arXiv subjects

Swaroop Ghosh

Publications and source records attributed to Swaroop Ghosh.

At least 55 records · Page 3Linked to original sources

arXiv2024

QuaLITi: Quantum Machine Learning Hardware Selection for Inferencing with Top-Tier Performance

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 suffer from noise that corrupts the quantum states of the qubits and affects the training and inferencing accuracy. Furthermore, quantum computers have long access queues. A single execution with a pre-defined number of shots can take hours just to reach the top of the wait queue, which is especially disadvantageous to Quantum Machine Learning (QML) algorithms that are iterative in nature. Many vendors provide access to a suite of quantum hardware with varied qubit technologies, number of qubits, coupling architectures, and noise characteristics. However, present QML algorithms do not use them for the training procedure and often rely on local noiseless/noisy simulators due to cost and training timing overhead on real hardware. Additionally, inferencing is generally performed on reduced datasets with fewer datapoints. Taking these constraints into account, we perform a study to maximize the inferencing performance of QML workloads based on the choice of hardware selection. Specifically, we perform a detailed analysis of quantum classifiers (both training and inference through the lens of hardware queue wait times) on Iris and reduced Digits datasets under noise and varied conditions such as different hardware and coupling maps. We show that using multiple readily available hardware for training rather than relying on a single hardware, especially if it has a long queue depth of pending jobs, can lead to a performance impact of only 3-4% while providing up to 45X reduction in training wait time.

quant-ph↗

arXiv2024

Guardians of the Quantum GAN

Quantum Generative Adversarial Networks (qGANs) are at the forefront of image-generating quantum machine learning models. To accommodate the growing demand for Noisy Intermediate-Scale Quantum (NISQ) devices to train and infer quantum machine learning models, the number of third-party vendors offering quantum hardware as a service is expected to rise. This expansion introduces the risk of untrusted vendors potentially stealing proprietary information from the quantum machine learning models. To address this concern we propose a novel watermarking technique that exploits the noise signature embedded during the training phase of qGANs as a non-invasive watermark. The watermark is identifiable in the images generated by the qGAN allowing us to trace the specific quantum hardware used during training hence providing strong proof of ownership. To further enhance the security robustness, we propose the training of qGANs on a sequence of multiple quantum hardware, embedding a complex watermark comprising the noise signatures of all the training hardware that is difficult for adversaries to replicate. We also develop a machine learning classifier to extract this watermark robustly, thereby identifying the training hardware (or the suite of hardware) from the images generated by the qGAN validating the authenticity of the model. We note that the watermark signature is robust against inferencing on hardware different than the hardware that was used for training. We obtain watermark extraction accuracy of 100% and ~90% for training the qGAN on individual and multiple quantum hardware setups (and inferencing on different hardware), respectively. Since parameter evolution during training is strongly modulated by quantum noise, the proposed watermark can be extended to other quantum machine learning models as well.

quant-ph↗

arXiv2024

Investigating impact of bit-flip errors in control electronics on quantum computation

In this paper, we investigate the impact of bit flip errors in FPGA memories in control electronics on quantum computing systems. FPGA memories are integral in storing the amplitude and phase information pulse envelopes, which are essential for generating quantum gate pulses. However, these memories can incur faults due to physical and environmental stressors such as electromagnetic interference, power fluctuations, and temperature variations and adversarial fault injections, potentially leading to errors in quantum gate operations. To understand how these faults affect quantum computations, we conducted a series of experiments to introduce bit flips into the amplitude (both real and imaginary components) and phase values of quantum pulses using IBM's simulated quantum environments, FakeValencia, FakeManila, and FakeLima. Our findings reveal that bit flips in the exponent and initial mantissa bits of the real amplitude cause substantial deviations in quantum gate operations, with TVD increases as high as ~200%. Interestingly, the remaining bits exhibited natural tolerance to errors. We proposed a 3-bit repetition error correction code, which effectively reduced the TVD increases to below 40% without incurring any memory overhead. Due to reuse of less significant bits for error correction, the proposed approach introduces maximum of 5-7% extra TVD in nominal cases. However, this can be avoided by sacrificing memory area for implementing the repetition code.

quant-ph↗

arXiv2024

SHARE: Secure Hardware Allocation and Resource Efficiency in Quantum Systems

Quantum computing (QC) is poised to revolutionize problem solving across various fields, with research suggesting that systems with over 50 qubits may achieve quantum advantage surpassing supercomputers in certain optimization tasks. As the hardware size of Noisy Intermediate Scale Quantum (NISQ) computers continues to grow, Multi tenant computing (MTC) has emerged as a viable approach to enhance hardware utilization by allowing shared resource access across multiple quantum programs. However, MTC can also bring challenges and security concerns. This paper focuses on optimizing quantum hardware utilization in shared environments by implementing multi programming strategies that not only enhance hardware utilization but also effectively manage associated risks like crosstalk and fault injection. We propose a novel partitioning and allocation method called Community Based Dynamic Allocation Partitioning (COMDAP) and Secure COMDAP to refine and secure multi programming capabilities in quantum systems. COMDAP ensures equitable and efficient resource distribution, addresses the issues of suboptimal partitioning, and significantly improves hardware utilization. We report a 23 percent average improvement in hardware utilization rate compared to existing greedy heuristics, with rates averaging 92 percent. COMDAP introduces an average increase of approximately 0.05X in delta CX, alongside a 3.5 percent average reduction in PST across benchmarks.

quant-ph↗

arXiv2024

Lattice Surgery for Dummies

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 address errors effectively. Among these, topological codes, particularly surface codes, stand out for their low error thresholds and feasibility for implementation in large-scale quantum computers. However, these codes are restricted to encoding a single qubit. Lattice surgery is crucial for enabling interactions among multiple encoded qubits or between the lattices of a surface code, ensuring that its sophisticated error-correcting features are maintained without significantly increasing the operational overhead. Lattice surgery is pivotal for scaling QECCs across more extensive quantum systems. Despite its critical importance, comprehending lattice surgery is challenging due to its inherent complexity, demanding a deep understanding of intricate quantum physics and mathematical concepts. This paper endeavors to demystify lattice surgery, making it accessible to those without a profound background in quantum physics or mathematics. This work explores surface codes, introduces the basics of lattice surgery, and demonstrates its application in building quantum gates and emulating multi-qubit circuits.

quant-ph↗

arXiv2024

Predicting Side Effect of Drug Molecules using Recurrent Neural Networks

Identification and verification of molecular properties such as side effects is one of the most important and time-consuming steps in the process of molecule synthesis. For example, failure to identify side effects before submission to regulatory groups can cost millions of dollars and months of additional research to the companies. Failure to identify side effects during the regulatory review can also cost lives. The complexity and expense of this task have made it a candidate for a machine learning-based solution. Prior approaches rely on complex model designs and excessive parameter counts for side effect predictions. We believe reliance on complex models only shifts the difficulty away from chemists rather than alleviating the issue. Implementing large models is also expensive without prior access to high-performance computers. We propose a heuristic approach that allows for the utilization of simple neural networks, specifically the recurrent neural network, with a 98+% reduction in the number of required parameters compared to available large language models while still obtaining near identical results as top-performing models.

q-bio.QM↗

arXiv2024

AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization

Quantum circuit transformation aims to produce equivalent circuits while optimizing for various aspects such as circuit depth, gate count, and compatibility with modern Noisy Intermediate Scale Quantum (NISQ) devices. There are two techniques for circuit transformation. The first is a rule-based approach that greedily cancels out pairs of gates that equate to the identity unitary operation. Rule-based approaches are used in quantum compilers such as Qiskit, tket, and Quilc. The second is a search-based approach that tries to find an equivalent quantum circuit by exploring the quantum circuits search space. Search-based approaches typically rely on machine learning techniques such as generative models and Reinforcement Learning (RL). In this work, we propose AltGraph, a novel search-based circuit transformation approach that generates equivalent quantum circuits using existing generative graph models. We use three main graph models: DAG Variational Autoencoder (D-VAE) with two variants: Gated Recurrent Unit (GRU) and Graph Convolutional Network (GCN), and Deep Generative Model for Graphs (DeepGMG) that take a Direct Acyclic Graph (DAG) of the quantum circuit as input and output a new DAG from which we reconstruct the equivalent quantum circuit. Next, we perturb the latent space to generate equivalent quantum circuits some of which may be more compatible with the hardware coupling map and/or enable better optimization leading to reduced gate count and circuit depth. AltGraph achieves on average a 37.55% reduction in the number of gates and a 37.75% reduction in the circuit depth post-transpiling compared to the original transpiled circuit with only 0.0074 Mean Squared Error (MSE) in the density matrix.

quant-ph↗

arXiv2024

Application of Quantum Tensor Networks for Protein Classification

We show that protein sequences can be thought of as sentences in natural language processing and can be parsed using the existing Quantum Natural Language framework into parameterized quantum circuits of reasonable qubits, which can be trained to solve various protein-related machine-learning problems. We classify proteins based on their subcellular locations, a pivotal task in bioinformatics that is key to understanding biological processes and disease mechanisms. Leveraging the quantum-enhanced processing capabilities, we demonstrate that Quantum Tensor Networks (QTN) can effectively handle the complexity and diversity of protein sequences. We present a detailed methodology that adapts QTN architectures to the nuanced requirements of protein data, supported by comprehensive experimental results. We demonstrate two distinct QTNs, inspired by classical recurrent neural networks (RNN) and convolutional neural networks (CNN), to solve the binary classification task mentioned above. Our top-performing quantum model has achieved a 94% accuracy rate, which is comparable to the performance of a classical model that uses the ESM2 protein language model embeddings. It's noteworthy that the ESM2 model is extremely large, containing 8 million parameters in its smallest configuration, whereas our best quantum model requires only around 800 parameters. We demonstrate that these hybrid models exhibit promising performance, showcasing their potential to compete with classical models of similar complexity.

quant-ph↗

arXiv2024

MITS: A Quantum Sorcerer Stone For Designing Surface Codes

In the evolving landscape of quantum computing, determining the most efficient parameters for Quantum Error Correction (QEC) is paramount. Various quantum computers possess varied types and amounts of physical noise. Traditionally, simulators operate in a forward paradigm, taking parameters such as distance, rounds, and physical error to output a logical error rate. However, usage of maximum distance and rounds of the surface code might waste resources. An approach that relies on trial and error to fine-tune QEC code parameters using simulation tools like STIM can be exceedingly time-consuming. Additionally, daily fluctuations in quantum error rates can alter the ideal QEC settings needed. As a result, there is a crucial need for an automated solution that can rapidly determine the appropriate QEC parameters tailored to the current conditions. To bridge this gap, we present MITS, a tool designed to reverse-engineer the well-known simulator STIM for designing QEC codes. MITS accepts the specific noise model of a quantum computer and a target logical error rate as input and outputs the optimal surface code rounds and code distances. This guarantees minimal qubit and gate usage, harmonizing the desired logical error rate with the existing hardware limitations on qubit numbers and gate fidelity. We explored and compared multiple heuristics and machine learning models for training/designing MITS and concluded that XGBoost and Random Forest regression were most effective, with Pearson correlation coefficients of 0.98 and 0.96 respectively.

quant-ph↗

arXiv2024

Q-Embroidery: A Study on Weaving Quantum Error Correction into the Fabric of Quantum Classifiers

Quantum computing holds transformative potential for various fields, yet its practical application is hindered by the susceptibility to errors. This study makes a pioneering contribution by applying quantum error correction codes (QECCs) for complex, multi-qubit classification tasks. We implement 1-qubit and 2-qubit quantum classifiers with QECCs, specifically the Steane code, and the distance 3 & 5 surface codes to analyze 2-dimensional and 4-dimensional datasets. This research uniquely evaluates the performance of these QECCs in enhancing the robustness and accuracy of quantum classifiers against various physical errors, including bit-flip, phase-flip, and depolarizing errors. The results emphasize that the effectiveness of a QECC in practical scenarios depends on various factors, including qubit availability, desired accuracy, and the specific types and levels of physical errors, rather than solely on theoretical superiority.

quant-ph↗

arXiv2024

DyPP: Dynamic Parameter Prediction to Accelerate Convergence of Variational Quantum Algorithms

The exponential run time of quantum simulators on classical machines and long queue times and high costs of real quantum devices present significant challenges in the efficient optimization of Variational Quantum Algorithms (VQAs) like Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA) and Quantum Neural Networks (QNNs). To address these limitations, we propose a new approach, DyPP (Dynamic Parameter Prediction), which accelerates the convergence of VQAs by exploiting regular trends in the parameter weights to update parameters. We introduce two techniques for optimal prediction performance namely, Naive Prediction (NaP) and Adaptive Prediction (AdaP). Through extensive experimentation and training of multiple QNN models on various datasets, we demonstrate that DyPP offers a speedup of approximately $2.25\times$ compared to standard training methods, while also providing improved accuracy (up to $2.3\%$ higher) and loss (up to $6.1\%$ lower) with low storage and computational overheads. We also evaluate DyPP's effectiveness in VQE for molecular ground-state energy estimation and in QAOA for graph MaxCut. Our results show that on average, DyPP leads to speedup of up to $3.1\times$ for VQE and $2.91\times$ for QAOA, compared to traditional optimization techniques, while using up to $3.3\times$ lesser shots (i.e., repeated circuit executions). Even under hardware noise, DyPP outperforms existing optimization techniques, delivering upto $3.33\times$ speedup and $2.5\times$ fewer shots, thereby enhancing efficiency of VQAs.

quant-ph↗

arXiv2024

Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks

Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we assess the efficacy of such attacks in the realm of quantum computing. We conducted comprehensive experiments on various datasets with multiple QML model architectures. Our findings revealed that model stealing attacks can produce clone models achieving up to $0.9\times$ and $0.99\times$ clone test accuracy when trained using Top-$1$ and Top-$k$ labels, respectively ($k:$ num\_classes). To defend against these attacks, we leverage the unique properties of current noisy hardware and perturb the victim model outputs and hinder the attacker's training process. In particular, we propose: 1) hardware variation-induced perturbation (HVIP) and 2) hardware and architecture variation-induced perturbation (HAVIP). Although noise and architectural variability can provide up to $\sim16\%$ output obfuscation, our comprehensive analysis revealed that models cloned under noisy conditions tend to be resilient, suffering little to no performance degradation due to such obfuscations. Despite limited success with our defense techniques, this outcome has led to an important discovery: QML models trained on noisy hardwares are naturally resistant to perturbation or obfuscation-based defenses or attacks.

quant-ph↗

arXiv2024

Hardware Trojans in Quantum Circuits, Their Impacts, and Defense

The reliability of the outcome of a quantum circuit in near-term noisy quantum computers depends on the gate count and depth for a given problem. Circuits with a short depth and lower gate count can yield the correct solution more often than the variant with a higher gate count and depth. To work successfully for Noisy Intermediate Scale Quantum (NISQ) computers, quantum circuits need to be optimized efficiently using a compiler that decomposes high-level gates to native gates of the hardware. Many 3rd party compilers are being developed for lower compilation time, reduced circuit depth, and lower gate count for large quantum circuits. Such compilers, or even a specific release version of a compiler that is otherwise trustworthy, may be unreliable and give rise to security risks such as insertion of a quantum trojan during compilation that evades detection due to the lack of a golden/Oracle model in quantum computing. Trojans may corrupt the functionality to give flipped probabilities of basis states, or result in a lower probability of correct basis states in the output. In this paper, we investigate and discuss the impact of a single qubit Trojan (we have chosen a Hadamard gate and a NOT gate) inserted one at a time at various locations in benchmark quantum circuits without changing the the depth of the circuit. Results indicate an average of 16.18% degradation for the Hadamard Trojan without noise, and 7.78% with noise. For the NOT Trojan (with noise) there is 14.6% degradation over all possible inputs. We then discuss the detection of such Trojans in a quantum circuit using CNN-based classifier achieving an accuracy of 90%.

cs.CR↗

arXiv2024

Non-parametric Greedy Optimization of Parametric Quantum Circuits

The use of Quantum Neural Networks (QNN) that are analogous to classical neural networks, has greatly increased in the past decade owing to the growing interest in the field of Quantum Machine Learning (QML). A QNN consists of three major components: (i) data loading/encoding circuit, (ii) Parametric Quantum Circuit (PQC), and (iii) measurement operations. Under ideal circumstances the PQC of the QNN trains well, however that may not be the case for training under quantum hardware due to presence of different kinds of noise. Deeper QNNs with high depths tend to degrade more in terms of performance compared to shallower networks. This work aims to reduce depth and gate count of PQCs by replacing parametric gates with their approximate fixed non-parametric representations. We propose a greedy algorithm to achieve this such that the algorithm minimizes a distance metric based on unitary transformation matrix of original parametric gate and new set of non-parametric gates. From this greedy optimization followed by a few epochs of re-training, we observe roughly 14% reduction in depth and 48% reduction in gate count at the cost of 3.33% reduction in inferencing accuracy. Similar results are observed for a different dataset as well with different PQC structure.

quant-ph↗

arXiv2023

Stealthy SWAPs: Adversarial SWAP Injection in Multi-Tenant Quantum Computing

Quantum computing (QC) holds tremendous promise in revolutionizing problem-solving across various domains. It has been suggested in literature that 50+ qubits are sufficient to achieve quantum advantage (i.e., to surpass supercomputers in solving certain class of optimization problems).The hardware size of existing Noisy Intermediate-Scale Quantum (NISQ) computers have been ever increasing over the years. Therefore, Multi-tenant computing (MTC) has emerged as a potential solution for efficient hardware utilization, enabling shared resource access among multiple quantum programs. However, MTC can also bring new security concerns. This paper proposes one such threat for MTC in superconducting quantum hardware i.e., adversarial SWAP gate injection in victims program during compilation for MTC. We present a representative scheduler designed for optimal resource allocation. To demonstrate the impact of this attack model, we conduct a detailed case study using a sample scheduler. Exhaustive experiments on circuits with varying depths and qubits offer valuable insights into the repercussions of these attacks. We report a max of approximately 55 percent and a median increase of approximately 25 percent in SWAP overhead. As a countermeasure, we also propose a sample machine learning model for detecting any abnormal user behavior and priority adjustment.

quant-ph↗

arXiv2023

Designing Hash and Encryption Engines using Quantum Computing

Quantum computing (QC) holds the promise of revolutionizing problem-solving by exploiting quantum phenomena like superposition and entanglement. It offers exponential speed-ups across various domains, from machine learning and security to drug discovery and optimization. In parallel, quantum encryption and key distribution have garnered substantial interest, leveraging quantum engines to enhance cryptographic techniques. Classical cryptography faces imminent threats from quantum computing, exemplified by Shors algorithms capacity to breach established encryption schemes. However, quantum circuits and algorithms, capitalizing on superposition and entanglement, offer innovative avenues for enhancing security. In this paper we explore quantum-based hash functions and encryption to fortify data security. Quantum hash functions and encryption can have numerous potential application cases, such as password storage, digital signatures, cryptography, anti-tampering etc. The integration of quantum and classical methods demonstrates potential in securing data in the era of quantum computing.

quant-ph↗

arXiv2023

Q-Pandora Unboxed: Characterizing Noise Resilience of Quantum Error Correction Codes

Quantum error correction codes (QECCs) are critical for realizing reliable quantum computing by protecting fragile quantum states against noise and errors. However, limited research has analyzed the noise resilience of QECCs to help select optimal codes. This paper conducts a comprehensive study analyzing two QECCs - rotated and unrotated surface codes - under different error types and noise models using simulations. Among them, rotated surface codes perform best with higher thresholds attributed to simplicity and lower qubit overhead. The noise threshold, or the point at which QECCs become ineffective, surpasses the error rate found in contemporary quantum processors. When confronting quantum hardware where a specific error or noise model is dominant, a discernible hierarchy emerges for surface code implementation in terms of resource demand. This ordering is consistently observed across unrotated, and rotated surface codes. Our noise model analysis ranks the code-capacity model as the most pessimistic and circuit-level model as the most realistic. The study maps error thresholds, revealing surface code's advantage over modern quantum processors. It also shows higher code distances and rounds consistently improve performance. However, excessive distances needlessly increase qubit overhead. By matching target logical error rates and feasible number of qubits to optimal surface code parameters, our study demonstrates the necessity of tailoring these codes to balance reliability and qubit resources. Conclusively, we underscore the significance of addressing the notable challenges associated with surface code overheads and qubit improvements.

quant-ph↗

arXiv2023

Trojan Taxonomy in Quantum Computing

Quantum computing introduces unfamiliar security vulnerabilities demanding customized threat models. Hardware and software Trojans pose serious concerns needing rethinking from classical paradigms. This paper develops the first structured taxonomy of Trojans tailored to quantum information systems. We enumerate potential attack vectors across the quantum stack from hardware to software layers. A categorization of quantum Trojan types and payloads is outlined ranging from reliability degradation, functionality corruption, backdoors, and denial-of-service. Adversarial motivations behind quantum Trojans are analyzed. By consolidating diverse threats into a unified perspective, this quantum Trojan taxonomy provides insights guiding threat modeling, risk analysis, detection mechanisms, and security best practices customized for this novel computing paradigm.

cs.CR↗