72 citations · 346 across the 20 of their papers we have counts for
20 papers
OrganiQ: Mitigating Classical Resource Bottlenecks of Quantum Generative Adversarial Networks on NISQ-Era Machines
Daniel Silver, Tirthak Patel, Aditya Ranjan +2
Driven by swift progress in hardware capabilities, quantum machine learning has emerged as a research area of interest. Recently, quantum image generation has produced promising re…
Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures
Daniel Silver, Tirthak Patel, Devesh Tiwari
As quantum computing architecture matures, it is important to investigate new technologies that lend unique advantages. In this work, we propose, Qompose, a neutral atom quantum co…
SLIQ: Quantum Image Similarity Networks on Noisy Quantum Computers
Daniel Silver, Tirthak Patel, Aditya Ranjan +3
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have…
QUILT: Effective Multi-Class Classification on Quantum Computers Using an Ensemble of Diverse Quantum Classifiers
Daniel Silver, Tirthak Patel, Devesh Tiwari
Quantum computers can theoretically have significant acceleration over classical computers; but, the near-future era of quantum computing is limited due to small number of qubits t…
MosaiQ: Quantum Generative Adversarial Networks for Image Generation on NISQ Computers
Daniel Silver, Tirthak Patel, William Cutler +3
Quantum machine learning and vision have come to the fore recently, with hardware advances enabling rapid advancement in the capabilities of quantum machines. Recently, quantum ima…
Toward Privacy in Quantum Program Execution On Untrusted Quantum Cloud Computing Machines for Business-sensitive Quantum Needs
Tirthak Patel, Daniel Silver, Aditya Ranjan +3
Quantum computing is an emerging paradigm that has shown great promise in accelerating large-scale scientific, optimization, and machine-learning workloads. With most quantum compu…
Toward Sustainable HPC: Carbon Footprint Estimation and Environmental Implications of HPC Systems
Baolin Li, Rohan Basu Roy, Daniel Wang +3
The rapid growth in demand for HPC systems has led to a rise in carbon footprint, which requires urgent intervention. In this work, we present a comprehensive analysis of the carbo…
Clover: Toward Sustainable AI with Carbon-Aware Machine Learning Inference Service
Baolin Li, Siddharth Samsi, Vijay Gadepally +1
This paper presents a solution to the challenge of mitigating carbon emissions from hosting large-scale machine learning (ML) inference services. ML inference is critical to modern…
CHARTER: Identifying the Most-Critical Gate Operations in Quantum Circuits via Amplified Gate Reversibility
Tirthak Patel, Daniel Silver, Devesh Tiwari
When quantum programs are executed on noisy intermediate-scale quantum (NISQ) computers, they experience hardware noise; consequently, the program outputs are often erroneous. To m…
KAIROS: Building Cost-Efficient Machine Learning Inference Systems with Heterogeneous Cloud Resources
Baolin Li, Siddharth Samsi, Vijay Gadepally +1
Online inference is becoming a key service product for many businesses, deployed in cloud platforms to meet customer demands. Despite their revenue-generation capability, these ser…
RIBBON: Cost-Effective and QoS-Aware Deep Learning Model Inference using a Diverse Pool of Cloud Computing Instances
Baolin Li, Rohan Basu Roy, Tirthak Patel +3
Deep learning model inference is a key service in many businesses and scientific discovery processes. This paper introduces RIBBON, a novel deep learning inference serving system t…
MISO: Exploiting Multi-Instance GPU Capability on Multi-Tenant Systems for Machine Learning
Baolin Li, Tirthak Patel, Siddarth Samsi +2
GPU technology has been improving at an expedited pace in terms of size and performance, empowering HPC and AI/ML researchers to advance the scientific discovery process. However,…
Great Power, Great Responsibility: Recommendations for Reducing Energy for Training Language Models
Joseph McDonald, Baolin Li, Nathan Frey +3
The energy requirements of current natural language processing models continue to grow at a rapid, unsustainable pace. Recent works highlighting this problem conclude there is an u…
The MIT Supercloud Workload Classification Challenge
Benny J. Tang, Qiqi Chen, Matthew L. Weiss +24
High-Performance Computing (HPC) centers and cloud providers support an increasingly diverse set of applications on heterogenous hardware. As Artificial Intelligence (AI) and Machi…
Benchmarking Resource Usage for Efficient Distributed Deep Learning
Nathan C. Frey, Baolin Li, Joseph McDonald +6
Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains. Neural architecture searches, hyperparameter sweeps, and rap…
Robust and Resource-Efficient Quantum Circuit Approximation
Tirthak Patel, Ed Younis, Costin Iancu +2
We present QEst, a procedure to systematically generate approximations for quantum circuits to reduce their CNOT gate count. Our approach employs circuit partitioning for scalabili…
The MIT Supercloud Dataset
Siddharth Samsi, Matthew L Weiss, David Bestor +24
Artificial intelligence (AI) and Machine learning (ML) workloads are an increasingly larger share of the compute workloads in traditional High-Performance Computing (HPC) centers a…
DisQ: A Novel Quantum Output State Classification Method on IBM Quantum Computers using OpenPulse
Tirthak Patel, Devesh Tiwari
Superconducting quantum computing technology has ushered in a new era of computational possibilities. While a considerable research effort has been geared toward improving the quan…
Two stage cluster for resource optimization with Apache Mesos
Gourav Rattihalli, Pankaj Saha, Madhusudhan Govindaraju +1
As resource estimation for jobs is difficult, users often overestimate their requirements. Both commercial clouds and academic campus clusters suffer from low resource utilization…
Measuring and Managing Answer Quality for Online Data-Intensive Services
Jaimie Kelley, Christopher Stewart, Nathaniel Morris +3
Online data-intensive services parallelize query execution across distributed software components. Interactive response time is a priority, so online query executions return answer…