arXiv subjects

Swaroop Ghosh

Publications and source records attributed to Swaroop Ghosh.

At least 91 records · Page 5Linked to original sources

arXiv2022

Optimization of Quantum Read-Only Memory Circuits

Quantum computing is a rapidly expanding field with applications ranging from optimization all the way to complex machine learning tasks. Quantum memories, while lacking in practical quantum computers, have the potential to bring quantum advantage. In quantum machine learning applications for example, a quantum memory can simplify the data loading process and potentially accelerate the learning task. Quantum memory can also store intermediate quantum state of qubits that can be reused for computation. However, the depth, gate count and compilation time of quantum memories such as, Quantum Read Only Memory (QROM) scale exponentially with the number of address lines making them impractical in state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers beyond 4-bit addresses. In this paper, we propose techniques such as, predecoding logic and qubit reset to reduce the depth and gate count of QROM circuits to target wider address ranges such as, 8-bits. The proposed approach reduces the number of gates and depth count by at least 2X compared to the naive implementation at only 36% qubit overhead. A reduction in circuit depth and gate count as high as 75X and compilation time by 85X at the cost of a maximum of 2.28X qubit overhead is observed. Experimentally, the fidelity with the proposed predecoding circuit compared to existing optimization approach is also higher (as much as 73% compared to 40.8%) under reduced error rates.

quant-ph↗

arXiv2022

DeepQMLP: A Scalable Quantum-Classical Hybrid DeepNeural Network Architecture for Classification

Quantum machine learning (QML) is promising for potential speedups and improvements in conventional machine learning (ML) tasks (e.g., classification/regression). The search for ideal QML models is an active research field. This includes identification of efficient classical-to-quantum data encoding scheme, construction of parametric quantum circuits (PQC) with optimal expressivity and entanglement capability, and efficient output decoding scheme to minimize the required number of measurements, to name a few. However, most of the empirical/numerical studies lack a clear path towards scalability. Any potential benefit observed in a simulated environment may diminish in practical applications due to the limitations of noisy quantum hardware (e.g., under decoherence, gate-errors, and crosstalk). We present a scalable quantum-classical hybrid deep neural network (DeepQMLP) architecture inspired by classical deep neural network architectures. In DeepQMLP, stacked shallow Quantum Neural Network (QNN) models mimic the hidden layers of a classical feed-forward multi-layer perceptron network. Each QNN layer produces a new and potentially rich representation of the input data for the next layer. This new representation can be tuned by the parameters of the circuit. Shallow QNN models experience less decoherence, gate errors, etc. which make them (and the network) more resilient to quantum noise. We present numerical studies on a variety of classification problems to show the trainability of DeepQMLP. We also show that DeepQMLP performs reasonably well on unseen data and exhibits greater resilience to noise over QNN models that use a deep quantum circuit. DeepQMLP provided up to 25.3% lower loss and 7.92% higher accuracy during inference under noise than QMLP.

quant-ph↗

arXiv2021

Scalable Variational Quantum Circuits for Autoencoder-based Drug Discovery

The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational autoencoder is one of the computer-aided design methods which explores the chemical space based on existing molecular dataset. Quantum machine learning has emerged as an atypical learning method that may speed up some classical learning tasks because of its strong expressive power. However, near-term quantum computers suffer from limited number of qubits which hinders the representation learning in high dimensional spaces. We present a scalable quantum generative autoencoder (SQ-VAE) for simultaneously reconstructing and sampling drug molecules, and a corresponding vanilla variant (SQ-AE) for better reconstruction. The architectural strategies in hybrid quantum classical networks such as, adjustable quantum layer depth, heterogeneous learning rates, and patched quantum circuits are proposed to learn high dimensional dataset such as, ligand-targeted drugs. Extensive experimental results are reported for different dimensions including 8x8 and 32x32 after choosing suitable architectural strategies. The performance of quantum generative autoencoder is compared with the corresponding classical counterpart throughout all experiments. The results show that quantum computing advantages can be achieved for normalized low-dimension molecules, and that high-dimension molecules generated from quantum generative autoencoders have better drug properties within the same learning period.

quant-ph↗

arXiv2021

Muzzle the Shuttle: Efficient Compilation for Multi-Trap Trapped-Ion Quantum Computers

Trapped-ion systems can have a limited number of ions (qubits) in a single trap. Increasing the qubit count to run meaningful quantum algorithms would require multiple traps where ions need to shuttle between traps to communicate. The existing compiler has several limitations which result in a high number of shuttle operations and degraded fidelity. In this paper, we target this gap and propose compiler optimizations to reduce the number of shuttles. Our technique achieves a maximum reduction of $51.17\%$ in shuttles (average $\approx 33\%$) tested over $125$ circuits. Furthermore, the improved compilation enhances the program fidelity up to $22.68$X with a modest increase in the compilation time.

quant-ph↗

arXiv2021

Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

cs.CV↗

arXiv2021

Shuttle-Exploiting Attacks and Their Defenses in Trapped-Ion Quantum Computers

Trapped-ion (TI) quantum bits are a front-runner technology for quantum computing. TI systems with multiple interconnected traps can overcome the hardware connectivity issue inherent in superconducting qubits and can solve practical problems at scale. With a sufficient number of qubits on the horizon, the multi-programming model for Quantum Computers (QC) has been proposed where multiple users share the same QC for their computing. Multi-programming is enticing for quantum cloud providers as it can maximize device utilization, throughput, and profit for clouds. Users can also benefit from the short wait queue. However, shared access to quantum computers can create new security issues. This paper presents one such vulnerability in shared TI systems that require shuttle operations for communication among traps. Repeated shuttle operations increase quantum bit energy and degrade the reliability of computations (fidelity). We show adversarial program design approaches requiring numerous shuttles. We propose a random and systematic methodology for adversary program generation. Our analysis shows shuttle-exploiting attacks can substantially degrade the fidelities of victim programs by 2X to 63X. Finally, we present several countermeasures such as adopting a hybrid initial mapping policy, padding victim programs with dummy qubits, and capping maximum shuttles.

quant-ph↗

arXiv2021

A Survey and Tutorial on Security and Resilience of Quantum Computing

Present-day quantum computers suffer from various noises or errors such as gate error, relaxation, dephasing, readout error, and crosstalk. Besides, they offer a limited number of qubits with restrictive connectivity. Therefore, quantum programs running these computers face resilience issues and low output fidelities. The noise in the cloud-based access of quantum computers also introduces new modes of security and privacy issues. Furthermore, quantum computers face several threat models from insider and outsider adversaries including input tampering, program misallocation, fault injection, Reverse Engineering (RE), and Cloning. This paper provides an overview of various assets embedded in quantum computers and programs, vulnerabilities and attack models, and the relation between resilience and security. We also cover countermeasures against the reliability and security issues and present a future outlook for the security of quantum computing.

quant-ph↗

arXiv2021

HeapSafe: Securing Unprotected Heaps in RISC-V

RISC-V is a promising open-source architecture primarily targeted for embedded systems. Programs compiled using the RISC-V toolchain can run bare-metal on the system, and, as such, can be vulnerable to several memory corruption vulnerabilities. In this work, we present HeapSafe, a lightweight hardware assisted heap-buffer protection scheme to mitigate heap overflow and use-after-free vulnerabilities in a RISC-V SoC. The proposed scheme tags pointers associated with heap buffers with metadata indices and enforces tag propagation for commonly used pointer operations. The HeapSafe hardware is decoupled from the core and is designed as a configurable coprocessor and is responsible for validating the heap buffer accesses. Benchmark results show a 1.5X performance overhead and 1.59% area overhead, while being 22% faster than a software protection. We further implemented a HeapSafe-nb, an asynchronous validation design, which improves performance by 27% over the synchronous HeapSafe.

cs.CR↗

arXiv2021

Comprehensive Study of Security and Privacy of Emerging Non-Volatile Memories

At the end of Silicon roadmap, keeping the leakage power in tolerable limit and bridging the bandwidth gap between processor and memory have become some of the biggest challenges. Several promising Non-Volatile Memories (NVMs) such as, Spin-Transfer Torque RAM (STTRAM), Magnetic RAM (MRAM), Phase Change Memory (PCM), Resistive RAM (RRAM) and Ferroelectric RAM (FeRAM) are being investigated to address the above issues since they offer high density and consumes zero leakage power. On one hand, the desirable properties of emerging NVMs make them suitable candidates for several applications including replacement of conventional memories. On the other hand, their unique characteristics such as, high and asymmetric read/write current and persistence bring new threats to data security and privacy. Some of these memories are already deployed in full systems and as discrete chips and are believed to become ubiquitous in future computing devices. Therefore, it is of utmost important to investigate their security and privacy issues. Note that these NVMs can be considered for cache, main memory or storage application. They are also suitable to implement in-memory computation which increases system throughput and eliminates Von-Neumann Bottleneck. Compute-capable NVMs impose new security and privacy challenges that are fundamentally different than their storage counterpart. This work identifies NVM vulnerabilities, attack vectors originating from device level all the way to circuits and systems considering both storage and compute applications. We also summarize the circuit/system level countermeasures to make the NVMs robust against security and privacy issues.

cs.CR↗

arXiv2021

Quantum PUF for Security and Trust in Quantum Computing

Quantum computing is a promising paradigm to solve computationally intractable problems. Various companies such as, IBM, Rigetti and D-Wave offer quantum computers using a cloud-based platform that possess several interesting features. These factors motivate a new threat model. To mitigate this threat, we propose two flavors of QuPUF: one based on superposition, and another based on decoherence. Experiments on real IBM quantum hardware show that the proposed QuPUF can achieve inter-die Hamming Distance(HD) of 55% and intra-HD as low as 4%, as compared to ideal cases of 50% and 0% respectively. The proposed QuPUFs can also be used as a standalone solution for any other application.

quant-ph↗

arXiv2021

Qubit Sensing: A New Attack Model for Multi-programming Quantum Computing

Noisy quantum computers suffer from readout or measurement error. It is a classical bit-flip error due to which state "1" is read out as "0" and vice-versa. The probability of readout error shows a state dependence i.e., flipping probability of state "1" may differ from flipping probability of state "0". Moreover, the probability shows correlation across qubits. These state-dependent and correlated error probability introduces a signature of victim outputs on adversary output when two programs are run simultaneously on the same quantum computer. This can be exploited to sense victim output which may contain sensitive information. In this paper, we systematically show that such readout error-dependent signatures exist and that an adversary can use such signature to infer a user output. We experimentally demonstrate the attack (inference) on 3 public IBM quantum computers. Using Jensen-Shannon Distance (JSD) a measure for statistical inference, we show that our approach identifies victim output with an accuracy of 96% on real hardware. We also present randomized output flipping as a lightweight yet effective countermeasure to thwart such information leakage attacks. Our analysis shows the countermeasure incurs a minor penalty of 0.05% in terms of fidelity.

quant-ph↗

arXiv2021

A Quantum Circuit Obfuscation Methodology for Security and Privacy

Optimization of quantum circuits using an efficient compiler is key to its success for NISQ computers. Several 3rd party compilers are evolving to offer improved performance for large quantum circuits. These 3rd parties, or just a certain release of an otherwise trustworthy compiler, may possibly be untrusted and this could lead to an adversary to Reverse Engineer (RE) the quantum circuit for extracting sensitive aspects e.g., circuit topology, program, and its properties. In this paper, we propose obfuscation of quantum circuits to hide the functionality. Quantum circuits have inherent margin between correct and incorrect outputs. Therefore, obfuscation (i.e., corruption of functionality) by inserting dummy gates is nontrivial. We insert dummy SWAP gates one at a time for maximum corruption of functionality before sending the quantum circuit to an untrusted compiler. If an untrusted party clones the design, they get incorrect functionality. The designer removes the dummy SWAP gate post-compilation to restore the correct functionality. Compared to a classical counterpart, the quantum chip does not reveal the circuit functionality. Therefore, an adversary cannot guess the SWAP gate and location/validate using an oracle model. Evaluation of realistic quantum circuit with/without SWAP insertion is impossible in classical computers. Therefore, we propose a metric-based SWAP gate insertion process. The objective of the metric is to ensure maximum corruption of functionality measured using Total Variation Distance (TVD). The proposed approach is validated using IBM default noisy simulation model. Our metric-based approach predicts the SWAP position to achieve TVD of upto 50%, and performs 7.5% better than average TVD, and performs within 12.3% of the best obtainable TVD for the benchmarks. We obtain an overhead of < 5% for the number of gates and circuit depth after SWAP addition.

quant-ph↗

arXiv2021

Drug Discovery Approaches using Quantum Machine Learning

Traditional drug discovery pipeline takes several years and cost billions of dollars. Deep generative and predictive models are widely adopted to assist in drug development. Classical machines cannot efficiently produce atypical patterns of quantum computers which might improve the training quality of learning tasks. We propose a suite of quantum machine learning techniques e.g., generative adversarial network (GAN), convolutional neural network (CNN) and variational auto-encoder (VAE) to generate small drug molecules, classify binding pockets in proteins, and generate large drug molecules, respectively.

cs.ET↗

arXiv2021

Large-scale Quantum Approximate Optimization via Divide-and-Conquer

Quantum Approximate Optimization Algorithm (QAOA) is a promising hybrid quantum-classical algorithm for solving combinatorial optimization problems. However, it cannot overcome qubit limitation for large-scale problems. Furthermore, the execution time of QAOA scales exponentially with the problem size. We propose a Divide-and-Conquer QAOA (DC-QAOA) to address the above challenges for graph maximum cut (MaxCut) problem. The algorithm works by recursively partitioning a larger graph into smaller ones whose MaxCut solutions are obtained with small-size NISQ computers. The overall solution is retrieved from the sub-solutions by applying the combination policy of quantum state reconstruction. Multiple partitioning and reconstruction methods are proposed/ compared. DC-QAOA achieves 97.14% approximation ratio (20.32% higher than classical counterpart), and 94.79% expectation value (15.80% higher than quantum annealing). DC-QAOA also reduces the time complexity of conventional QAOA from exponential to quadratic.

cs.ET↗

arXiv2021

Quantum Generative Models for Small Molecule Drug Discovery

Existing drug discovery pipelines take 5-10 years and cost billions of dollars. Computational approaches aim to sample from regions of the whole molecular and solid-state compounds called chemical space which could be on the order of 1060 . Deep generative models can model the underlying probability distribution of both the physical structures and property of drugs and relate them nonlinearly. By exploiting patterns in massive datasets, these models can distill salient features that characterize the molecules. Generative Adversarial Networks (GANs) discover drug candidates by generating molecular structures that obey chemical and physical properties and show affinity towards binding with the receptor for a target disease. However, classical GANs cannot explore certain regions of the chemical space and suffer from curse-of-dimensionality. A full quantum GAN may require more than 90 qubits even to generate QM9-like small molecules. We propose a qubit-efficient quantum GAN with a hybrid generator (QGAN-HG) to learn richer representation of molecules via searching exponentially large chemical space with few qubits more efficiently than classical GAN. The QGANHG model is composed of a hybrid quantum generator that supports various number of qubits and quantum circuit layers, and, a classical discriminator. QGAN-HG with only 14.93% retained parameters can learn molecular distribution as efficiently as classical counterpart. The QGAN-HG variation with patched circuits considerably accelerates our standard QGANHG training process and avoids potential gradient vanishing issue of deep neural networks. Code is available on GitHub https://github.com/jundeli/quantum-gan.

cs.ET↗

arXiv2020

Addressing Resiliency of In-Memory Floating Point Computation

In-memory computing (IMC) can eliminate the data movement between processor and memory which is a barrier to the energy-efficiency and performance in Von-Neumann computing. Resistive RAM (RRAM) is one of the promising devices for IMC applications (e.g. integer and Floating Point (FP) operations and random logic implementation) due to low power consumption, fast operation, and small footprint in crossbar architecture. In this paper, we propose FAME, a pipelined FP arithmetic (adder/subtractor) using RRAM crossbar based IMC. A novel shift circuitry is proposed to lower the shift overhead during FP operations. Since 96% of the RRAMs used in our architecture are in High Resistance State (HRS), we propose two approaches namely Shift-At-The-Output (SATO) and Force To VDD (FTV) (ground (FTG)) to mitigate Stuck-at-1 (SA1) failures. In both techniques, the fault-free RRAMs are exploited to perform the computation by using an extra clock cycle. Although performance degrades by 50%, SATO can handle 50% of the faults whereas FTV can handle 99% of the faults in the RRAM-based compute array at low power and area overhead. Simulation results show that the proposed single precision FP adder consumes 335 pJ and 322 pJ for NAND-NAND and NOR-NOR based implementations, respectively. The area overheads of SATO and FTV are 28.5% and 9.5%, respectively.

cs.AR↗

arXiv2020

Quantum-soft QUBO Suppression for Accurate Object Detection

Non-maximum suppression (NMS) has been adopted by default for removing redundant object detections for decades. It eliminates false positives by only keeping the image M with highest detection score and images whose overlap ratio with M is less than a predefined threshold. However, this greedy algorithm may not work well for object detection under occlusion scenario where true positives with lower detection scores are possibly suppressed. In this paper, we first map the task of removing redundant detections into Quadratic Unconstrained Binary Optimization (QUBO) framework that consists of detection score from each bounding box and overlap ratio between pair of bounding boxes. Next, we solve the QUBO problem using the proposed Quantum-soft QUBO Suppression (QSQS) algorithm for fast and accurate detection by exploiting quantum computing advantages. Experiments indicate that QSQS improves mean average precision from 74.20% to 75.11% for PASCAL VOC 2007. It consistently outperforms NMS and soft-NMS for Reasonable subset of benchmark pedestrian detection CityPersons.

cs.CV↗

arXiv2020

SCARE: Side Channel Attack on In-Memory Computing for Reverse Engineering

In-memory computing architectures provide a much needed solution to energy-efficiency barriers posed by Von-Neumann computing due to the movement of data between the processor and the memory. Functions implemented in such in-memory architectures are often proprietary and constitute confidential Intellectual Property. Our studies indicate that IMCs implemented using RRAM are susceptible to Side Channel Attack. Unlike conventional SCAs that are aimed to leak private keys from cryptographic implementations, SCARE can reveal the sensitive IP implemented within the memory. Therefore, the adversary does not need to perform invasive Reverse Engineering to unlock the functionality. We demonstrate SCARE by taking recent IMC architectures such as DCIM and MAGIC as test cases. Simulation results indicate that AND, OR, and NOR gates (building blocks of complex functions) yield distinct power and timing signatures based on the number of inputs making them vulnerable to SCA. Although process variations can obfuscate the signatures due to significant overlap, we show that the adversary can use statistical modeling and analysis to identify the structure of the implemented function. SCARE can find the implemented IP by testing a limited number of patterns. For example, the proposed technique reduces the number of patterns by 64% compared to a brute force attack for a+bc function. Additionally, analysis shows improvement in SCAREs detection model due to adversarial change in supply voltage for both DCIM and MAGIC. We also propose countermeasures such as redundant inputs and expansion of literals. Redundant inputs can mask the IP with 25% area and 20% power overhead. However, functions can be found by greater RE effort. Expansion of literals incurs 36% power overhead. However, it imposes brute force search by the adversary for which the RE effort increases by 3.04X.

cs.AR↗