menu
{ "item_title" : "Applications of Quantum Field Theory to Problems in Machine Learning", "item_author" : [" Harish Parthasarathy "], "item_description" : "This book examines quantum neural networks through renormalization techniques, supersymmetric field theory, and noisy harmonic oscillator systems. The book's analysis covers adaptive beamforming applications, brain modeling, gravitational control mechanisms, and mixed-state dynamics in superstring theory.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/1/04/128/125/1041281250_b.jpg", "price_data" : { "retail_price" : "249.99", "online_price" : "249.99", "our_price" : "249.99", "club_price" : "249.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applications of Quantum Field Theory to Problems in Machine Learning|Harish Parthasarathy

Applications of Quantum Field Theory to Problems in Machine Learning : Advanced Techniques Based on Path Integrals

local_shippingShip to Me
In Stock.
FREE Shipping for Club Members help

Overview

This book examines quantum neural networks through renormalization techniques, supersymmetric field theory, and noisy harmonic oscillator systems. The book's analysis covers adaptive beamforming applications, brain modeling, gravitational control mechanisms, and mixed-state dynamics in superstring theory.

This item is Non-Returnable

Details

  • ISBN-13: 9781041281252
  • ISBN-10: 1041281250
  • Publisher: CRC Press
  • Publish Date: May 2026
  • Dimensions: 9.21 x 6.14 x 0.88 inches
  • Shipping Weight: 1.6 pounds
  • Page Count: 376

Related Categories

You May Also Like...

    1

BAM Customer Reviews