Random Sequence Generation using Superconducting Qubits

Gupta, Mayank; Nene, Manisha J.

doi:10.1109/icicv50876.2021.9388495

Cited by 11 publications

(6 citation statements)

References 43 publications

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“…On similar lines for 4 bits, 2 qubits will be required and hence, 2 N Classical Bits can be stored on N Qubits. This means that a particle has an ability to exist in multiple quantum states and when the measurement is performed, it undergoes certain changes resulting in a probabilistic value, thereby losing its individuality [15,16].…”

Section: Superpositionmentioning

confidence: 99%

Machine Learning: A Quantum Perspective

Jhanwar

Nene

2021

Recent Trends in Intensive Computing

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Recently, increased availability of the data has led to advances in the field of machine learning. Despite of the growth in the domain of machine learning, the proximity to the physical limits of chip fabrication in classical computing is motivating researchers to explore the properties of quantum computing. Since quantum computers leverages the properties of quantum mechanics, it carries the ability to surpass classical computers in machine learning tasks. The study in this paper contributes in enabling researchers to understand how quantum computers can bring a paradigm shift in the field of machine learning. This paper addresses the concepts of quantum computing which influences machine learning in a quantum world. It also states the speedup observed in different machine learning algorithms when executed on quantum computers. The paper towards the end advocates the use of quantum application software and throw light on the existing challenges faced by quantum computers in the current scenario.

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Section: Superpositionmentioning

confidence: 99%

Machine Learning: A Quantum Perspective

Jhanwar

Nene

2021

Recent Trends in Intensive Computing

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“…To overcome these limitations, researchers have explored new methods, such as those based on quantum computing, which can naturally generate true random samples due to the probabilistic nature of qubits [ 11 ]. Moreover, quantum amplitude estimation (QAE) has shown promise in estimating the value at risk and offers a quadratic speedup over classical Monte Carlo methods [ 4 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

A More General Quantum Credit Risk Analysis Framework

Dri

Aita

Giusto

et al. 2023

Entropy

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Credit risk analysis (CRA) quantum algorithms aim at providing a quadratic speedup over classical analogous methods. Despite this, experts in the business domain have identified significant limitations in the existing approaches. Thus, we proposed a new variant of the CRA quantum algorithm to address these limitations. In particular, we improved the risk model for each asset in a portfolio by enabling it to consider multiple systemic risk factors, resulting in a more realistic and complex model for each asset’s default probability. Additionally, we increased the flexibility of the loss-given-default input by removing the constraint of using only integer values, enabling the use of real data from the financial sector to establish fair benchmarking protocols. Furthermore, all proposed enhancements were tested both through classical simulation of quantum hardware and, for this new version of our work, also using QPUs from IBM Quantum Experience in order to provide a baseline for future research. Our proposed variant of the CRA quantum algorithm addresses the significant limitations of the current approach and highlights an increased cost in terms of circuit depth and width. In addition, it provides a path to a substantially more realistic software solution. Indeed, as quantum technology progresses, the proposed improvements will enable meaningful scales and useful results for the financial sector.

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“…This stimulated the search for new methods and solutions, exploiting new emerging fields such as quantum computing. Quantum computers naturally overcome this restriction since they naturally generate true random samples due to the probabilistic nature of qubits [9]. In addition to this, the paradigmatic change operated by this technology offered new possibilities and favored innovative approaches able to take advantage of quantum technology.…”

Section: Introductionmentioning

confidence: 99%

Towards practical Quantum Credit Risk Analysis

Dri

Giusto

Aita

et al. 2022

J. Phys.: Conf. Ser.

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In recent years a CRA (Credit Risk Analysis) quantum algorithm with a quadratic speedup over classical analogous methods has been introduced [1]. We propose a new variant of this quantum algorithm with the intent of overcoming some of the most significant limitations (according to business domain experts) of this approach. In particular, we describe a method to implement a more realistic and complex risk model for the default probability of each portfolio’s asset, capable of taking into account multiple systemic risk factors. In addition, we present a solution to increase the flexibility of one of the model’s inputs, the Loss Given Default, removing the constraint to use integer values. This specific improvement addresses the need to use real data coming from the financial sector in order to establish fair benchmarking protocols. Although these enhancements come at a cost in terms of circuit depth and width, they nevertheless show a path towards a more realistic software solution. Recent progress in quantum technology shows that eventually, the increase in the number and reliability of qubits will allow for useful results and meaningful scales for the financial sector, also on real quantum hardware, paving the way for a concrete quantum advantage in the field. The paper also describes experiments conducted on simulators to test the circuit proposed and contains an assessment of the scalability of the approach presented.

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Random Sequence Generation using Superconducting Qubits

Cited by 11 publications

References 43 publications

Machine Learning: A Quantum Perspective

Machine Learning: A Quantum Perspective

A More General Quantum Credit Risk Analysis Framework

Towards practical Quantum Credit Risk Analysis

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