About

Sabre Kais is a professor and associate faculty member of the Department of Chemistry at NC State University. He holds the Goodnight Distinguished Chair in Quantum Computing and is also an Emeritus Distinguished Professor of Chemistry and Electrical and Computer Engineering at Purdue University. Kais earned his Ph.D. in Chemical Physics from Hebrew University in 1989, his Master's in Theoretical Chemistry from the same institution in 1984, and his Bachelor's in Chemistry in 1983. His research focuses on computational and theoretical chemistry, with particular interest in quantum computing, energy, green chemistry, medicinal chemistry, and various research areas within chemistry. Kais is involved in multiple research centers and facilities, contributing to advancements in chemical research and education.

Research topics

• Computer Science
• Quantum mechanics
• Machine Learning
• Artificial Intelligence
• Physics
• Psychology
• Algorithm
• Data science

Selected publications

• Chiral discrimination on gate-based quantum computers

  The Journal of Chemical Physics · 2026-04-09

  articleSenior author

  We present a novel approach to chiral discrimination using gate-based quantum processors, addressing a key challenge in adapting conventional control techniques using modern quantum computing. Schemes such as stimulated rapid adiabatic passage and shortcuts to adiabaticity have shown strong potential for enantiomer discrimination; however, their reliance on analog and continuous-time control makes them incompatible with digital gate-based quantum computing architectures. Here, we adapt these protocols for quantum computers by discretizing their Gaussian-shaped pulses through Trotterization. We simulate the chiral molecule 1,2-propanediol and experimentally validate this gate-based implementation on IBM quantum hardware. Our results demonstrate that this approach is a viable foundation for advancing chiral discrimination protocols, preparing the way for quantum-level manipulation of molecular chirality on accessible quantum architectures.

  PublisherDOI

• Polynomially efficient quantum enabled variational Monte Carlo for training neural-network quantum states for physico-chemical applications

  npj Quantum Information · 2026-05-08 · 2 citations

  preprintOpen accessSenior authorCorresponding

  With diverse architectures and strong expressivity, neural-network quantum states (NQS) offer an alternative to traditional variational ansätze for simulating physical systems. Energy-based models such as Hopfield networks and Restricted Boltzmann Machines draw on statistical physics, mapping quantum states onto energy landscapes as associative memories. We show these models can be trained efficiently with Monte Carlo accelerated by quantum devices. Our algorithm scales linearly with circuit width and depth, uses constant measurements, avoids mid-circuit measurements, and requires polynomial storage. It treats both phase and amplitude fields, enlarging the trial space. Sampling on quantum hardware shortens mixing times and yields more faithful estimates, revealing a quantum-assisted advantage. We demonstrate accurate learning of ground states for local spin models and nonlocal electronic-structure Hamiltonians, including at distorted geometries with strong multi-reference correlation. Benchmarks show close agreement and high robustness highlighting promise of machine-learning protocols paired with near-term quantum devices for state learning in chemistry and condensed-matter physics.

  PublisherOA PDFDOI

• Superdiffusion resilience in Heisenberg chains with two-dimensional interactions on a quantum processor

  Physical review. B./Physical review. B · 2026-03-30

  articleOpen access

  Observing superdiffusive scaling in the spin transport of the integrable 1D Heisenberg model is one of the key discoveries in non-equilibrium quantum many-body physics. Despite this remarkable theoretical development and the subsequent experimental observation of the phenomena in KCuF$_3$, real materials are often imperfect and contain integrability breaking interactions. Understanding the effect of such terms on the superdiffusion is crucial in identifying connections to such materials. Current quantum hardware has already ascertained its utility in studying such non-equilibrium phenomena by simulating the superdiffusion of the 1D Heisenberg model. In this work, we perform a quantum simulation of the superdiffusion breakdown by generalizing the superdiffusive Floquet-type 1D Heisenberg model to a general 2D model. We comprehensively study the effect of different 2D interactions on the superdiffusion breakdown by tuning up their strength from zero, corresponding to the 1D Heisenberg chain, to finite nonzero values. We observe that certain 2D interactions are more resilient against superdiffusion breakdown than others and that the $SU(2)$ preserving 2D interaction has the highest resilience among all the 2D interactions we study. Importantly, this observed resilience has direct implications for sustaining superdiffusive spin transport in two-dimensional lattices. We reason out the relative resilience against the superdiffusion breakdown through an analysis of the scattering coefficients off the 2D interaction in otherwise 1D chains. The relative resilience of different interaction types against superdiffusion breakdown was also captured in quantum hardware with remarkable accuracy, further establishing the current quantum hardware's applicability in simulating interesting non-equilibrium quantum many-body phenomena.

  PublisherOA PDFDOI

• Strong Local Passivity in Unconventional Scenarios: A New Protocol for Amplified Quantum Energy Teleportation.

  PubMed · 2025-11-12

  articleOpen accessSenior authorCorresponding

  Quantum energy teleportation (QET) has been proposed to overcome the restrictions of strong local passivity (SLP) and to facilitate energy transfer in quantum systems. Traditionally, QET has only been considered under strict constraints, including the requirements that the initial state be the ground state of an interacting Hamiltonian, that Alice's measurement commute with the interaction terms, and that entanglement be present. These constraints have significantly limited the broader applicability of QET protocols. In this work, we demonstrate that SLP can arise beyond these conventional constraints, establishing the necessity of QET in a wider range of scenarios for local energy extraction. This leads to a more flexible and generalized framework for QET. Furthermore, we introduce the concept of a "local effective Hamiltonian," which eliminates the need for optimization techniques in determining Bob's optimal energy extraction in QET protocols. As an additional advantage, the amount of energy that can be extracted using our new protocol is amplified to be 7.2 times higher than that of the original protocol. These advancements enhance our understanding of QET and extend its broader applications to quantum technologies. To support our findings, we implement the protocol on quantum hardware, confirming its theoretical validity and experimental feasibility.

  PublisherOA PDFDOI

• A Deep Dive into the Interplay of Structured Quantum Peaked Circuits and Infinite Temperature Correlation Functions

  2025-08-30

  articleSenior author

  Random quantum circuits have been extensively explored for quantum supremacy demonstrations [1]–[4]. However, verifying their output distributions remains challenging [2], [5], [6]. Here, we propose the infinite-temperature correlation function (ITCF) as a physically meaningful observable for noisy intermediate-scale quantum (NISQ) devices one that can be extracted using engineered circuits rather than relying on fully random constructions. This is realized by leveraging peaked quantum states whose probability distributions are sharply peaked at specific outcomes due to constructive interference thus offering more efficient verifiability and stronger signal observability. Rather than using Haar-random states, which often yield vanishing signals through destructive interference, we construct purposefully biased quantum states using either Grover-based amplitude amplification or shallow structured circuits. These engineered states amplify contributions from relevant operator subspaces, enabling robust detection of non-zero ITCF values that would otherwise be suppressed under random-state sampling. Our results highlight a problem-specific state preparation framework that mitigates signal loss from random averaging and facilitates the detection of physically meaningful observables in NISQ devices. We also discuss future extensions to multiqubit observables, scrambling diagnostics, and variational circuit optimization, underscoring the broader potential of Peaked States for quantum simulation and verification.

  PublisherDOI

• A deep dive into the interplay of structured quantum peaked circuits and infinite temperature correlation functions

  ArXiv.org · 2025-04-15

  preprintOpen accessSenior author

  Random quantum circuits have been extensively explored for quantum supremacy demonstrations. However, verifying their output distributions remains challenging. Here, we propose the infinite-temperature correlation function (ITCF) as a physically meaningful observable for noisy intermediate-scale quantum (NISQ) devices one that can be extracted using engineered circuits rather than relying on fully random constructions. This is realized by leveraging peaked quantum states whose probability distributions are sharply peaked at specific outcomes due to constructive interference thus offering more efficient verifiability and stronger signal observability. Rather than using Haar-random states, which often yield vanishing signals through destructive interference, we construct purposefully biased quantum states using either Grover-based amplitude amplification or shallow structured circuits. These engineered states amplify contributions from relevant operator subspaces, enabling robust detection of non-zero ITCF values that would otherwise be suppressed under random-state sampling. Our results highlight a problem-specific state preparation framework that mitigates signal loss from random averaging and facilitates the detection of physically meaningful observables in NISQ devices. We also discuss future extensions to multi-qubit observables, scrambling diagnostics, and variational circuit optimization, underscoring the broader potential of Peaked States for quantum simulation and verification.

  PublisherOA PDFDOI

• Quantum Integration of Spiking Leaky Integrate-and-Fire Neurons and Variational Circuits for Enhanced Multiclass Classification

  2025-08-30

  articleSenior author

  Quantum-AI is Ushering in a new scientific revolution, where Quantum Spiking Neural Networks (QSNN) with Quantum Leaky Integrate-and-Fire (QLIF) neurons propel advancements in the modeling of complex biological systems and neural networks. Inspired by the remarkable synergy between quantum mechanics and biological spiking neurons, we present a novel QSNN architecture that leverages QLIF to tackle complex AI tasks. Leveraging entanglement, the quantum variant of SNNs acts as a game-changer, offering faster computation, enhanced accuracy. QLIF Spiking Neurons provide a powerful solution to scalability issues, advancing the potential of AI and quantum technologies in neural networks and complex problem-solving across industries and science. Our approach presents the QLIF model, where neurons integrate incoming signals and fire upon a threshold crossing, creating spikes that interact with quantum circuits to form a quantum-spiking neural network. Our work aims to: (i) enhance computational time and (ii) enrich the learning capabilities of the training model. Our results highlight that QSNN not only accelerates training but also improves accuracy, particularly in deeper quantum circuits. This demonstrates the effectiveness of integrating spiking neural dynamics into quantum models, paving the way for more efficient and biologically inspired quantum learning systems. The proposed approach holds promise for future applications in neuromorphic quantum computing and real-time edge intelligence.

  PublisherDOI

• Quantum Algorithms and Applications for Open Quantum Systems

  Chemical Reviews · 2025-02-04 · 23 citations

  review

  Accurate models for open quantum systems─quantum states that have nontrivial interactions with their environment─may aid in the advancement of a diverse array of fields, including quantum computation, informatics, and the prediction of static and dynamic molecular properties. In recent years, quantum algorithms have been leveraged for the computation of open quantum systems as the predicted quantum advantage of quantum devices over classical ones may allow previously inaccessible applications. Accomplishing this goal will require input and expertise from different research perspectives, as well as the training of a diverse quantum workforce, making a compilation of current quantum methods for treating open quantum systems both useful and timely. In this Review, we first provide a succinct summary of the fundamental theory of open quantum systems and then delve into a discussion on recent quantum algorithms. We conclude with a discussion of pertinent applications, demonstrating the applicability of this field to realistic chemical, biological, and material systems.

  PublisherDOI

• Enhancing Quantum Federated Learning with Fisher Information-Based Optimization

  2025-08-30 · 1 citations

  articleSenior author

  Federated Learning (FL) has become increasingly popular across different sectors, offering a way for clients to work together to train a global model without sharing sensitive data. It involves multiple rounds of communication between the global model and participating clients, which introduces several challenges like high communication costs, heterogeneous client data, prolonged processing times, and increased vulnerability to privacy threats. In recent years, the convergence of federated learning and parameterized quantum circuits has sparked significant research interest, with promising implications for fields such as healthcare and finance. By enabling decentralized training of quantum models, it allows clients or institutions to collaboratively enhance model performance and outcomes while preserving data privacy. Recognizing that Fisher information can quantify the amount of information that a quantum state carries under parameter changes, thereby providing insight into its geometric and statistical properties. We intend to leverage this property to address the aforementioned challenges. In this work, we propose a Quantum Federated Learning (QFL) algorithm that makes use of the Fisher information computed on local client models, with data distributed across heterogeneous partitions. This approach identifies the critical parameters that significantly influence the quantum model's performance, ensuring they are preserved during the aggregation process. Our research assessed the effectiveness and feasibility of QFL by comparing its performance against other variants, and exploring the benefits of incorporating Fisher information in QFL settings. Experimental results on ADNI and MNIST datasets demonstrate the effectiveness of our approach in achieving better performance and robustness against the quantum federated averaging method.

  PublisherDOI

• Quantum Smell: Tunneling Mechanisms in Olfaction

  Molecules · 2025-12-05

  articleOpen accessSenior author

  The mechanism by which odorants are recognized by olfactory receptors remains primarily unresolved. While charge transport is believed to play a significant role, its precise nature is still unclear. Here, we present a novel perspective by exploring the interplay between the intrinsic energy scales of odorant molecules and the gap states that facilitate intermolecular charge transport. We find that odorants act as weak tunneling conductors mainly because of the limited magnitude of electronic coupling between frontier molecular levels. This behavior is further connected to electron-phonon interaction and reorganization energy, suggesting that physically meaningful values for the latter parameter emerge only in the deep off-resonant tunneling regime. These findings complement the swipe card model of olfaction, in which an odorant needs both the right shape to bind to a receptor and the correct vibrational frequency to trigger signal transduction. Moreover, they reveal that the underlying mechanisms are much more complex than previously assumed.

  PublisherOA PDFDOI

Recent grants

• Centers for Chemical Innovation, Phase I: Quantum Information and Computation for Chemistry

  NSF · $1.5M · 2010–2014

• Near Term Applications of Quantum Machine Learning Techniques to Quantum Chemistry

  NSF · $450k · 2020–2024

Frequent coauthors

• Fahhad H. Alharbi

  King Fahd University of Petroleum and Minerals

  113 shared

• Zixuan Hu

  Guangdong University Of Finances and Economics

  72 shared

• D. R. Herschbach

  Harvard University

  63 shared

• Ross D. Hoehn

  61 shared

• Pablo Serra

  Consejo Nacional de Investigaciones Científicas y Técnicas

  57 shared

• Gregory S. Engel

  University of Chicago

  45 shared

• Qi Wei

  China Academy of Engineering Physics

  43 shared

• Shu-Hao Yeh

  National Taiwan University

  40 shared

Labs

• Sabre KaisPI