Quantum machine learning

Hybrid quantum–classical models for large-scale scientific datasets, including applications in chemistry, materials, and ADME-Tox property prediction for drug discovery. We also study training dynamics and information scrambling in QML models.

For related publications, see the Publications page.

Related publications

MK Saggi, AS Bhatia, IK Mensah, H Gowher, S Kais. “Multi-omic and quantum machine learning integration for lung subtypes classification.” Future Generation Computer Systems 174, 107905. 2026. Cited by 31
M Bezick, BA Wilson, V Iyer, Y Chen, VM Shalaev, S Kais, AV Kildishev, .... “Pearsan: a machine learning method for inverse design using pearson correlated surrogate annealing.” Advanced Optical Materials 14 (10), e00249. 2026. Cited by 2
M Isik, S Kais. “Quantum Reinforcement Learning for Semantic Ranking of Enzyme Functionality.” APS Global Physics Summit 2026. 2026.
MK Saggi, AS Bhatia, H Gowher, S Kais. “Quantum AI for Cancer Diagnostic Biomarker Discovery.” arXiv preprint arXiv:2604.18621. 2026. Cited by 1
V Singh, AS Bhatia, MK Saggi, M Sajjan, S Kais. “Quantum machine learning for complex systems: paradigms, applications, and challenges.” Academia Quantum 3 (2). 2026.
M Sajjan, V Singh, S Kais. “Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications.” npj Quantum Information. 2026. Cited by 4
T Chatterjee, M Sajjan, S Xie, E Love, V Singh, BN Bakalov, S Kais. “Symmetry Constraints Regularize Neural Quantum State Learning.” arXiv preprint arXiv:2608.08798. 2026.
M Sajjan, VK Singh, S Kais. “Efficient Quantum-enabled Monte Carlo sampling for training neural network quantum states.” APS Global Physics Summit 2025. 2025.
M Bezick, BA Wilson, V Iyer, Y Chen, VM Shalaev, S Kais, AV Kildishev, .... “PearSAN: an inverse design framework for the latent optimization of photonic devices using Pearson Correlated Surrogate Annealing.” APS Global Physics Summit 2025. 2025.
M Bezick, BA Wilson, V Iyer, Y Chen, VM Shalaev, S Kais, AV Kildishev, .... “Photonic inverse design through machine learning and correlated surrogate annealing.” 2025 Conference on Lasers and Electro-Optics (CLEO), 1-2. 2025. Cited by 2
AS Bhatia, MK Saggi, S Kais. “Application of quantum-inspired tensor networks to optimize federated learning systems.” Quantum Machine Intelligence 7 (1), 12. 2025. Cited by 15
V Singh, R Gupta, M Sajjan, F Remacle, RD Levine, S Kais. “Maximal entropy formalism and the restricted boltzmann machine.” The Journal of Physical Chemistry A 129 (24), 5405-5414. 2025. Cited by 5
AS Bhatia, S Kais, MA Alam. “Quantum federated learning in healthcare: the shift from development to deployment and from models to data.” IEEE Journal of Biomedical and Health Informatics 30 (8), 6746-6759. 2025. Cited by 23
AS Bhatia, S Kais. “Enhancing quantum federated learning with Fisher information-based optimization.” 2025 IEEE International Conference on Quantum Computing and Engineering (QCE …. 2025. Cited by 4
MK Saggi, AS Bhatia, S Kais. “Quantum Integration of Spiking Leaky Integrate-and-Fire Neurons and Variational Circuits for Enhanced Multiclass Classification.” 2025 IEEE International Conference on Quantum Computing and Engineering (QCE …. 2025. Cited by 1
V Iyer, M Bezick, BA Wilson, Y Chen, VM Shalaev, S Kais, AV Kildishev, .... “Machine-learning assisted surrogate annealing for photonic device inverse design.” Photonic Computing: From Materials and Devices to Systems and Applications …. 2025.
M Isik, MK Saggi, H Gowher, S Kais. “Multimodal Quantum Vision Transformer for Enzyme Commission Classification from Biochemical Representations.” 2025 IEEE International Conference on Quantum Artificial Intelligence (QAI …. 2025. Cited by 2
MK Saggi, AS Bhatia, S Kais. “Federated quantum machine learning for drug discovery and healthcare.” Annual Reports in Computational Chemistry 20, 269-322. 2024. Cited by 17
J Wang, M Kim, S Kais. “Information Bottleneck of Quantum Neural Networks.” NeurIPS 2024 Workshop Machine Learning with new Compute Paradigms. 2024. Cited by 2
M Sajjan, V Singh, S Kais. “Sampling based quantum training of arbitrary spin-graphs.” APS March Meeting Abstracts 2024, N49. 009. 2024.
AS Bhatia, S Kais, MA Alam. “Robustness of quantum federated learning (qfl) against “label flipping attacks” for lithography hotspot detection in semiconductor manufacturing.” 2024 IEEE international reliability physics symposium (IRPS), 1-4. 2024. Cited by 12
BA Wilson, J Wurtz, V Mkhitaryan, M Bezick, ST Wang, S Kais, .... “Non-native quantum generative optimization with adversarial autoencoders.” arXiv preprint arXiv:2407.13830. 2024. Cited by 8
MK Saggi, S Kais. “MQML: multi-omic quantum machine learning based cancer classification, biomarker identification in human lung adenocarcinoma.” 2024 IEEE International Conference on Quantum Computing and Engineering (QCE …. 2024. Cited by 9
AS Bhatia, S Kais, MA Alam. “On the robustness of variational quantum classifier against “label flipping attacks” in federated learning for semiconductor manufacturing.” 2024 IEEE International Conference on Quantum Computing and Engineering (QCE …. 2024. Cited by 5
AS Bhatia, MK Saggi, S Kais. “Communication-efficient quantum federated learning optimization for multi-center healthcare data.” 2024 IEEE EMBS International Conference on Biomedical and Health Informatics …. 2024. Cited by 18
A Bhatia, S Kais, M Alam. “Handling privacy-sensitive clinical data with federated quantum machine learning.” APS March Meeting Abstracts 2023, T70. 007. 2023. Cited by 14
M Saggi, S Kais. “Using quantum circuits with convolutional neural networks for multi-object detection and classification.” APS march meeting abstracts 2023, D73. 001. 2023. Cited by 2
M Sajjan, V Singh, S Kais. “Demystifying a generative Quantum Machine Learning model using Information scrambling and Imaginary components of out-of-time correlators..” APS March Meeting Abstracts 2023, K73. 012. 2023.
M Sajjan, V Singh, R Selvarajan, S Kais. “Imaginary components of out-of-time-order correlator and information scrambling for navigating the learning landscape of a quantum machine learning model.” Physical Review Research 5 (1), 013146. 2023. Cited by 52
SS Iyengar, S Kais. “Analogy between Boltzmann machines and Feynman path integrals.” Journal of Chemical Theory and Computation 19 (9), 2446-2454. 2023. Cited by 5
AS Bhatia, MK Saggi, S Kais. “Quantum machine learning predicting ADME-Tox properties in drug discovery.” Journal of Chemical Information and Modeling 63 (21), 6476-6486. 2023. Cited by 82
AS Bhatia, S Kais, MA Alam. “Federated quanvolutional neural network: a new paradigm for collaborative quantum learning.” Quantum Science and Technology 8 (4), 045032. 2023. Cited by 49
R Selvarajan, M Sajjan, TS Humble, S Kais. “Dimensionality reduction with variational encoders based on subsystem purification.” Mathematics 11 (22), 4678. 2023. Cited by 11
S Kais. “Quantum machine-learning algorithm for complex chemical systems.” APS March Meeting Abstracts 2022, B01. 001. 2022. Cited by 1
B Khalid, SH Sureshbabu, A Banerjee, S Kais. “Finite-size scaling on a digital quantum simulator using quantum restricted Boltzmann machine.” Frontiers in Physics 10, 915863. 2022. Cited by 7
B Wilson, Y Chen, S Kais, A Kildishev, V Shalaev, A Boltasseva. “Empowering quantum 2.0 devices and approaches with machine learning.” Quantum 2.0, QTu2A. 13. 2022. Cited by 7
M Sajjan, J Li, R Selvarajan, SH Sureshbabu, SS Kale, R Gupta, V Singh, .... “Quantum machine learning for chemistry and physics.” Chemical Society Reviews 51 (15), 6475-6573. 2022. Cited by 228
S Oh, S Kais. “Quantum Thermodynamics of Quantum Boltzmann Machines.” APS March Meeting Abstracts 2021, V32. 006. 2021.
SH Sureshbabu, R Xia, S Kais. “Implementation of quantum machine learning for electronic structure calculations of periodic systems on NISQ devices.” APS March Meeting Abstracts 2021, V32. 010. 2021.
BA Wilson, ZA Kudyshev, AV Kildishev, S Kais, VM Shalaev, A Boltasseva. “Metasurface design optimization via D-Wave based sampling.” 2021 Conference on Lasers and Electro-Optics (CLEO), 1-2. 2021. Cited by 4
V Dixit, R Selvarajan, T Aldwairi, Y Koshka, MA Novotny, TS Humble, .... “Training a quantum annealing based restricted boltzmann machine on cybersecurity data.” IEEE Transactions on Emerging Topics in Computational Intelligence 6 (3 …. 2021. Cited by 68
SH Sureshbabu, M Sajjan, S Oh, S Kais. “Implementation of quantum machine learning for electronic structure calculations of periodic systems on quantum computing devices.” Journal of Chemical Information and Modeling 61 (6), 2667-2674. 2021. Cited by 54
V Dixit, R Selvarajan, MA Alam, TS Humble, S Kais. “Training restricted Boltzmann machines with a D-Wave quantum annealer.” Frontiers in Physics 9, 589626. 2021. Cited by 73
J Li, S Kais. “Quantum cluster algorithm for data classification.” Materials Theory 5 (1), 6. 2021. Cited by 18
M Sajjan, SH Sureshbabu, S Kais. “Quantum machine-learning for eigenstate filtration in two-dimensional materials.” Journal of the American Chemical Society 143 (44), 18426-18445. 2021. Cited by 62
M Sajjan, J Li, R Selvarajan, SH Sureshbabu, SS Kale, R Gupta, S Kais. “Quantum computing enhanced machine learning for physico-chemical applications.” arXiv preprint arXiv:2111.00851. 2021. Cited by 8
BA Wilson, ZA Kudyshev, AV Kildishev, S Kais, VM Shalaev, A Boltasseva. “Machine learning framework for quantum sampling of highly constrained, continuous optimization problems.” Applied Physics Reviews 8 (4). 2021. Cited by 53
T Bian, S Kais. “Hybrid quantum-classical algorithms for generative models.” APS March Meeting 2020. 2020.
R Xia, S Kais. “Quantum neural network for generating quantum states.” APS March Meeting 2020. 2020.
V Dixit, R Selvarajan, MA Alam, TS Humble, S Kais. “Training and classification using a restricted boltzmann machine on the d-wave 2000q.” arXiv preprint arXiv:2005.03247. 2020. Cited by 22
R Xia, S Kais. “Hybrid quantum-classical neural network for calculating ground state energies of molecules.” Entropy 22 (8), 828. 2020. Cited by 59
R Xia, S Kais. “Hybrid quantum-classical neural network for generating quantum states.” arXiv preprint arXiv:1912.06184. 2019. Cited by 4
R Xia, S Kais. “Quantum machine learning for electronic structure calculations.” Nature communications 9 (1), 4195. 2018. Cited by 236