What physics is harder to study, and why?
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Khurshed P. Fitter
Khurshed P. Fitter
I am a Quantum Science and Engineering master's student at EPFL, Switzerland. I am pursuing my thesis at the Max Planck Institute for Quantum Optics with Dr. Mari Carmen Bañuls and Prof. Ignacio Cirac, co-supervised by Prof. Vincenzo Savona. My thesis focuses on entanglement and destabilizerness dynamics in integrable and non-integrable systems.
My past experience and present interests revolve around understanding
What physical properties of a system are harder to capture, and make the system harder to simulate?
Mainly through,
Studying collective phenomena in quantum many-body systems.
Classical and quantum algorithms for the same.
Resource scaling like entanglement and non-stabilizerness.
Besides research, I like to spend my time reading and looking at the trees and clouds.
I am looking for PhD positions starting in Fall 2026.
News
News
[Jan 2026] Presented our work on resource scaling in non-integrable spin chains at the Quantum Many Body Seminar, MPQ Theory division.
[Nov 2025] Presented our work on variational phase estimation at the International European Tensor Network PhD School TENSOR25, Gottingen.
[Oct 2025] Presented our work on variational phase estimation at the Enhanced quantum information processing targeting the near term 2025, Munich.
[Oct 2025] Our work on multipartite entanglement in random quantum states was published in Quantum.
[Feb 2025] Presented our work on variational phase estimation at the International Workshop on Machine Learning for Quantum Matter 2025, Dresden.
[Feb 2025] Started my internship at the Max Planck Institute for Quantum Optics with Dr. Mari Carmen Bañuls.
Publications
Publications
Estimating the entanglement of random multipartite quantum states
Estimating the entanglement of random multipartite quantum states
K. Fitter, C. Lancien, I. Nechita
We present a general algorithm to compute the injective norm for arbitrary quantum states. Our results constitute the first numerical estimates on the amount of genuinely multipartite entanglement typically present in various, physically relevant models of random multipartite pure states.
A Max-Flow approach to Random Tensor Networks
A Max-Flow approach to Random Tensor Networks
K. Fitter, F. Loulidi, I. Nechita
Random tensor networks play a pivotal role in several physically rich settings. We aim to study their entanglement using a max-flow approach that is equivalent and more general than the usual min-cut approaches. We analytically derive leading terms and corrections for the bipartite entanglement using concepts from combinatorics and free probability theory.
Development of analytical solutions for Photoacoustic Imaging of an extended line source with acoustic lens based reconstruction strategy
Development of analytical solutions for Photoacoustic Imaging of an extended line source with acoustic lens based reconstruction strategy
International Conference on the Paradigm Shifts in Communication, Embedded Systems, Machine Learning and Signal Processing (PCEMS), 2022
K. Fitter, S. Sinha
Photoacoustic imaging combines the best of both worlds; optical and acoustic imaging. However, most current methods involve huge computational overheads. We present analytical solutions for the imaging of extended line sources using an acoustic lensing setup.
Enhancing Context Through Contrast
Enhancing Context Through Contrast
NeurIPS 2021 Workshop on Pre-registration in Machine Learning
K. Ambilduke, A. Shetye, D. Bagade, R. Bhagwatkar, K. Fitter, P. Vagdargi, S. Chiddarwar
We posit that languages are linguistic transforms that map abstract meaning to sentences. We attempt to extract and investigate this abstract space by optimizing the Barlow Twins objective between latent representations of parallel sentences.
Paying Attention to Video Generation
Paying Attention to Video Generation
PMLR 148:139-154, 2021, NeurIPS 2020 Workshop on Pre-registration in Machine Learning
R. Bhagwatkar, K. Fitter, S. Bachu, A. Kulkarni, S. Chiddarwar
Just like sentences are series of words, videos are series of images. Inspired by the success of large language models in predicting language, we attempt to generate videos using a GPT and a novel Attention-based Discretized Autoencoder.
A Review of Video Generation Approaches
A Review of Video Generation Approaches
International Conference on Power, Instrumentation, Control and Computing (PICC), 2020
R. Bhagwatkar, K. Fitter, S. Bachu, A. Kulkarni, S. Chiddarwar
In this work we study and discuss several approaches for generating videos, either using Generative Adversarial Networks (GANs) to sequential models like LSTMs. Further, we compare the strengths and weakness of each approach with the underlying motivation to provide a broad and rigorous review on the subject.
Projects
Projects
Machine Learning Phases of Matter
Developed machine learning models for detecting phases in toy physical models inspired by previous work.
Implemented feed-forward and convolutional neural networks for detecting topological phases in 2D Ising gauge theories.
Quantum Machine Learning
Developing an open-source repository containing various QML paper implementations.
Variational Quantum Classifier
Explored, designed and cross-validated more than 20 feature maps for binary classification using variational quantum circuits.
Achieved an accuracy of more than 81% on a classified test dataset.
Trained the models for binary classification amongst samples of digits 4 and 9 from the MNIST Dataset, reduced to three dimensions.
Quantum SVM
Implemented a QSVM using Qiskit to develop kernel mappings for fitting hyperplanes corresponding to binary classification tasks.
Performed binary classification on an ad-hoc and breast cancer dataset with more than 99% accuracy.
Medical VQA
Deployed various Visual Question Answering models on medical datasets.
Improved Facebook AI Research’s MMF framework for medical data.
Achieved leaderboard performance on the ImageCLEF-2019 dataset.
Video Generation
Aimed at generating entire frames and not pixel-level predictions.
Developed a novel Attention Based Discretized Autoencocder (ADAE).
Coupled the ADAE with a GPT-2 for video generation.
Neural Machine Translation
Language Modelling
Generated Dinosaur names using Character-level RNNs.
Developed a paragraph generator to generate text from Harry Potter novels.
Implemented RNNs from scratch and compared performance with and amongst different inbuilt RNN modules using PyTorch.
Variational Deep Learning
Studied and implemented various autoencoders and generative networks.
Developing variational models for multimodal applications, mainly sequential multimodal data like electroencephalography signals.
Landmark Retrieval
Aimed at extracting images of landmarks similar to a query image.
Designed a ResNet-101 based autoencoder for the above task on “Google’s Landmark Dataset-v2” using TensorFlow.
Real-time Digit Classifier
Developed an open-source pipeline for human-computer interaction using Deep Learning and Computer Vision for digit classification.
Trained Convolutional and Deep Neural Networks from scratch.
Achieved 99% accuracy on the MNIST Dataset in real-time.
Detection & Tracking
Aimed at object detection and tracking from high altitude aerial vehicles.
Optimized the pipeline to deliver real-time performance with human accuracy.
Email: khurshed [dot] fitter [at] epfl [dot] ch
Email: khurshed [dot] fitter [at] epfl [dot] ch
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