Rajesh N. Rao, PhD’s Post

✨ Day 10 Of #Quantum30 challenge: Today's learning topic was Quantum Support Vector Machine (QSVM) -> It is the quantum analog of classical SVM -> The thing that gives QSVM an edge over SVM is the computation of kernel function as some kernel matrices can be expensive to compute classically. -> QSVM uses quantum kernel method. When the classical data is mapped into Hilbert space of quantum computer it is intrinsically cast into higher dimensional space and then by utilising the superposition and entanglement properties of quantum mechanics we can perform classifications or regression tasks. -> Thus QSVM can be used to accelerate the classifications tasks especially involving large datasets. #QuantumComputingIndia #quantumcomputing #quantumtechnology

What does human behaviour have in common with quantum mechanics, and how can it inform the design of quantum machine learning models? Find out in "An inductive bias from quantum mechanics: learning order effects with non-commuting measurements" https://lnkd.in/gqyb25ZE ➡

Truly fascinating. Let's study this on a deeper level!

Good afternoon! I hope you had a good run this morning. Below is a sneak peek into the text-to-physics engine (using the Inverse Kinematics for Human Joints model) that's about to launch soon. Human motion and interaction with our environment is one of the most intriguing problems to solve using inverse processes (like Diffusion) instead of differentiators or hardware acceleration. Follow LearnQuantum for more updates on our AI Native Physics Engine.

💡#LessonOfTheWeek: Multibody Interactions! Read this week’s lesson from QCi’s learning module on the analog quantum advantage and learn about the significance of multibody interactions and their crucial role and usefulness in computing and optimization. In this lesson, QCi will contrast two-body interactions that are common in physics (like the gravitational many-body problem), with higher-order interactions, which are challenging to engineer but enable complex logical statements and problem-solving, such as in satisfiability and factoring problems. Interested in learning more? Read the full lesson here: https://lnkd.in/eDxc9JKT #QCiLesson #QCi #multibody #manybody #interaction #problem #expression #computing #hardware #optimizationproblem #optimization #NPhard #hardoptimization #linear #QUBO #quadratic #highorder #secondorder #satisfiability #factoring #integer #errorcorrection #quantumcomputing #computing #quantumsystems #quantumphysics #computerscience #quantum #quantumadvantage #quantumlessons #learningmodule #learnquantum

A general quantum circuit can be simulated classically in exponential time. If it has a planar layout, then a tensor-network contraction algorithm due to Markov and Shi has a runtime exponential in the square root of its size, or more generally exponential in the treewidth of the underlying graph. Separately, Gottesman and Knill showed that if all gates are restricted to be Clifford, then there is a polynomial time simulation. We combine these two ideas and show that treewidth and planarity can be exploited to improve Clifford circuit simulation. Our main result is a classical algorithm with runtime scaling asymptotically as nω/2<n1.19 which samples from the output distribution obtained by measuring all n qubits of a planar graph state in given Pauli bases. Here ω is the matrix multiplication exponent. We also provide a classical algorithm with the same asymptotic runtime which samples from the output distribution of any constant-depth Clifford circuit in a planar geometry. Our work improves known classical algorithms with cubic runtime. https://lnkd.in/gmmWUFM6

📢📢 Excited to share our preprint "Wu's Method can Boost Symbolic AI to Rival Silver Medalists and AlphaGeometry to Outperform Gold Medalists at IMO Geometry"! We tackle problems from the International Math Olympiad (the 'olympics of math'), and: - present a purely symbolic AI method that solves 21/30 IMO problems, on par with Silver Medalists, in 3 mins on a laptop CPU. - extend AlphaGeometry to solve 27/30 problems -> the first instance of an AI outperforming IMO Gold Medalists! Findings X 🧵 👉🏽 https://lnkd.in/dQvNVeTi  📜 Pre-print: https://lnkd.in/dbSXg9pW 🤗 Data: https://lnkd.in/dc_DuG5F W/ Shiven Sinha, Ameya Prabhu, Siddharth Bhat, Matthias Bethge #Maths #Mathematics #MathOlympiad #ProfGiri

Machine Learning for Multiphysics applications...

Machine Learning for Multiphysics applications...

Simulate gravitational interaction with #MATLAB. Explore the science at https://lnkd.in/dmMPxJrD