to the maximum 34 qubits available with SV1. Beyond this limit, QMware can execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen-M2, can provide an exponential runtime advantage for circuits with more than 30 qubits. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, only IonQ's Harmony quantum device achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.
Zoomers of the Sunshine Coast 🇨🇦
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Victoria Stuart 🇨🇦 🏳️⚧️
Florian Marquardt
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