arXiv subjects

Mahabubul Alam

Publications and source records attributed to Mahabubul Alam.

16 recordsLinked to original sources

arXiv2022

Architectures for Quantum Information Processing

Quantum computing is changing the way we think about computing. Significant strides in research and development for managing and harnessing the power of quantum systems has been made in recent years, demonstrating the potential for transformative quantum technology. Quantum phenomena like superposition, entanglement, and interference can be exploited to solve issues that are difficult for traditional computers. IBM's first public access to true quantum computers through the cloud, as well as Google's demonstration of quantum supremacy, are among the accomplishments. Besides, a slew of other commercial, government, and academic projects are in the works to create next-generation hardware, a software stack to support the hardware ecosystem, and viable quantum algorithms. This chapter covers various quantum computing architectures including many hardware technologies that are being investigated. We also discuss a variety of challenges, including numerous errors/noise that plague the quantum computers. An overview of literature investigating noise-resilience approaches is also presented.

quant-ph↗

arXiv2022

Knowledge Distillation in Quantum Neural Network using Approximate Synthesis

Recent assertions of a potential advantage of Quantum Neural Network (QNN) for specific Machine Learning (ML) tasks have sparked the curiosity of a sizable number of application researchers. The parameterized quantum circuit (PQC), a major building block of a QNN, consists of several layers of single-qubit rotations and multi-qubit entanglement operations. The optimum number of PQC layers for a particular ML task is generally unknown. A larger network often provides better performance in noiseless simulations. However, it may perform poorly on hardware compared to a shallower network. Because the amount of noise varies amongst quantum devices, the optimal depth of PQC can vary significantly. Additionally, the gates chosen for the PQC may be suitable for one type of hardware but not for another due to compilation overhead. This makes it difficult to generalize a QNN design to wide range of hardware and noise levels. An alternate approach is to build and train multiple QNN models targeted for each hardware which can be expensive. To circumvent these issues, we introduce the concept of knowledge distillation in QNN using approximate synthesis. The proposed approach will create a new QNN network with (i) a reduced number of layers or (ii) a different gate set without having to train it from scratch. Training the new network for a few epochs can compensate for the loss caused by approximation error. Through empirical analysis, we demonstrate ~71.4% reduction in circuit layers, and still achieve ~16.2% better accuracy under noise.

quant-ph↗

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

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

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

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

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↗

arXiv2020

Resiliency Analysis and Improvement of Variational Quantum Factoring in Superconducting Qubit

Variational algorithm using Quantum Approximate Optimization Algorithm (QAOA) can solve the prime factorization problem in near-term noisy quantum computers. Conventional Variational Quantum Factoring (VQF) requires a large number of 2-qubit gates (especially for factoring a large number) resulting in deep circuits. The output quality of the deep quantum circuit is degraded due to errors limiting the computational power of quantum computing. In this paper, we explore various transformations to optimize the QAOA circuit for integer factorization. We propose two criteria to select the optimal quantum circuit that can improve the noise resiliency of VQF.

cs.ET↗

arXiv2020

Accelerating Quantum Approximate Optimization Algorithm using Machine Learning

We propose a machine learning based approach to accelerate quantum approximate optimization algorithm (QAOA) implementation which is a promising quantum-classical hybrid algorithm to prove the so-called quantum supremacy. In QAOA, a parametric quantum circuit and a classical optimizer iterates in a closed loop to solve hard combinatorial optimization problems. The performance of QAOA improves with increasing number of stages (depth) in the quantum circuit. However, two new parameters are introduced with each added stage for the classical optimizer increasing the number of optimization loop iterations. We note a correlation among parameters of the lower-depth and the higher-depth QAOA implementations and, exploit it by developing a machine learning model to predict the gate parameters close to the optimal values. As a result, the optimization loop converges in a fewer number of iterations. We choose graph MaxCut problem as a prototype to solve using QAOA. We perform a feature extraction routine using 100 different QAOA instances and develop a training data-set with 13,860 optimal parameters. We present our analysis for 4 flavors of regression models and 4 flavors of classical optimizers. Finally, we show that the proposed approach can curtail the number of optimization iterations by on average 44.9% (up to 65.7%) from an analysis performed with 264 flavors of graphs.

cs.ET↗

arXiv2019

MUQUT: Multi-Constraint Quantum Circuit Mapping on Noisy Intermediate-Scale Quantum Computers

Rapid advancement in the domain of quantum technologies has opened up researchers to the real possibility of experimenting with quantum circuits and simulating small-scale quantum programs. Nevertheless, the quality of currently available qubits and environmental noise poses a challenge in the smooth execution of the quantum circuits. Therefore, efficient design automation flows for mapping a given algorithm to the Noisy Intermediate Scale Quantum (NISQ) computer becomes of utmost importance. State-of-the-art quantum design automation tools are primarily focused on reducing logical depth, gate count and qubit count with the recent emphasis on topology-aware (nearest-neighbor compliance) mapping. In this work, we extend the technology mapping flows to simultaneously consider the topology and gate fidelity constraints while keeping logical depth and gate count as optimization objectives. We provide a comprehensive problem formulation and multi-tier approach towards solving it. The proposed automation flow is compatible with commercial quantum computers, such as IBM QX and Rigetti. Our simulation results over 10 quantum circuit benchmarks show that the fidelity of the circuit can be improved up to 3.37X with an average improvement of 1.87X.

quant-ph↗

arXiv2019

Analysis of Quantum Approximate Optimization Algorithm under Realistic Noise in Superconducting Qubits

The quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid technique to solve combinatorial optimization problems in near-term gate-based noisy quantum devices. In QAOA, the objective is a function of the quantum state, which itself is a function of the gate parameters of a multi-level parameterized quantum circuit (PQC). A classical optimizer varies the continuous gate parameters to generate distributions (quantum state) with significant support to the optimal solution. Even at the lowest circuit depth, QAOA offers non-trivial provable performance guarantee which is expected to increase with the circuit depth. However, the existing analysis fails to consider non-idealities in the qubit quality i.e., short lifetime and imperfect gate operations in realistic quantum hardware. In this article, we investigate the impact of various noise sources on the performance of QAOA both in simulation and on a real quantum computer from IBM. Our analyses indicate that the optimal number of stages (p-value) for any QAOA instance is limited by the noise characteristics (gate error, coherence time, etc.) of the target hardware as opposed to the current perception that higher-depth QAOA will provide monotonically better performance for a given problem compared to the low-depth implementations.

quant-ph↗

arXiv2019

Study of Decoherence in Quantum Computers: A Circuit-Design Perspective

Decoherence of quantum states is a major hurdle towards scalable and reliable quantum computing. Lower decoherence (i.e., higher fidelity) can alleviate the error correction overhead and obviate the need for energy-intensive noise reduction techniques e.g., cryogenic cooling. In this paper, we performed a noise-induced decoherence analysis of single and multi-qubit quantum gates using physics-based simulations. The analysis indicates that (i) decoherence depends on the input state and the gate type. Larger number of $|1\rangle$ states worsen the decoherence; (ii) amplitude damping is more detrimental than phase damping; (iii) shorter depth implementation of a quantum function can achieve lower decoherence. Simulations indicate 20\% improvement in the fidelity of a quantum adder when realized using lower depth topology. The insights developed in this paper can be exploited by the circuit designer to choose the right gates and logic implementation to optimize the system-level fidelity.

cs.ET↗

arXiv2019

Addressing Temporal Variations in Qubit Quality Metrics for Parameterized Quantum Circuits

The public access to noisy intermediate-scale quantum (NISQ) computers facilitated by IBM, Rigetti, D-Wave, etc., has propelled the development of quantum applications that may offer quantum supremacy in the future large-scale quantum computers. Parameterized quantum circuits (PQC) have emerged as a major driver for the development of quantum routines that potentially improve the circuit's resilience to the noise. PQC's have been applied in both generative (e.g. generative adversarial network) and discriminative (e.g. quantum classifier) tasks in the field of quantum machine learning. PQC's have been also considered to realize high fidelity quantum gates with the available imperfect native gates of a target quantum hardware. Parameters of a PQC are determined through an iterative training process for a target noisy quantum hardware. However, temporal variations in qubit quality metrics affect the performance of a PQC. Therefore, the circuit that is trained without considering temporal variations exhibits poor fidelity over time. In this paper, we present training methodologies for PQC in a completely classical environment that can improve the fidelity of the trained PQC on a target NISQ hardware by as much as 42.5%.

cs.ET↗