About

Himanshu Gupta is a Professor in the Department of Computer Science at Stony Brook University. He holds a Ph.D. in Computer Science from Stanford University, obtained in 1999, and a B.Tech. in Computer Science from the Indian Institute of Technology, Bombay, completed in May 1992. His research activities focus on theoretical issues in wireless networking, with recent interests in free-space optical communications and spectrum management. He is involved in teaching courses such as CSE 215, 305, 373, and 532. His work contributes to the understanding and development of wireless networks and algorithms.

Research topics

• Computer Science
• Quantum mechanics
• Physics
• Theoretical computer science
• Parallel computing
• Computer network
• Algorithm

Selected publications

• Learning to Correct Errors in Quantum Circuits via Transformer-Predicted PQCs

  2026-04-06

  articleSenior author

  Practical quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices is fundamentally bottlenecked by hardware imperfections: errors accumulate quickly and can destroy the interference patterns that quantum algorithms rely on. While full quantum error correction promises fault tolerance, its overhead is prohibitive for near-term processors, and many quantum error mitigation techniques trade this limitation for substantial sampling cost, limited applicability, or per-circuit retraining. We propose an active, learning-based circuit-level correction framework that suppresses errors during execution. Our approach interleaves lightweight single-qubit parameterized quantum circuit (PQC) blocks into a target circuit and predicts their corrective rotation angles directly from the circuit's gate sequence. We formalize this setting as a general parameter prediction problem: learn a model that maps circuits to continuous corrective parameters to minimize the expected discrepancy between the ideal and corrected output distributions over the circuit domain. We implement this with a Transformer-Encoder architecture operating on a sliding-window circuit representation, enabling prediction of corrections in a single forward pass. Across diverse random benchmark circuits, the learned predictor achieves zero-shot correction on unseen instances, eliminating expensive per-circuit tuning at inference time. Empirically, our interleaved corrections substantially improve output distribution fidelity, maintaining state fidelities above 0.99 in regimes where uncorrected executions average between 0.3 and 0.5.

  PublisherDOI

• Decisions.jl: Representing and Transforming Decision Problem Classes in Julia

  2026-05-24

  article

  Decisions.jl is an open-source Julia ecosystem for standardized representations of general sequential decision problems. Uniquely, it explicitly represents the dynamic decision networks that underlie problem classes, and therefore supports everything from the most basic Markovian problems to problems with intricate variations on multiagency, observability, constraints, and continuity (among other characteristics). By leveraging the Julia language's just-in-time compilation, Decisions.jl delivers high-performance interfaces in the intuitive style of similar single-model frameworks. In this work, we explore how Decisions.jl can be used to navigate the space of sequential decision problem classes. Decisions.jl enables the transformation of problem formulations into simpler or more complex variants, allowing users to rigorously evaluate tradeoffs between generality and performance in decision-making algorithms. Additionally, Decisions.jl streamlines the usage of multiagent problem formulations that would otherwise be too cumbersome to replicate consistently.

  PublisherDOI

• Distributing Quantum Circuits using Pre-Distributed Entanglement Pairs Over Quantum Networks

  2026-04-06

  articleSenior author

  Distributing quantum circuits across a network of quantum devices is a recent approach to improve the scalability of quantum computation. Entangled pairs of qubits distributed across network nodes are used as a communication resource for executing inter-node (remote) gates. However, establishing these entanglement pairs incurs significant latency, and doing so during circuit execution may lead to decoherence of logical qubits. Instead, it may be beneficial to proactively distribute certain entanglement pairs a priori (before execution begins). Thus, we consider the problem of distributing a quantum circuit over a quantum network with pre-distributed entanglement pairs, and seek to minimize the circuit's execution time. We develop algorithms for (i) allocating a limited predistribution budget across memory pairs (optionally guided by a training-circuit distribution) and (ii) computing a static qubit-to-memory allocation that exploits the resulting pre-distributed resources to reduce execution time. We evaluate the proposed methods in NetSquid under a telegate-based execution model on standard benchmark circuit families.

  PublisherDOI

• AI-Enabled Spectrum Sensing and Allocation in Cognitive Radio Networks

  International Scientific Journal of Engineering and Management · 2026-04-06

  articleSenior author

  Abstract: Cognitive Radio Networks (CRNs) represent a paradigm shift in wireless spectrum management, enabling unlicensed secondary users (SUs) to opportunistically access licensed spectrum bands without causing harmful interference to primary users (PUs). A fundamental challenge in realizing practical CRNs is the design of accurate, low-latency spectrum sensing mechanisms and efficient spectrum allocation policies that can adapt to dynamic radio environments. This paper proposes a unified AI-enabled framework that integrates a Transformer-based cooperative spectrum sensing module with a Soft Actor-Critic (SAC) deep reinforcement learning (DRL) engine for joint spectrum access decision-making. The Transformer encoder processes multi-band spectrum snapshots and computes attention-weighted sensing decisions across a cooperative SU cluster, achieving a probability of detection (Pd) of 0.98 at SNR = -15 dB with a false alarm rate of 0.02. The SAC allocation agent learns a maximum-entropy spectrum access policy that simultaneously maximizes SU throughput and enforces the PU interference temperature limit of -55 dBm. Extensive simulations using ITU-R M.1225 vehicular and Rayleigh fading channel models demonstrate that the proposed framework achieves 83.6% spectrum utilization, an aggregate throughput of 46.2 Mbps for 16 concurrent SUs, and a 33.1 dB reduction in PU interference compared to fixed spectrum allocation. These results represent improvements of 51.4%, 173%, and 21 dB respectively over the threshold energy detection baseline. The proposed system is benchmarked against seven state-of-the-art methods from SCI-indexed literature, consistently outperforming all comparators across detection, allocation, and energy efficiency metrics. Keywords: Cognitive radio networks; Spectrum sensing; Dynamic spectrum access; Deep reinforcement learning; Transformer neural network; Cooperative sensing; Soft Actor-Critic; Primary user protection; 5G spectrum sharing; IEEE 802.22

  PublisherDOI

• Uncomputing Ancilla Qubits in Quantum Circuits

  2025-03-31 · 2 citations

  article

  Uncomputation of ancilla qubits is essential in quantum computing to reset auxiliary qubits to a zero state, ensuring their safe discarding and reducing resource overhead. Current methods for uncomputation either rely on manual procedures or use additional ancilla qubits to store intermediate computation results, which increases qubit storage requirements. Additionally, most in-place uncomputation techniques employ an all-or-nothing strategy, wherein they perform uncomputation only when all ancilla qubits in the given circuit can be uncomputed.In this work, we tackle the problem of uncomputing a maximum number of ancilla qubits in a quantum circuit with minimal qubit storage overhead. We propose a novel approach where partial uncomputation—reverting the last ’m’ gates—enables the uncomputation of more qubits. We present three algorithms designed to uncompute the largest possible set of ancilla qubits and demonstrate their effectiveness through experiments on various randomly generated quantum circuits.

  PublisherDOI

• DQC-QR: Distributing and Routing Quantum Circuits with Minimum Execution Time

  ACM Transactions on Quantum Computing · 2025-07-30

  article

  Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is a promising way to circumvent these challenges and facilitate large quantum computations. However, distributed quantum computations require entanglements (to execute remote gates) which can incur significant generation latency and, thus, lead to decoherence of qubits. In this work, we consider the problem of distributing quantum circuits across a quantum network to minimize the execution time. The problem entails mapping the circuit qubits to network memories, including within each computer since limited connectivity within computers can affect the circuit execution time. We provide two-step solutions for the above problem: In the first step, we allocate qubits to memories to minimize the estimated execution time; for this step, we design an efficient algorithm based on an approximation algorithm for the max-quadratic-assignment problem. In the second step, we determine an efficient execution scheme, including generating required entanglements with minimum latency under the network resource and decoherence constraints; for this step, we develop two algorithms with appropriate performance guarantees under certain settings or assumptions. We consider multiple protocols for executing remote gates, viz., telegates and cat-entanglements. With extensive simulations over NetSquid, a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform a scheme based on prior work by 40 to 50% on average and up to 95% in some cases.

  PublisherDOI

• Distribution and Purification of Entanglement States in Quantum Networks

  2025-03-31 · 5 citations

  article

  We consider problems of distributing high-fidelity entangled states across nodes of a quantum network. We consider a repeater-based network architecture with entanglement swapping (fusion) operations for generating long-distance entanglements, and purification operations that produce high-fidelity states from several lower-fidelity states. The contributions of this paper are two-fold: First, while there have been several works on fidelity-aware routing and incorporating purification into routing for generating EPs, this paper presents the first algorithms for optimal solutions to the high-fidelity EP distribution problem. We provide a dynamic programming algorithm for generating the optimal tree of operations to produce a high-fidelity EP, and an LP-based algorithm for generating an optimal collection of trees. Second, following the EP algorithms, this paper presents the first algorithms for the high-fidelity GHZ-state distribution problem and characterizes its optimality. We evaluate our techniques via simulations over NetSquid, a quantum network simulator.

  PublisherDOI

• Efficient Execution of Multiple Quantum Circuits Over a Quantum Network

  2025-08-30 · 2 citations

  articleSenior author

  As quantum computing continues to scale, the ability to execute quantum circuits across distributed quantum networks is becoming increasingly important. While prior work has largely focused on distributing a single circuit to optimize the number of entanglement pairs (EPs) used or the execution time, future applications will require the efficient scheduling and execution of multiple circuits on a shared quantum network. Therefore, we study the problem of efficiently distributing multiple quantum circuits across a shared quantum network under decoherence and network constraints and seek to minimize the execution time required to execute all circuits (makespan). Solving the above problem involves jointly determining when and where each circuit should be executed, and how to schedule concurrent EP generation required to execute remote gates. We propose several algorithmic approaches for this multi-circuit distribution problem and provide theoretical performance guarantees for special cases. To assess the practical effectiveness of our methods, we conduct extensive simulations using the NetSquid quantum network simulator.

  PublisherDOI

• Optimized Distribution of Entanglement Graph States in Quantum Networks

  IEEE Transactions on Quantum Engineering · 2025-01-01 · 9 citations

  articleOpen access

  Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum networks can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, quantum networks can enable fully secured long-distance communication. Thus, quantum networks lie at the heart of the success of future quantum information technologies. In quantum networks, multipartite entangled states distributed over the network help implement and support many quantum network applications for communications, sensing, and computing. Our work focuses on developing optimal techniques to generate and distribute multipartite entanglement states efficiently. Prior works on generating general multipartite entanglement states have focused on the objective of minimizing the number of maximally entangled pairs while ignoring the heterogeneity of the network nodes and links as well as the stochastic nature of underlying processes. In this work, we develop a hypergraph-based linear programming framework that delivers optimal (under certain assumptions) generation schemes for general multipartite entanglement represented by graph states, under the network resources, decoherence, and fidelity constraints, while considering the stochasticity of the underlying processes. We illustrate our technique by developing generation schemes for the special cases of path and tree graph states and discuss optimized generation schemes for more general classes of graph states. Using extensive simulations over a quantum network simulator, we demonstrate the effectiveness of our developed techniques and show that they outperform prior known schemes by up to orders of magnitude.

  PublisherDOI

• Machine Learning Based Energy Consumption Modeling of Machining Process-approach for Sustainability

  Evergreen · 2025-03-01 · 1 citations

  articleOpen access

  According to U.S. Energy Information Administration (EIA) energy consumption and carbon emission are projected to increase globally.In metal manufacturing there is continuous increase in energy demand and carbon emission.Machining is one of them which is widely used in discrete manufacturing sector.The performance of the machining process depends upon cutting parameters.These parameters are selected based on various criteria like minimum cost, maximum production rate, maximum material removal rate, and minimum energy consumption.For this, experiments have been conducted and based on collected data of input parameters cutting speed ,feed and depth of cut and corresponding energy consumption, modeling has been done.In the present work algorithms related to machine learning have been applied to the experimental data for modeling to predict energy consumption The performance of algorithms has been compared using mean squared error (MSE), mean absolute error (MAE) and R 2 value.The four favorable algorithms have been compared in terms of accuracy with the experimental data.Among the models tested, the Lasso Regression with Cross-Validation performed the best with lowest mean squared error (MSE) 158.9, mean absolute error (MAE) 8.37 and highest R 2 value 0.991.

  PublisherOA PDFDOI

Recent grants

• NeTS: Small: A Wireless Backhaul for Multi-Gigabit Picocells Using Steerable Free Space Optics

  NSF · $499k · 2018–2021

• NOSS: Declarative Framework for Learning and Evaluating Probabilistic Models of Events in Sensor Networks

  NSF · $377k · 2007–2011

• Collaborative Research: FET: Medium: Robust Quantum Networks via Efficient Entanglement Distribution

  NSF · $750k · 2021–2026

• NOSS: Airborne Video Sensor Networks for Surveillance and Emergency Response

  NSF · $528k · 2007–2012

• CNS Core: Small: Secured Spectrum Allocation and Patrolling in Shared Spectrum Systems

  NSF · $415k · 2021–2024

Frequent coauthors

• Samir R. Das

  Stony Brook University

  21 shared

• Caitao Zhan

  Stony Brook University

  12 shared

• Bin Tang

  Ministry of Water Resources of the People's Republic of China

  10 shared

• Chitta Baral

  Arizona State University

  8 shared

• Mark Hillery

  8 shared

• Mahmoud Al‐Ayyoub

  Jordan University of Science and Technology

  7 shared

• Giordano Fusco

  Stony Brook University

  7 shared

• Xianjin Zhu

  Harbin Institute of Technology

  6 shared