publications

2026

1. Local strategies are pretty good at computing Boolean properties of quantum sequences

   Tathagata Gupta, Ankith Mohan, Shayeef Murshid, and 3 more authors

   Preprint, 2026

   Abs arXiv

   Quantum memory is a scarce and costly resource, yet little is known about which learning tasks remain feasible under severe memory constraints. We study the problem of computing global properties of quantum sequences when quantum systems must be measured individually, without storing or jointly processing them. In our setting, a bit string x ∈{0, 1}^n is encoded into an n-qubit product state \ket\psi_x_1 ⊗⋯⊗\ket\psi_x_n , and the goal is to infer f(x) ∈{0, 1} from measurements of this quantum encoding. We consider a simple local strategy, which we call the greedy strategy, that applies the same optimal single-system measurement independently to each subsystem and then infers f(x) from the outcomes. Our main result gives a complete characterization of when the greedy strategy is optimal: it achieves the same maximum success probability as an unrestricted global measurement if and only if the target Boolean function is affine (in all but finitely many cases). We establish a universal performance guarantee for general Boolean functions, showing that the success probability of the greedy strategy is always at least the square of the optimal global success probability, in direct analogy with the Barnum-Knill bound for the pretty good measurement. These results demonstrate that even under extreme memory constraints, simple local measurement strategies can remain provably competitive for learning global properties of quantum sequences.

2. Hybrid classical-quantum unsupervised multiclass classification

   Ankith Mohan and Sarvagya Upadhyay

   Mar 2026

   US Patent 12,566,780

   Abs PDF

   A method may include obtaining a multi-dimensional training dataset that includes multiple datums. Each of the datums may correspond to a number of quantum bits (qubits) and may represent a quantum state. The method may also include generating, using a quantum computing device, a Gram matrix based on the multiple datums. In addition, the method may include determining, using a classical computing device, multiple operators according to a constraint defined by the Gram matrix. Each of the operators may be configured as a proxy for a corresponding datum. Further, the method may include assigning, using the classical computing device, each of the operators to a label.

3. Hierarchical quantum decoders

   Nirupam Basak, Ankith Mohan, Andrew Tanggara, and 3 more authors

   Preprint, 2026

   Abs arXiv

   Decoders are a critical component of fault-tolerant quantum computing. They must identify errors based on syndrome measurements to correct quantum states. While finding the optimal correction is NP-hard and thus extremely difficult, approximate decoders with faster runtime often rely on uncontrolled heuristics. In this work, we propose a family of hierarchical quantum decoders with a tunable trade-off between speed and accuracy while retaining guarantees of optimality. We use the Lasserre Sum-of-Squares (SOS) hierarchy from optimization theory to relax the decoding problem. This approach creates a sequence of Semidefinite Programs (SDPs). Lower levels of the hierarchy are faster but approximate, while higher levels are slower but more accurate. We demonstrate that even low levels of this hierarchy significantly outperform standard Linear Programming relaxations. Our results on rotated surface codes and honeycomb color codes show that the SOS decoder approaches the performance of exact decoding. We find that Levels 2 and 3 of our hierarchy perform nearly as well as the exact solver. We analyze the convergence using rank-loop criteria and compare the method against other relaxation schemes. This work bridges the gap between fast heuristics and rigorous optimal decoding.

4. A hybrid quantum-classical algorithm for Bayes-optimal quantum state discrimination using the source code

   Ankith Mohan, Jamie Sikora, and Sarvagya Upadhyay

   2026

   Abs arXiv

   Quantum state discrimination is a fundamental primitive in quantum information processing, underpinning tasks in quantum communication, sensing, and learning. We consider the general Bayes framework, as introduced by Helstrom, for state discrimination when, instead of a classical description of the candidate states, one has access to their source code: the quantum circuit that prepares them. We show that the semidefinite program (SDP) for the discrimination problem can be reformulated in terms of the Gram matrix of these states, reducing the SDP variable dimensions from d L to N L, where d is the Hilbert space dimension, N is the number of candidate states, and L is the number of possible guesses. Importantly, we further introduce a quantum pre-processing procedure which efficiently constructs the reduced semidefinite program from the source code, enabling our method to operate directly on quantum data. We consider two applications. First, we characterize the optimal identifications for quantum changepoint problems under several reward structures, including multiple-changepoint settings that were previously computationally inaccessible. Second, we consider a quantum error classification problem and show how our reduction makes it tractable for systems of hundreds of qubits.

2025

1. Quantum heuristics for linear optimization over large separable operators

   Ankith Mohan, Tobias Haug, Kishor Bharti, and 1 more author

   In 2025 IEEE 7th International Conference on Trust, Privacy and Security in Intelligent Systems, and Applications (TPS-ISA), 2025

   Abs arXiv PDF

   Optimizing over separable quantum objects is challenging for two key reasons: determining separability is NP-hard, and the dimensionality of the problem grows exponentially with the number of qubits. We address both challenges by introducing a heuristic algorithm that leverages a quantum co-processor to significantly reduce the problem’s dimensionality. We then numerically demonstrate that see-saw type optimization performs well in lower dimensional settings. A notable feature of our approach is that it yields feasible solutions, not just bounds on the optimal value, in contrast to many outer approximation based methods. We apply our method to the problem of finding separable states with minimal energy for a given Hamiltonian and use this to define an entanglement measure for its ground space. Finally, we demonstrate how our approach can approximate the separable ground energy of Hamiltonians up to 28 qubits.

2. Approximate dynamical quantum error-correcting codes

   Nirupam Basak, Andrew Tanggara, Ankith Mohan, and 2 more authors

   Preprint, 2025

   Abs arXiv

   Quantum error correction plays a critical role in enabling fault-tolerant quantum computing by protecting fragile quantum information from noise. While general-purpose quantum error correction codes are designed to address a wide range of noise types, they often require substantial resources, making them impractical for near-term quantum devices. Approximate quantum error correction provides an alternative by tailoring codes to specific noise environments, reducing resource demands while still maintaining noise-robustness. Dynamical codes, including Floquet codes, introduce a dynamic approach to quantum error correction, employing time-dependent operations to stabilize logical qubits. In this work, we combine the flexibility of dynamical codes with the versatility of approximate quantum error correction to offer a promising avenue for addressing dominant noise in quantum systems. We construct several approximate dynamical codes using the recently developed strategic code framework. As a special case, we recover the approximate static codes widely studied in the existing literature. By analyzing these approximate dynamical codes through semidefinite programming, we establish the uniqueness and robustness of the optimal encoding, decoding, and check measurements. We also develop a temporal Petz recovery map suited to approximate dynamical codes.

2024

1. InternalInspector I^2: Robust confidence estimation in LLMs through internal states

   Mohammad Beigi, Ying Shen, Runing Yang, and 7 more authors

   In Findings of the association for computational linguistics: EMNLP 2024, 2024

   Abs arXiv PDF

   Despite their vast capabilities, Large Language Models (LLMs) often struggle with generating reliable outputs, frequently producing high-confidence inaccuracies known as hallucinations. Addressing this challenge, our research introduces InternalInspector, a novel framework designed to enhance confidence estimation in LLMs by leveraging contrastive learning on internal states including attention states, feed-forward states, and activation states of all layers. Unlike existing methods that primarily focus on the final activation state, InternalInspector conducts a comprehensive analysis across all internal states of every layer to accurately identify both correct and incorrect prediction processes. By benchmarking InternalInspector against existing confidence estimation methods across various natural language understanding and generation tasks, including factual question answering, commonsense reasoning, and reading comprehension, InternalInspector achieves significantly higher accuracy in aligning the estimated confidence scores with the correctness of the LLM’s predictions and lower calibration error. Furthermore, InternalInspector excels at HaluEval, a hallucination detection benchmark, outperforming other internal-based confidence estimation methods in this task.

2. Quantum state exclusion through offset measurement

   Caleb McIrvin, Ankith Mohan, and Jamie Sikora

   Physical Review A, 2024

   Abs arXiv PDF

   The quantum state discrimination problem has Alice sending a quantum state to Bob who wins if he correctly identifies the state. The pretty good measurement, also known as the square root measurement, performs pretty well at this task. We study the version of this problem where Bob tries to lose with the greatest probability possible (which is harder than it sounds). We define the pretty bad measurement, which performs pretty well at this task, or in other words, pretty poorly for the original task. We show that both the pretty good measurement and the pretty bad measurement are always no worse than blind guessing at their respective tasks. As an application, we apply the pretty bad measurement to the quantum state anomaly detection problem and show how to avoid pretty bad qubits.

3. Identification of influential nodes in graph datasets using combinatorial optimization formulations

   Xiaoyuan Liu, Ankith Mohan, and Sarvagya Upadhyay

   Jul 2024

   US Patent App. 18/156,330

   Abs PDF

   A method may include obtaining a graph dataset that represents a social network and includes a plurality of nodes. The method may include partitioning the graph dataset into a first node cluster and a second node cluster in which the first node cluster and the second node cluster each include one or more nodes from the graph dataset. The method may include identifying one or more first candidate influential nodes included in the first node cluster and one or more second candidate influential nodes included in the second node cluster. The method may include selecting one or more of the first candidate influential nodes and the second candidate influential nodes as influencer nodes and identifying a respective object included in the social network corresponding to each of the influencer nodes.

2021

1. Graph signal recovery using restricted Boltzmann machines

   Ankith Mohan, Aiichiro Nakano, and Emilio Ferrara

   Expert Systems with Applications, 2021

   Abs arXiv PDF

   We propose a model-agnostic pipeline to recover graph signals from an expert system by exploiting the content addressable memory property of restricted Boltzmann machine and the representational ability of a neural network. The proposed pipeline requires the deep neural network that is trained on a downward machine learning task with clean data, data which is free from any form of corruption or incompletion. We show that denoising the representations learned by the deep neural networks is usually more effective than denoising the data itself. Although this pipeline can deal with noise in any dataset, it is particularly effective for graph-structured datasets.

2020

1. Boltzmann machine modeling of layered MoS2 synthesis on a quantum annealer

   Jeremy Liu, Ankith Mohan, Rajiv K Kalia, and 4 more authors

   Computational Materials Science, 2020

   Abs PDF

   Two-dimensional layered materials are usually synthesized by chemical vapor deposition (CVD). Here we use the Restricted Boltzmann Machine (RBM) and its enhancement called Limited Boltzmann Machine (LBM) on a quantum computer to model the chemical vapor deposition (CVD) growth data for a MoS2 monolayer. The CVD data for Boltzmann machines are generated by reactive molecular dynamics (RMD) simulations. The Boltzmann machines give insight into the semiconducting (2H) and metallic (1T) phases and defects generated during the CVD growth of MoS2. We compare the performances of the two types of Boltzmann machine models, i.e., RBM versus augmented topologies with limited intra-layer coupling between the hidden units (LBM). We show that limited topologies have a performance advantage over restricted topologies. We examine connectivity within our topological variants, explore hardware qubit mapping schemes, and discuss what performance differences may imply about locality within the data without prior knowledge.

2018

1. Practical social network analysis with python

   Krishna Raj PM, Ankith Mohan, and KG Srinivasa

   2018

   Abs HTML

   This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.

2017

1. Performance of procedures for identifying influentials in a social network: prediction of time and memory usage as a function of network properties

   PM Krishnaraj, Ankith Mohan, and KG Srinivasa

   Social Network Analysis and Mining, 2017

   Abs HTML

   Identification of influential nodes in a social network is an interesting problem since these nodes assist in faster information propagation in the network. In the current work, the procedures based on Cole and Weiss are used to identify influentials in networks of various sizes and their performances are analyzed. Further it is established that preprocessing techniques like clustering and sampling improve the performance of Cole and Weiss procedures. Finally, the computational resources required for these procedures are estimated based on network properties.