KM
K.J. Mesman
info
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2 records found
1
Currently, quantum computing is making large steps to becoming a mature technology. Many companies are developing their own quantum hardware for both research and preparing for practical deployment. The main challenges in the past years have been an abundance of practical applications for the few-qubit Noisy Intermediate-Scale Quantum (NISQ) quantum hardware and scaling the qubits and depth of the quantum hardware. For these reasons, quantum computing was not yet at the maturity level needed to create a useful application level quantumbenchmark. Many quantum benchmarks have been developed, but these either aim at qubit level assessment or are lacking in practical usability. For a benchmark to be useful, the practical application of the quantum hardware needs to be reflected. For this a practical application is required and this was not yet achievable with current quantum hardware scales. Benchmarks at the component level (individual qubits, quantum logic gates) have been developed widely and are useful for the development of the hardware. This is, however, more aimed at basic quantum research rather than application [21]. This also serves a fundamentally different goal than a performance measure of quantum computers. Many different forms of quantum hardware are being used, without a clear dominating implementation. Each of these have different dynamics and metrics, and can therefore not be generalized [97]. For this reason, making a benchmark for low-level hardware aspectswill not properly reflect its performance compared to other implementations of the quantum hardware. Furthermore, while benchmarks for single qubit operations have been developed [32, 36, 70, 102], these performances do not accurately reflect quantum operations on larger scales. Noise levels, for example, are an important metric in single-gate performance. The noise, however, varies per gate and due to qubit entanglement will propagate unpredictably throughout the system [38, 43, 97]. This makes it impossible to use single gate noise performance to extrapolate for the entire system, while for classical computers this would have been possible. These difficulties in determining the performance will require abstraction from the low level performance and an application level benchmark is needed to properly verify the performance.
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Currently, quantum computing is making large steps to becoming a mature technology. Many companies are developing their own quantum hardware for both research and preparing for practical deployment. The main challenges in the past years have been an abundance of practical applications for the few-qubit Noisy Intermediate-Scale Quantum (NISQ) quantum hardware and scaling the qubits and depth of the quantum hardware. For these reasons, quantum computing was not yet at the maturity level needed to create a useful application level quantumbenchmark. Many quantum benchmarks have been developed, but these either aim at qubit level assessment or are lacking in practical usability. For a benchmark to be useful, the practical application of the quantum hardware needs to be reflected. For this a practical application is required and this was not yet achievable with current quantum hardware scales. Benchmarks at the component level (individual qubits, quantum logic gates) have been developed widely and are useful for the development of the hardware. This is, however, more aimed at basic quantum research rather than application [21]. This also serves a fundamentally different goal than a performance measure of quantum computers. Many different forms of quantum hardware are being used, without a clear dominating implementation. Each of these have different dynamics and metrics, and can therefore not be generalized [97]. For this reason, making a benchmark for low-level hardware aspectswill not properly reflect its performance compared to other implementations of the quantum hardware. Furthermore, while benchmarks for single qubit operations have been developed [32, 36, 70, 102], these performances do not accurately reflect quantum operations on larger scales. Noise levels, for example, are an important metric in single-gate performance. The noise, however, varies per gate and due to qubit entanglement will propagate unpredictably throughout the system [38, 43, 97]. This makes it impossible to use single gate noise performance to extrapolate for the entire system, while for classical computers this would have been possible. These difficulties in determining the performance will require abstraction from the low level performance and an application level benchmark is needed to properly verify the performance.
Battery Free Jogger Light
Energy Storage
The deliverable of this project is a light for joggers that does not use a battery and keeps blinking for a short period of time after standing still. This research details the design and implementation of the storage part of a battery free jogger's light. The goal of this storage part is that it stores energy delivered by an energy harvester so that it can power LEDs for at least 30 seconds when there is no energy harvested anymore. The system consists of a full-bridge rectifier, a voltage regulator, a supercapacitor to store the energy and a switch to couple or decouple the load. The main trade-off of this research is between the charging and discharging times of the supercapacitor. Results show that LEDs in the rectifier offer advantages, since there is instant lighting when jogging and which makes the charging time less critical. With this feature the discharging time could be increased up to one minute. The total efficiency of this storage system is calculated to be 67.9\%.
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The deliverable of this project is a light for joggers that does not use a battery and keeps blinking for a short period of time after standing still. This research details the design and implementation of the storage part of a battery free jogger's light. The goal of this storage part is that it stores energy delivered by an energy harvester so that it can power LEDs for at least 30 seconds when there is no energy harvested anymore. The system consists of a full-bridge rectifier, a voltage regulator, a supercapacitor to store the energy and a switch to couple or decouple the load. The main trade-off of this research is between the charging and discharging times of the supercapacitor. Results show that LEDs in the rectifier offer advantages, since there is instant lighting when jogging and which makes the charging time less critical. With this feature the discharging time could be increased up to one minute. The total efficiency of this storage system is calculated to be 67.9\%.