1 citations · 2 across the 15 of their papers we have counts for
17 papers
Predicting Collision Cross Sections with GRACE: Geometric Residual Adduct Conditioning via Early-fusion
Parthasarathy Suryanarayanan, Susanta Das, Shreyans Sethi +2
Collision cross section (CCS), derived from ion mobility mass spectrometry, is a common descriptor for molecular annotation. Prediction is challenging for machine learning models b…
Quantum-Centric Geometry Optimization with Wave-Function-Based Embedding
Danil Kaliakin, Akhil Shajan, Fangchun Liang +2
The EWF-(FCI,SQD) method, a wave-function-based embedding approach combining full configuration interaction (FCI) and sample-based quantum diagonalization (SQD), is a promising new…
Quantum Computing Enabled ab initio Molecular Dynamics Simulations
Susanta Das, Subhamoy Bhowmik, Zhen Li +4
We demonstrate a quantum-classical workflow for ab initio molecular dynamics (AIMD) in which quantum measurements from a chemistry-inspired LUCJ ansatz are post-processed using Sam…
Quantum Computations on Fusion Blanket Molten Salts
Susanta Das, Thiago J. Pinheiro Dos Santos, Subhamoy Bhowmik +14
Molten salts such as FLiBe (2LiF--BeF) are leading blanket materials for breeding and recovering tritium in fusion reactors. Predicting tritium speciation requires accurate ele…
I-QMapper: Error-Aware Layout Optimization and Device Diagnostics for NISQ Hardware
Milana Bazayeva, Kenneth Merz
Achieving high-fidelity execution on noisy intermediate-scale quantum (NISQ) hardware requires careful selection of physical qubit layouts, as gate errors, readout errors, and cohe…
A Quantum Multi-Programming Framework to Maximize Quantum Resources for the LUCJ Ansatz
Milana Bazayeva, Abigail McClain Gomez, Kenneth M. Merz
In the context of quantum computing, efficient resource management is crucial for optimizing throughput on cloud-based platforms and maximizing hardware utilization. In the present…
Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes
Kenneth M. Merz, Akhil Shajan, Danil Kaliakin +21
We develop a workflow decomposing a molecule into fragments via quantum embedding and simulating them with a heterogeneous quantum-classical (HQC) method. We sample fragment electr…
Protein-Ligand Free Energy Perturbation on Quantum Hardware
Zhen Li, Milana Bazayeva, Thaddeus Pellegrini +6
The use of free energy perturbation (FEP) methods to study protein-ligand complexes is one of the most important tools in structure-based drug design. Because FEP methods typically…
Molecular Quantum Computations on a Protein
Akhil Shajan, Danil Kaliakin, Fangchun Liang +7
This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow…
Domain-Aware Quantum Circuit for QML
Gurinder Singh, Thaddeus Pellegrini, Kenneth M. Merz
Designing parameterized quantum circuits (PQCs) that are expressive, trainable, and robust to hardware noise is a central challenge for quantum machine learning (QML) on noisy inte…
Improved parameter initialization for the (local) unitary cluster Jastrow ansatz
Wan-Hsuan Lin, Fangchun Liang, Mario Motta +3
The unitary cluster Jastrow (UCJ) ansatz and its variant known as local UCJ (LUCJ) are promising choices for variational quantum algorithms for chemistry due to their combination o…
Quantum Algorithm for Protein Structure Prediction Using the Face-Centered Cubic Lattice
Rui-Hao Li, Hakan Doga, Bryan Raubenolt +16
In this work, we present the first implementation of the face-centered cubic (FCC) lattice model for protein structure prediction with a quantum algorithm. Our motivation to encode…
Improving GANs by leveraging the quantum noise from real hardware
Hongni Jin, Kenneth M. Merz
We propose a novel approach to generative adversarial networks (GANs) in which the standard i.i.d. Gaussian latent prior is replaced or hybridized with a quantum-correlated prior d…
Quantum-Centric Alchemical Free Energy Calculations
Milana Bazayeva, Zhen Li, Danil Kaliakin +4
In the present work, we present a hybrid quantum-classical workflow aimed at improving the accuracy of alchemical free energy (AFE) predictions by incorporating configuration inter…
QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations
Hongni Jin, Gurinder Singh, Kenneth M. Merz
Implicit neural representations have shown potential in various applications. However, accurately reconstructing the image or providing clear details via image super-resolution rem…
Benchmarking MedMNIST dataset on real quantum hardware
Gurinder Singh, Hongni Jin, Kenneth M. Merz
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work,…
Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions
Hongni Jin, Kenneth M. Merz
A key step in interpreting gas-phase ion mobility coupled with mass spectrometry (IM-MS) data for unknown structure prediction involves identifying the most favorable protonated st…