Understanding Quantum Technologies

Ezratty, Olivier

doi:10.48550/arxiv.2111.15352

Cited by 6 publications

(5 citation statements)

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“…These centers are created by replacing a carbon atom with a nitrogen atom in an artificial diamond structure near a carbon atom gap. Microwaves, a magnetic field, and an electric field are used to implement qubit gates, while qubit readout is achieved through the use of a laser and fluorescence detection [23].…”

Section: The Qubit Implementation Techniquesmentioning

confidence: 99%

“…While progress is being made in error removal, it is barely managing to gain one or two orders of magnitude in error reduction. In an ideal world, however, we would require improvements of ten orders of magnitude [23]. It is possible to correct errors, even by using noisy gates, provided that the noise level remains below a certain threshold.…”

Section: Error Mitigation and Correctionmentioning

confidence: 99%

“…The opinions of experts in the field of quantum computing range from optimistic to pessimistic, with a significant disparity. According to their 2020 roadmaps, leading tech giants such as Google, IBM, and Amazon anticipate achieving true quantum supremacy in the near future and developing a quantum computer with 100 logical qubits within a decade [23]. On the other end, some pessimistic views are saying that there is no hope of reaching quantum speed-up ever.…”

Section: Investment In Quantum Computingmentioning

confidence: 99%

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Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Otgonbaatar,

Nurmi,

Johansson

et al. 2023

Preprint

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<p>This study explores the potential of quantum machine learning and quantum computing for climate change detection, climate modeling, and climate digital twin. We additionally consider the time and energy consumption of quantum machines and classical computers. Moreover, we identified several use-case instances for climate change detection, climate modeling, and climate digital twin that are challenging for conventional computers but can be tackled efficiently with quantum machines or by integrating them with classical computers. We also evaluated the efficacy of quantum annealers, quantum simulators, and universal quantum computers, each designed to solve specific types and kinds of computational problems that are otherwise difficult.</p>

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Section: The Qubit Implementation Techniquesmentioning

confidence: 99%

Section: Error Mitigation and Correctionmentioning

confidence: 99%

Section: Investment In Quantum Computingmentioning

confidence: 99%

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Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Otgonbaatar,

Nurmi,

Johansson

et al. 2023

Preprint

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“…We mainly used publicly available resources such as websites, LinkedIn profiles, targeted Crunchbase searches [26], and other databases. There were several ordered lists of companies that we utilized as resources, such as the lists in at Understanding Quantum Technologies document by Olivier Ezratty [27], the Private/Startup Companies section of the Quantum Computing Report website [28], The Quantum Insider platform [29], QIS Data Portal [30], and several consortium member lists such as Quantum Industry Canada (QIC) [31], The Quantum Economic Development Consortium (QED-C) [32], European Quantum Industry Consortium (QuIC) [33], UKQuantum [34], Quantum Technologies Development Consortium (QTC) in Israel [35], Quantum Technology and Application Consortium (QUTAC) in Germany [36], MSU Quantum Technology Centre in Russia [37], Quantum Business Network in Germany [38], and IBM Quantum Network [39]. Furthermore, in years we added start-ups to our database from the participant lists of events like Inside Quantum Technology [40], Quantum Business Europe [41], Q2B [42] organized by QCWare, and Careers in Quantum Technologies organized by QURECA [43].…”

Section: Datasetmentioning

confidence: 99%

The landscape of the quantum start-up ecosystem

Seskir¹,

Ramis

Aydınoğlu

2022

EPJ Quantum Technol.

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The second quantum revolution has been producing groundbreaking scientific and technological outputs since the early 2000s; however, the scientific literature on the impact of this revolution on the industry, specifically on start-ups, is limited. In this paper, we present a landscaping study with a gathered dataset of 441 companies from 42 countries that we identify as quantum start-ups, meaning that they mainly focus on quantum technologies (QT) as their primary priority business. We answer the following questions: (1) What are the temporal and geographical distributions of the quantum start-ups? (2) How can we categorize them, and how are these categories populated? (3) Are there any patterns that we can derive from empirical data on trends? We found that more than 92% of these companies have been founded within the last 10 years, and more than 50% of them are located in the US, the UK, and Canada. We categorized the QT start-ups into six fields: (i) complementary technologies, (ii) quantum computing (hardware), (iii) quantum computing (software/application/simulation), (iv) quantum cryptography/communication, (v) quantum sensing and metrology, and (vi) supporting companies, and analyzed the population of each field both for countries, and temporally. Finally, we argue that low levels of quantum start-up activity in a country might be an indicator of a national initiative to be adopted afterwards, which later sees both an increase in the number of start-ups, and a diversification of activity in different QT fields.

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“…Quantum technologies [1,2], which rest on the ability to actively control microscopic systems and exploit their unique quantum mechanical features, have the potential for high impact in a number of different fields; from precision sensing [3][4][5] and communication [6,7], to encryption [8] and very prominently quantum computing [1,9,10]. The extreme sensitivity of quantum effects to their immediate environment, combined with the exquisite control required to exploit these effects, necessitates accurate dynamical modeling.…”

Section: Introductionmentioning

confidence: 99%

Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification

Bondar¹,

Popovych²,

Jacobs³

et al. 2022

Preprint

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Current quantum devices suffer imperfections as a result of fabrication, as well as noise and dissipation as a result of coupling to their immediate environments. Because of this, it is often difficult to obtain accurate models of their dynamics from first principles. An alternative is to extract such models from time-series measurements of their behavior. Here, we formulate this systemidentification problem as a polynomial optimization problem. Recent advances in optimization have provided globally convergent solvers for this class of problems, which using our formulation prove estimates of the Kraus map or the Lindblad equation. We include an overview of the state-of-the-art algorithms, bounds, and convergence rates, and illustrate the use of this approach to modeling open quantum systems.

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Understanding Quantum Technologies

Cited by 6 publications

References 0 publications

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

Quantum Computing for Climate Change Detection, Climate Modeling, and Climate Digital Twin

The landscape of the quantum start-up ecosystem

Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification

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