arXiv subjects

Himanshu Thapliyal

Publications and source records attributed to Himanshu Thapliyal.

At least 37 records · Page 2Linked to original sources

arXiv2024

Noise-Resilient and Reduced Depth Approximate Adders for NISQ Quantum Computing

The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics.

quant-ph↗

arXiv2024

Novel Optimized Designs of Modulo $2n+1$ Adder for Quantum Computing

Quantum modular adders are one of the most fundamental yet versatile quantum computation operations. They help implement functions of higher complexity, such as subtraction and multiplication, which are used in applications such as quantum cryptanalysis, quantum image processing, and securing communication. To the best of our knowledge, there is no existing design of quantum modulo $(2n+1)$ adder. In this work, we propose four quantum adders targeted specifically for modulo $(2n+1)$ addition. These adders can provide both regular and modulo $(2n+1)$ sum concurrently, enhancing their application in residue number system based arithmetic. Our first design, QMA1, is a novel quantum modulo $(2n+1)$ adder. The second proposed adder, QMA2, optimizes the utilization of quantum gates within the QMA1, resulting in 37.5% reduced CNOT gate count, 46.15% reduced CNOT depth, and 26.5% decrease in both Toffoli gates and depth. We propose a third adder QMA3 that uses zero resets, a dynamic circuits based feature that reuses qubits, leading to 25% savings in qubit count. Our fourth design, QMA4, demonstrates the benefit of incorporating additional zero resets to achieve a purer zero state, reducing quantum state preparation errors. Notably, we conducted experiments using 5-qubit configurations of the proposed modulo $(2n+1)$ adders on the IBM Washington, a 127-qubit quantum computer based on the Eagle R1 architecture, to demonstrate a 28.8% reduction in QMA1's error of which: (i) 18.63% error reduction happens due to gate and depth reduction in QMA2, and (ii) 2.53% drop in error due to qubit reduction in QMA3, and (iii) 7.64% error decreased due to application of additional zero resets in QMA4.

quant-ph↗

arXiv2024

Residue Number System (RNS) based Distributed Quantum Addition

Quantum Arithmetic faces limitations such as noise and resource constraints in the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers. We propose using Distributed Quantum Computing (DQC) to overcome these limitations by substituting a higher depth quantum addition circuit with Residue Number System (RNS) based quantum modulo adders. The RNS-based distributed quantum addition circuits possess lower depth and are distributed across multiple quantum computers/jobs, resulting in higher noise resilience. We propose the Quantum Superior Modulo Addition based on RNS Tool (QSMART), which can generate RNS sets of quantum adders based on multiple factors such as depth, range, and efficiency. We also propose a novel design of Quantum Diminished-1 Modulo (2n + 1) Adder (QDMA), which forms a crucial part of RNS-based distributed quantum addition and the QSMART tool. We demonstrate the higher noise resilience of the Residue Number System (RNS) based distributed quantum addition by conducting simulations modeling Quantinuum's H1 ion trap-based quantum computer. Our simulations demonstrate that RNS-based distributed quantum addition has 11.36% to 133.15% higher output probability over 6-bit to 10-bit non-distributed quantum full adders, indicating higher noise fidelity. Furthermore, we present a scalable way of achieving distributed quantum addition higher than limited otherwise by the 20-qubit range of Quantinuum H1.

quant-ph↗

arXiv2023

Vulnerabilities and Attacks on CAN-Based 3D Printing/Additive Manufacturing

Recent advancements in 3D-printing/additive manufacturing has brought forth a new interest in the use of Controller Area Network (CAN) for multi-module, plug-and-play bus support for their embedded systems. CAN systems provide a variety of benefits that can outweigh typical conventional wire-loom protocols in many categories. However, implementation of CAN also brings forth vulnerabilities provided by its spoofable, destination-encoded shared communication bus. These vulnerabilities result in undetectable fault injection, packet manipulation, unauthorized packet logging/sniffing, and more. They also provide attackers the capability to manipulate all sensor information, commands, and create unsafe operating conditions using only a single compromised node on the CAN network (bypassing all root-of-trust in the modules). Thus, malicious hardware requires only a connection to the bus for access to all traffic. In this paper, we discuss the effects of repurposed CAN-based attacks capable of manipulating sensor data, overriding systems, and injecting dangerous commands on the Controller Area Network using various entry methods. As a case study, we also showed a spoofing attack on critical data modules within a commercial 3D printer.

cs.CR↗

arXiv2022

CAN Bus: The Future of Additive Manufacturing (3D Printing)

Additive Manufacturing (AM) is gaining renewed popularity and attention due to low-cost fabrication systems proliferating the market. Current communication protocols used in AM limit the connection flexibility between the control board and peripherals; they are often complex in their wiring and thus restrict their avenue of expansion. Thus, the Controller Area Network (CAN) bus is an attractive pathway for inter-hardware connections due to its innate quality. However, the combination of CAN and AM is not well explored and documented in existing literature. This article aims to provide examples of CAN bus applications in AM.

cs.NI↗

arXiv2022

Everything You Always Wanted to Know About Quantum Circuits

In this work, we provide an overview of circuits for quantum computing. We introduce gates used in quantum computation and then present resource cost measurements used to evaluate circuits made from these gates. We then illustrate how the gates shown are then combined into quantum circuits for basic arithmetic functions. Architectures for addition, subtraction, multiplication, and division are shown. We demonstrate how to calculate the resource costs of quantum circuits. We conclude this overview with by illustrating an application of the elementary quantum circuits for the image rotation operation.

quant-ph↗

arXiv2021

Low-Energy and CPA-Resistant Adiabatic CMOS/MTJ Logic for IoT Devices

The tremendous growth in the number of Internet of Things (IoT) devices has increased focus on the energy efficiency and security of an IoT device. In this paper, we will present a design level, non-volatile adiabatic architecture for low-energy and Correlation Power Analysis (CPA) resistant IoT devices. IoT devices constructed with CMOS integrated circuits suffer from high dynamic energy and leakage power. To solve this, we look at both adiabatic logic and STT-MTJs (Spin Transfer Torque Magnetic Tunnel Junctions) to reduce both dynamic energy and leakage power. Furthermore, CMOS integrated circuits suffer from side-channel leakage making them insecure against power analysis attacks. We again look to adiabatic logic to design secure circuits with uniform power consumption, thus, defending against power analysis attacks. We have developed a hybrid adiabatic- MTJ architecture using two-phase adiabatic logic. We show that hybrid adiabatic-MTJ circuits are both low energy and secure when compared with CMOS circuits. As a case study, we have constructed one round of PRESENT and have shown energy savings of 64.29% at a frequency of 25 MHz. Furthermore, we have performed a correlation power analysis attack on our proposed design and determined that the key was kept hidden.

cs.ET↗

arXiv2021

Quantum Carry Lookahead Adders for NISQ and Quantum Image Processing

Progress in quantum hardware design is progressing toward machines of sufficient size to begin realizing quantum algorithms in disciplines such as encryption and physics. Quantum circuits for addition are crucial to realize many quantum algorithms on these machines. Ideally, quantum circuits based on fault-tolerant gates and error-correcting codes should be used as they tolerant environmental noise. However, current machines called Noisy Intermediate Scale Quantum (NISQ) machines cannot support the overhead associated with faulttolerant design. In response, low depth circuits such as quantum carry lookahead adders (QCLA)s have caught the attention of researchers. The risk for noise errors and decoherence increase as the number of gate layers (or depth) in the circuit increases. This work presents an out-of-place QCLA based on Clifford+T gates. The QCLAs optimized for T gate count and make use of a novel uncomputation gate to save T gates. We base our QCLAs on Clifford+T gates because they can eventually be made faulttolerant with error-correcting codes once quantum hardware that can support fault-tolerant designs becomes available. We focus on T gate cost as the T gate is significantly more costly to make faulttolerant than the other Clifford+T gates. The proposed QCLAs are compared and shown to be superior to existing works in terms of T-count and therefore the total number of quantum gates. Finally, we illustrate the application of the proposed QCLAs in quantum image processing by presenting quantum circuits for bilinear interpolation.

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arXiv2021

Quantum Annealing for Automated Feature Selection in Stress Detection

We present a novel methodology for automated feature subset selection from a pool of physiological signals using Quantum Annealing (QA). As a case study, we will investigate the effectiveness of QA-based feature selection techniques in selecting the optimal feature subset for stress detection. Features are extracted from four signal sources: foot EDA, hand EDA, ECG, and respiration. The proposed method embeds the feature variables extracted from the physiological signals in a binary quadratic model. The bias of the feature variable is calculated using the Pearson correlation coefficient between the feature variable and the target variable. The weight of the edge connecting the two feature variables is calculated using the Pearson correlation coefficient between two feature variables in the binary quadratic model. Subsequently, D-Wave's clique sampler is used to sample cliques from the binary quadratic model. The underlying solution is then re-sampled to obtain multiple good solutions and the clique with the lowest energy is returned as the optimal solution. The proposed method is compared with commonly used feature selection techniques for stress detection. Results indicate that QA-based feature subset selection performed equally as that of classical techniques. However, under data uncertainty conditions such as limited training data, the performance of quantum annealing for selecting optimum features remained unaffected, whereas a significant decrease in performance is observed with classical feature selection techniques. Preliminary results show the promise of quantum annealing in optimizing the training phase of a machine learning classifier, especially under data uncertainty conditions.

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arXiv2021

Wearable Health Monitoring System for Older Adults in a Smart Home Environment

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These smart health monitoring systems could be realized in a smart home context to render long-term care to the elderly population. In this paper, we present the design of a wearable health monitoring system suitable for older adults in a smart home context. The proposed system offers solutions to monitor the stress, blood pressure, and location of an individual within a smart home environment. The stress detection model proposed in this work uses Electrodermal Activity (EDA), Photoplethysmogram (PPG), and Skin Temperature (ST) sensors embedded in a smart wristband for detecting physiological stress. The stress detection model is trained and tested using stress labels obtained from salivary cortisol which is a clinically established biomarker for physiological stress. A voice-based prototype is also implemented and the feasibility of the proposed system for integration in a smart home environment is analyzed by simulating a data acquisition and streaming scenario. We have also proposed a blood pressure estimation model using PPG signal and advanced regression techniques for integration with the stress detection model in the wearable health monitoring system. Finally, the design of a voice-assisted indoor location system is proposed for integration with the proposed system within a smart home environment. The proposed wearable health monitoring system is an important direction to realize a smart home environment with extensive diagnostic capabilities so that such a system could be useful for rendering long-term and personalized care to the aging population in the comfort of their home.

eess.SP↗

arXiv2021

Machine Learning Based Prediction of Future Stress Events in a Driving Scenario

This paper presents a model for predicting a driver's stress level up to one minute in advance. Successfully predicting future stress would allow stress mitigation to begin before the subject becomes stressed, reducing or possibly avoiding the performance penalties of stress. The proposed model takes features extracted from Galvanic Skin Response (GSR) signals on the foot and hand and Respiration and Electrocardiogram (ECG) signals from the chest of the driver. The data used to train the model was retrieved from an existing database and then processed to create statistical and frequency features. A total of 42 features were extracted from the data and then expanded into a total of 252 features by grouping the data and taking six statistical measurements of each group for each feature. A Random Forest Classifier was trained and evaluated using a leave-one-subject-out testing approach. The model achieved 94% average accuracy on the test data. Results indicate that the model performs well and could be used as part of a vehicle stress prevention system.

cs.LG↗

arXiv2021

Smart Village: An IoT Based Digital Transformation

Almost 46% of the world's population resides in a rural landscape. Smart villages, alongside smart cities, are in need of time for future economic growth, improved agriculture, better health, and education. The smart village is a concept that improves the traditional rural aspects with the help of digital transformation. The smart village is built up using heterogeneous digital technologies pillared around the Internet-of-Thing (IoT). There exist many opportunities in research to design a low-cost, secure, and efficient technical ecosystem. This article identifies the key application areas, where the IoT can be applied in the smart village. The article also presents a comparative study of communication technology options.

cs.CY↗

arXiv2021

Machine Learning Based Anxiety Detection in Older Adults using Wristband Sensors and Context Feature

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as Electrodermal Activity (EDA) and Photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work is obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60-80 with mean age 73.36+-5.25 during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State-Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and Blood Volume Pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy respectively than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.

eess.SP↗

arXiv2021

A Review of Machine Learning Classification Using Quantum Annealing for Real-world Applications

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-Wave's quantum annealer for optimizing machine learning pipelines for real-world classification problems. We review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

quant-ph↗

arXiv2021

Fortifying Vehicular Security Through Low Overhead Physically Unclonable Functions

Within vehicles, the Controller Area Network (CAN) allows efficient communication between the electronic control units (ECUs) responsible for controlling the various subsystems. The CAN protocol was not designed to include much support for secure communication. The fact that so many critical systems can be accessed through an insecure communication network presents a major security concern. Adding security features to CAN is difficult due to the limited resources available to the individual ECUs and the costs that would be associated with adding the necessary hardware to support any additional security operations without overly degrading the performance of standard communication. Replacing the protocol is another option, but it is subject to many of the same problems. The lack of security becomes even more concerning as vehicles continue to adopt smart features. Smart vehicles have a multitude of communication interfaces would an attacker could exploit to gain access to the networks. In this work we propose a security framework that is based on physically unclonable functions (PUFs) and lightweight cryptography (LWC). The framework does not require any modification to the standard CAN protocol while also minimizing the amount of additional message overhead required for its operation. The improvements in our proposed framework results in major reduction in the number of CAN frames that must be sent during operation. For a system with 20 ECUs for example, our proposed framework only requires 6.5% of the number of CAN frames that is required by the existing approach to successfully authenticate every ECU.

cs.CR↗

arXiv2020

T-count and Qubit Optimized Quantum Circuit Designs of Carry Lookahead Adder

Quantum circuits of arithmetic operations such as addition are needed to implement quantum algorithms in hardware. Quantum circuits based on Clifford+T gates are used as they can be made tolerant to noise. The tradeoff of gaining fault tolerance from using Clifford+T gates and error correcting codes is the high implementation overhead of the T gate. As a result, the T-count performance measure has become important in quantum circuit design. Due to noise, the risk for errors in a quantum circuit computation increases as the number of gate layers (or depth) in the circuit increases. As a result, low depth circuits such as quantum carry lookahead adders (QCLA)s have caught the attention of researchers. This work presents two QCLA designs each optimized with emphasis on T-count or qubit cost respectively. In-place and out-of-place versions of each design are shown. The proposed QCLAs are compared against the existing works in terms of T-count. The proposed QCLAs for out-of-place addition achieve average T gate savings of $54.34 \%$ and $37.21 \%$, respectively. The proposed QCLAs for in-place addition achieve average T gate savings of $72.11 \%$ and $35.87 \%$

quant-ph↗

arXiv2018

T-count and Qubit Optimized Quantum Circuit Design of the Non-Restoring Square Root Algorithm

Quantum circuits for basic mathematical functions such as the square root are required to implement scientific computing algorithms on quantum computers. Quantum circuits that are based on Clifford+T gates can easily be made fault tolerant but the T gate is very costly to implement. As a result, reducing T-count has become an important optimization goal. Further, quantum circuits with many qubits are difficult to realize, making designs that save qubits and produce no garbage outputs desirable. In this work, we present a T-count optimized quantum square root circuit with only $2 \cdot n +1$ qubits and no garbage output. To make a fair comparison against existing work, the Bennett's garbage removal scheme is used to remove garbage output from existing works. We determined that our proposed design achieves an average T-count savings of $43.44 \%$, $98.95 \%$, $41.06 \%$ and $20.28 \%$ as well as qubit savings of $85.46 \%$, $95.16 \%$, $90.59 \%$ and $86.77 \%$ compared to existing works.

quant-ph↗

arXiv2018

T-count Optimized Quantum Circuits for Bilinear Interpolation

Quantum circuits for basic image processing functions such as bilinear interpolation are required to implement image processing algorithms on quantum computers. In this work, we propose quantum circuits for the bilinear interpolation of NEQR encoded images based on Clifford+T gates. Quantum circuits for the scale up operation and scale down operation are illustrated. The proposed quantum circuits are based on quantum Clifford+T gates and are optimized for T-count. Quantum circuits based on Clifford+T gates can be made fault tolerant but the T gate is very costly to implement. As a result, reducing T-count is an important optimization goal. The proposed quantum bilinear interpolation circuits are based on (i) a quantum adder, (ii) a proposed quantum subtractor, and (iii) a quantum multiplication circuit. Further, both designs are compared and shown to be superior to existing work in terms of T-count. The proposed quantum bilinear interpolation circuits for the scale down operation and for the scale up operation each have a $92.52\%$ improvement in terms of T-count compared to the existing work.

quant-ph↗