ChemicalQDeviceQuantum Coding Advantages: Neuroradiology
Dear Healthcare Colleague, my name is Kevin Kawchak, and I am the Founder CEO of ChemicalQDevice. Today is February 24th, 2023 for "Quantum Coding Advantages for Neuroradiology and Medical Imaging"
Several Quantum Algorithms have been identified that have potential for Medical benefit. These include Variational Quantum Eigensolvers (VQE), that require relatively low numbers of qubits, can handle large matrices, and are suitable for NISQ quantum computers. In addition, Hopfield networks and Quantum fuzzy feed forward networks can assist as quantum versions of Neural Networks. Quantum Unconstrained Binary Optimization (QUBO) has proven effective for Classification problems, such as in imaging. (1)
Emerging advantages from quantum mechanics are apparent through the principles of Superpositioning, Interference, and Entanglement. Single or Multiple Quantum versions of "gates" are effectively alterations to different configurations of the Identity Matrix. Since qubits are rich in information, and quantum amplitudes can be positive, negative, or complex - quantum solutions for diagnostic and/or predictive purposes can be significant for Neuroradiology, especially as quantum hardware continues to evolve. (2-4)
IBM Qiskit provides intuitive solutions to run and make modifications to quantum circuits such as Bell State, Grover's, and Shor's algorithm on real quantum hardware. A summary of the quantum computer, run times, and probability distributions are readily available to developers. (5-7)
Many researchers have published quantum findings on topics including "Single Qubit Encoding for Classification", QUBO Feature Selection for Classification, along with many applications of Quantum Convolutional Neural Networks for Classification. In specific, Baek, H. et al., revealed a Scalable Quantum Convolutional Neural Network for 3D Point Cloud Images, which may prove valuable for new Raw Brain MRI File analyses. sQCNN-3D promises to utilize less qubits while providing a favorable Reverse Fidelity training. (8-11)
In addition several papers have included approaches to incorporate quantum into Explainable AI. In 2023, Daskin, A., Buge, I., et al, and Heese, R., et al., have all helped progress this relatively new Quantum XAI field utilizing "Ridge Functions", Novel Algorithms, and providing understanding of how Shapley values interact with the quantum realm. (12-14)
Neuroradiologists that are familiar with Artificial Intelligence will likely be utilizing Variational Quantum Algorithms in the near future. De Palma, G., et al. have published findings based on newly developed quantum entropic and concentration inequalities. In addition, VQAs continue to be further understood by Anschuetz, E.R., et al. with a 2022 Nature Communications publication, stating that a Superpolynomially small fraction of local minima within any constant energy from the global minimum exists in the algorithms. (15-16)
In a February 22nd 2023 Nature Article, Using a 49 qubit quantum computer, Google recovered from two simultaneous errors, which had slightly better performance than with 17 qubits. Although this is the first time their larger computer outperformed the smaller, there is no guarantee that using larger circuits will always give better results. (17)
Lastly, in a February 23rd 2023 Quantinuum Publication, the hardware manufacturer achieved 5 digits in Quantum Volume for the first time. The previously published 8,192 QV was surpassed by 32,768 QV, corresponding to 2^15, with 15 presumably meaning 15 qubits. Their corresponding runtimes decreased 3 Times: From over 4 Hours to just over an Hour, which could correspond to faster fully connected quantum neural networks for Radiology utilizing this specific type of ion trap quantum technology. (18)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/24/23 "Quantum Coding Advantages for Neuroradiology and Medical Imaging" References[1] https://www.amarchenkova.com/posts/5-quantum-algorithms-that-could-change-the-world[2] https://learn.microsoft.com/en-us/azure/quantum/overview-understanding-quantum-computing[3] https://en.wikipedia.org/wiki/Quantum_logic_gate[4] https://quantum-computing.ibm.com/composer/docs/iqx/guide/entanglement[5] https://qiskit.org/[6] https://quantum-computing.ibm.com/composer/docs/iqx/guide/grovers-algorithm[7] https://quantum-computing.ibm.com/composer/docs/iqx/guide/shors-algorithm[8] https://ieeexplore.ieee.org/document/9798852[9] https://arxiv.org/pdf/2203.13261.pdf[10] https://arxiv.org/abs/2008.07230[11] https://arxiv.org/abs/2210.09728[12] https://arxiv.org/abs/2301.05549[13] https://arxiv.org/abs/2301.04727[14] https://arxiv.org/abs/2301.09138[15] https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010309[16] https://www.nature.com/articles/s41467-022-35364-5[17] https://www.nature.com/articles/d41586-023-00536-w[18] https://www.quantinuum.com/news/quantum-volume-reaches-5-digits-for-the-first-time-5-perspectives-on-what-it-means-for-quantum-computing
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaAdvancing Neuroimaging
Several Quantum Algorithms have been identified that have potential for Medical benefit. These include Variational Quantum Eigensolvers (VQE), that require relatively low numbers of qubits, can handle large matrices, and are suitable for NISQ quantum computers. In addition, Hopfield networks and Quantum fuzzy feed forward networks can assist as quantum versions of Neural Networks. Quantum Unconstrained Binary Optimization (QUBO) has proven effective for Classification problems, such as in imaging. (1)
Emerging advantages from quantum mechanics are apparent through the principles of Superpositioning, Interference, and Entanglement. Single or Multiple Quantum versions of "gates" are effectively alterations to different configurations of the Identity Matrix. Since qubits are rich in information, and quantum amplitudes can be positive, negative, or complex - quantum solutions for diagnostic and/or predictive purposes can be significant for Neuroradiology, especially as quantum hardware continues to evolve. (2-4)
IBM Qiskit provides intuitive solutions to run and make modifications to quantum circuits such as Bell State, Grover's, and Shor's algorithm on real quantum hardware. A summary of the quantum computer, run times, and probability distributions are readily available to developers. (5-7)
Many researchers have published quantum findings on topics including "Single Qubit Encoding for Classification", QUBO Feature Selection for Classification, along with many applications of Quantum Convolutional Neural Networks for Classification. In specific, Baek, H. et al., revealed a Scalable Quantum Convolutional Neural Network for 3D Point Cloud Images, which may prove valuable for new Raw Brain MRI File analyses. sQCNN-3D promises to utilize less qubits while providing a favorable Reverse Fidelity training. (8-11)
In addition several papers have included approaches to incorporate quantum into Explainable AI. In 2023, Daskin, A., Buge, I., et al, and Heese, R., et al., have all helped progress this relatively new Quantum XAI field utilizing "Ridge Functions", Novel Algorithms, and providing understanding of how Shapley values interact with the quantum realm. (12-14)
Neuroradiologists that are familiar with Artificial Intelligence will likely be utilizing Variational Quantum Algorithms in the near future. De Palma, G., et al. have published findings based on newly developed quantum entropic and concentration inequalities. In addition, VQAs continue to be further understood by Anschuetz, E.R., et al. with a 2022 Nature Communications publication, stating that a Superpolynomially small fraction of local minima within any constant energy from the global minimum exists in the algorithms. (15-16)
In a February 22nd 2023 Nature Article, Using a 49 qubit quantum computer, Google recovered from two simultaneous errors, which had slightly better performance than with 17 qubits. Although this is the first time their larger computer outperformed the smaller, there is no guarantee that using larger circuits will always give better results. (17)
Lastly, in a February 23rd 2023 Quantinuum Publication, the hardware manufacturer achieved 5 digits in Quantum Volume for the first time. The previously published 8,192 QV was surpassed by 32,768 QV, corresponding to 2^15, with 15 presumably meaning 15 qubits. Their corresponding runtimes decreased 3 Times: From over 4 Hours to just over an Hour, which could correspond to faster fully connected quantum neural networks for Radiology utilizing this specific type of ion trap quantum technology. (18)
References are available in the comments below. Have a productive rest of your day.
Smile at end!
2/24/23 "Quantum Coding Advantages for Neuroradiology and Medical Imaging" References[1] https://www.amarchenkova.com/posts/5-quantum-algorithms-that-could-change-the-world[2] https://learn.microsoft.com/en-us/azure/quantum/overview-understanding-quantum-computing[3] https://en.wikipedia.org/wiki/Quantum_logic_gate[4] https://quantum-computing.ibm.com/composer/docs/iqx/guide/entanglement[5] https://qiskit.org/[6] https://quantum-computing.ibm.com/composer/docs/iqx/guide/grovers-algorithm[7] https://quantum-computing.ibm.com/composer/docs/iqx/guide/shors-algorithm[8] https://ieeexplore.ieee.org/document/9798852[9] https://arxiv.org/pdf/2203.13261.pdf[10] https://arxiv.org/abs/2008.07230[11] https://arxiv.org/abs/2210.09728[12] https://arxiv.org/abs/2301.05549[13] https://arxiv.org/abs/2301.04727[14] https://arxiv.org/abs/2301.09138[15] https://journals.aps.org/prxquantum/abstract/10.1103/PRXQuantum.4.010309[16] https://www.nature.com/articles/s41467-022-35364-5[17] https://www.nature.com/articles/d41586-023-00536-w[18] https://www.quantinuum.com/news/quantum-volume-reaches-5-digits-for-the-first-time-5-perspectives-on-what-it-means-for-quantum-computing
Created by Kevin Kawchak Founder CEO ChemicalQDevice2023 San Diego, CaliforniaAdvancing Neuroimaging