Swaroop Ghosh
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View article: MITS: A Quantum Sorcerer’s Stone for Designing Surface Codes
MITS: A Quantum Sorcerer’s Stone for Designing Surface Codes Open
In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to d…
View article: Design Automation in Quantum Error Correction
Design Automation in Quantum Error Correction Open
Quantum error correction (QEC) underpins practical fault-tolerant quantum computing (FTQC) by addressing the fragility of quantum states and mitigating decoherence-induced errors. As quantum devices scale, integrating robust QEC protocols …
View article: Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses
Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses Open
Quantum Machine Learning (QML) integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Nois…
View article: Forensics of Error Rates of Quantum Hardware
Forensics of Error Rates of Quantum Hardware Open
There has been a rise in third-party cloud providers offering quantum hardware as a service to improve performance at lower cost. Although these providers provide flexibility to the users to choose from several qubit technologies, quantum …
View article: Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit
Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit Open
We propose Quantum Snapshot with Dynamic Circuit (QSDC), a hardware-agnostic, learning-driven framework for capturing quantum snapshots: non-destructive estimates of quantum states at arbitrary points within a quantum circuit, which can th…
View article: Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots
Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots Open
Circuit compilation, a crucial process for adapting quantum algorithms to hardware constraints, often operates as a ``black box,'' with limited visibility into the optimization techniques used by proprietary systems or advanced open-source…
View article: Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise Open
Quantum error correction codes (QECCs) are essential for reliable quantum computing as they protect quantum states against noise and errors. Limited research has explored the resilience of QECCs to biased noise, critical for selecting opti…
View article: Survival of the Optimized: An Evolutionary Approach to T-depth Reduction
Survival of the Optimized: An Evolutionary Approach to T-depth Reduction Open
Quantum Error Correction (QEC) is the cornerstone of practical Fault-Tolerant Quantum Computing (FTQC), but incurs enormous resource overheads. Circuits must decompose into Clifford+T gates, and the non-transversal T gates demand costly ma…
View article: Impact of Error Rate Misreporting on Resource Allocation in Multi-tenant Quantum Computing and Defense
Impact of Error Rate Misreporting on Resource Allocation in Multi-tenant Quantum Computing and Defense Open
Cloud-based quantum service providers allow multiple users to run programs on shared hardware concurrently to maximize resource utilization and minimize operational costs. This multi-tenant computing (MTC) model relies on the error paramet…
View article: Lattice Surgery for Dummies
Lattice Surgery for Dummies Open
Quantum error correction (QEC) plays a crucial role in correcting noise and paving the way for fault-tolerant quantum computing. This field has seen significant advancements, with new quantum error correction codes emerging regularly to ad…
View article: Quantum Computing and Cybersecurity Education: A Novel Curriculum for Enhancing Graduate STEM Learning
Quantum Computing and Cybersecurity Education: A Novel Curriculum for Enhancing Graduate STEM Learning Open
Quantum computing is an emerging paradigm with the potential to transform numerous application areas by addressing problems considered intractable in the classical domain. However, its integration into cyberspace introduces significant sec…
View article: The Art of Optimizing T-Depth for Quantum Error Correction in Large-Scale Quantum Computing
The Art of Optimizing T-Depth for Quantum Error Correction in Large-Scale Quantum Computing Open
Quantum Error Correction (QEC), combined with magic state distillation, ensures fault tolerance in large-scale quantum computation. To apply QEC, a circuit must first be transformed into a non-Clifford (or T) gate set. T-depth, the number …
View article: AI-Driven Industrial Robotics: Revolutionizing Automation with Machine Learning and Intelligent Adaptation
AI-Driven Industrial Robotics: Revolutionizing Automation with Machine Learning and Intelligent Adaptation Open
The domain of industrial robotics is experiencing a significant and continuous expansion. With the advancements in artificial intelligence(AI) and machine learning(ML), the strategies for creating and controlling robots have gained paramou…
View article: The Q-Spellbook: Crafting Surface Code Layouts and Magic State Protocols for Large-Scale Quantum Computing
The Q-Spellbook: Crafting Surface Code Layouts and Magic State Protocols for Large-Scale Quantum Computing Open
Quantum error correction is a cornerstone of reliable quantum computing, with surface codes emerging as a prominent method for protecting quantum information. Surface codes are efficient for Clifford gates but require magic state distillat…
View article: Quantum Quandaries: Unraveling Encoding Vulnerabilities in Quantum Neural Networks
Quantum Quandaries: Unraveling Encoding Vulnerabilities in Quantum Neural Networks Open
Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. Howeve…
View article: Advanced Digital Signal Processing, Interpolation and Extrapolation Based Symmetrical Fault Assessment in Transmission Line
Advanced Digital Signal Processing, Interpolation and Extrapolation Based Symmetrical Fault Assessment in Transmission Line Open
This article describes a novel method for locating and detecting triple line to ground (LLL-G) faults in 110 km of 132 kV transmission lines. To find the transmission line fault, the three phase system currents were first recorded at the a…
View article: Forensics of Transpiled Quantum Circuits
Forensics of Transpiled Quantum Circuits Open
Many third-party cloud providers set up quantum hardware as a service that includes a wide range of qubit technologies and architectures to maximize performance at minimal cost. However, there is little visibility to where the execution of…
View article: Optimizing Quantum Embedding using Genetic Algorithm for QML Applications
Optimizing Quantum Embedding using Genetic Algorithm for QML Applications Open
Quantum Embeddings (QE) are essential for loading classical data into quantum systems for Quantum Machine Learning (QML). The performance of QML algorithms depends on the type of QE and how features are mapped to qubits. Traditionally, the…
View article: Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era
Adversarial Data Poisoning Attacks on Quantum Machine Learning in the NISQ Era Open
With the growing interest in Quantum Machine Learning (QML) and the increasing availability of quantum computers through cloud providers, addressing the potential security risks associated with QML has become an urgent priority. One key co…
View article: Quantum Prometheus: Defying Overhead with Recycled Ancillas in Quantum Error Correction
Quantum Prometheus: Defying Overhead with Recycled Ancillas in Quantum Error Correction Open
Quantum error correction (QEC) is crucial for ensuring the reliability of quantum computers. However, implementing QEC often requires a significant number of qubits, leading to substantial overhead. One of the major challenges in quantum c…
View article: Watermarking of Quantum Circuits
Watermarking of Quantum Circuits Open
Quantum circuits constitute Intellectual Property (IP) of the quantum developers and users, which needs to be protected from theft by adversarial agents, e.g., the quantum cloud provider or a rogue adversary present in the cloud. This nece…
View article: AI-driven Reverse Engineering of QML Models
AI-driven Reverse Engineering of QML Models Open
Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the research of QML models, there is a rise in third-party quantum c…
View article: Trustworthy and reliable computing using untrusted and unreliable quantum hardware
Trustworthy and reliable computing using untrusted and unreliable quantum hardware Open
Security and reliability are primary concerns in any computing paradigm, including quantum computing. Currently, users can access quantum computers through a cloud-based platform where they can run their programs on a suite of quantum comp…
View article: Security Concerns in Quantum Machine Learning as a Service
Security Concerns in Quantum Machine Learning as a Service Open
Quantum machine learning (QML) is a category of algorithms that employ variational quantum circuits (VQCs) to tackle machine learning tasks. Recent discoveries have shown that QML models can effectively generalize from limited training dat…
View article: Quantum Data Breach: Reusing Training Dataset by Untrusted Quantum Clouds
Quantum Data Breach: Reusing Training Dataset by Untrusted Quantum Clouds Open
Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. Howeve…
View article: The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models
The Quantum Imitation Game: Reverse Engineering of Quantum Machine Learning Models Open
Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Int…
View article: STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud
STIQ: Safeguarding Training and Inferencing of Quantum Neural Networks from Untrusted Cloud Open
The high expenses imposed by current quantum cloud providers, coupled with the escalating need for quantum resources, may incentivize the emergence of cheaper cloud-based quantum services from potentially untrusted providers. Deploying or …
View article: QuaLITi: Quantum Machine Learning Hardware Selection for Inferencing with Top-Tier Performance
QuaLITi: Quantum Machine Learning Hardware Selection for Inferencing with Top-Tier Performance Open
Quantum Machine Learning (QML) is an accelerating field of study that leverages the principles of quantum computing to enhance and innovate within machine learning methodologies. However, Noisy Intermediate-Scale Quantum (NISQ) computers s…