153 citations · 1.1k across the 43 of their papers we have counts for
44 papers
Discretization-Aware Fine-Tuning for Quantum Machine Learning with Chemical Foundation Models
Shunji Matsuura, Sonika Johri
A key challenge in practical quantum machine learning (QML), particularly for discriminative tasks such as classification, is the limited capacity of near-term quantum devices to e…
Benchmarking Quantum and Classical Machine Learning Models on Oncological Data
Sydney Leither, Thomas Lubinski, Michael Kubal +1
Machine learning is being increasingly used for the detection, diagnosis, and treatment of cancer. However, models often struggle with biological data due to high dimensionality, l…
Platform-Agnostic Modular Architecture for Quantum Benchmarking
Neer Patel, Anish Giri, Hrushikesh Pramod Patil +6
We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit…
How many qubits does a machine learning problem require?
Sydney Leither, Michael Kubal, Sonika Johri
For a machine learning paradigm to be generally applicable, it should have the property of universal approximation, that is, it should be able to approximate any target function to…
A Quantum Platform for Multiomics Data
Michael Kubal, Sonika Johri
The complexity of biological systems, governed by molecular interactions across hierarchical scales, presents a challenge for computational modeling. While advances in multiomic pr…
A Practical Framework for Assessing the Performance of Observable Estimation in Quantum Simulation
Siyuan Niu, Efekan Kökcü, Sonika Johri +5
Simulating dynamics of physical systems is a key application of quantum computing, with potential impact in fields such as condensed matter physics and quantum chemistry. However,…
Bit-bit encoding, optimizer-free training and sub-net initialization: techniques for scalable quantum machine learning
Sonika Johri
Quantum machine learning for classical data is currently perceived to have a scalability problem due to (i) a bottleneck at the point of loading data into quantum states, (ii) the…
A Comprehensive Cross-Model Framework for Benchmarking the Performance of Quantum Hamiltonian Simulations
Avimita Chatterjee, Sonny Rappaport, Anish Giri +5
Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gau…
Quantum Algorithm Exploration using Application-Oriented Performance Benchmarks
Thomas Lubinski, Joshua J. Goings, Karl Mayer +11
The QED-C suite of Application-Oriented Benchmarks provides the ability to gauge performance characteristics of quantum computers as applied to real-world applications. Its benchma…
Tensor Network Based Efficient Quantum Data Loading of Images
Jason Iaconis, Sonika Johri
Image-based data is a popular arena for testing quantum machine learning algorithms. A crucial factor in realizing quantum advantage for these applications is the ability to effici…
Phase transition in magic with random quantum circuits
Pradeep Niroula, Christopher David White, Qingfeng Wang +5
Magic is a property of quantum states that enables universal fault-tolerant quantum computing using simple sets of gate operations. Understanding the mechanisms by which magic is c…
Quantum State Preparation of Normal Distributions using Matrix Product States
Jason Iaconis, Sonika Johri, Elton Yechao Zhu
State preparation is a necessary component of many quantum algorithms. In this work, we combine a method for efficiently representing smooth differentiable probability distribution…
Quantum computation: Efficient network partitioning for large scale critical infrastructures
Saikat Ray Majumder, Annarita Giani, Weiwei Shen +3
Quantum computers are emerging as a viable alternative to tackle certain computational problems that are challenging for classical computers. With the rapid development of quantum…
Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers
Luning Zhao, Joshua Goings, Kenneth Wright +8
Variational quantum eigensolvers (VQE) are among the most promising approaches for solving electronic structure problems on near-term quantum computers. A critical challenge for VQ…
Copula-based Risk Aggregation with Trapped Ion Quantum Computers
Daiwei Zhu, Weiwei Shen, Annarita Giani +3
Copulas are mathematical tools for modeling joint probability distributions. Since copulas enable one to conveniently treat the marginal distribution of each variable and the inter…
Application-Oriented Performance Benchmarks for Quantum Computing
Thomas Lubinski, Sonika Johri, Paul Varosy +6
In this work we introduce an open source suite of quantum application-oriented performance benchmarks that is designed to measure the effectiveness of quantum computing hardware at…
Low depth amplitude estimation on a trapped ion quantum computer
Tudor Giurgica-Tiron, Sonika Johri, Iordanis Kerenidis +6
Amplitude estimation is a fundamental quantum algorithmic primitive that enables quantum computers to achieve quadratic speedups for a large class of statistical estimation problem…
Generative Quantum Learning of Joint Probability Distribution Functions
Elton Yechao Zhu, Sonika Johri, Dave Bacon +9
Modeling joint probability distributions is an important task in a wide variety of fields. One popular technique for this employs a family of multivariate distributions with unifor…
Graph Optimization Perspective for Low-Depth Trotter-Suzuki Decomposition
Albert T. Schmitz, Nicolas P. D. Sawaya, Sonika Johri +1
Hamiltonian simulation represents an important module in a large class of quantum algorithms and simulations such as quantum machine learning, quantum linear algebra methods, and m…
Optimizing Electronic Structure Simulations on a Trapped-ion Quantum Computer using Problem Decomposition
Yukio Kawashima, Erika Lloyd, Marc P. Coons +12
Quantum computers have the potential to advance material design and drug discovery by performing costly electronic structure calculations. A critical aspect of this application req…
Nearest Centroid Classification on a Trapped Ion Quantum Computer
Sonika Johri, Shantanu Debnath, Avinash Mocherla +4
Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum…
Variational preparation of finite-temperature states on a quantum computer
R. Sagastizabal, S. P. Premaratne, B. A. Klaver +13
The preparation of thermal equilibrium states is important for the simulation of condensed-matter and cosmology systems using a quantum computer. We present a method to prepare suc…
Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer
Manuel S. Rudolph, Ntwali Bashige Toussaint, Amara Katabarwa +3
Generating high-quality data (e.g. images or video) is one of the most exciting and challenging frontiers in unsupervised machine learning. Utilizing quantum computers in such task…
Many Body Thermodynamics on Quantum Computers via Partition Function Zeros
Akhil Francis, D. Zhu, C. Huerta Alderete +6
Interacting quantum systems illustrate complex phenomena including phase transitions to novel ordered phases. The universal nature of critical phenomena reduces their description t…