Tensor computing and quantum computing are two distinct fields with different applications. Tensor networks, such as MPS, PEPS, TTNs, and MERA, have been successfully used in classical machine learning and quantum machine learning, where they can be mapped to quantum computers for improved performance. These tensor networks are efficient for preparing ground states on classical computers and can be combined with quantum processors for tasks like time evolution, which can be intractable on classical computers. On the other hand, quantum computers aim to outperform classical computers in various computational tasks by utilizing the principles of quantum mechanics. Here is a quick comparison between quantum computing and tensor computing:
Quantum Computing:
1- Based on principles of quantum mechanics - uses quantum bits (qubits) that can exist in a superposition of 0 and 1
2- Leverages quantum phenomena like entanglement and interference
3- Can solve certain problems exponentially faster than classical computers (Grover's algorithm, Shor's algorithm, etc)
4- Still in the early stages of development with small-scale quantum computers built
5- Potential applications in cryptography, machine learning, molecular modeling, etc.
Tensor Computing:
1- Based on multidimensional array data structures called tensors
2- Used extensively in deep learning and AI for parameters and dataset representations
3- Leverages tensors for efficient parallel data processing and manipulation
4- Scales well on classical hardware like GPUs through frameworks like TensorFlow
5- Already in use in many machine learning applications like computer vision, NLP, etc.
For more information and details, please see:
https://www.linkedin.com/feed/update/urn:li:activity:7160555391280398336?utm_source=share&utm_medium=member_desktop
Hi everyone,
Finally, in a good news, scientists in a case study managed to show that tensor networks styles are capable of competing and even superior to quantum computing.
Tensor computing > Quantum Computing.
Please, see the original article.
https://lnkd.in/duKeYe2W
Hi everyone,
Please see this:
https://www.linkedin.com/posts/hamid-reza-yazdani-74565910b_quantumcomputing-tensorcomputing-manifoldlearning-activity-7169951623878852608-RJqs?utm_source=share&utm_medium=member_android
Quantum Computing by Mathematics: achieving tomorrow's technology with today's tools. In continuation of the previous posts, despite the advances to achieve the power of quantum computing at temperatures such as 1 Kelvin and efforts to use photonic and laser styles to achieve this power at room temperature, it is still far from reaching the level of using quantum computers in consumers' homes with reasonable costs. As mentioned, computing styles based on artificial intelligence and neural, cognitive, and neuromorphic computing are also being developed. Of course, these methods seek to imitate one hundred percent the behavior of human brain calculations in machines, so that they can convey most of the possible abilities and states. Meanwhile, using computational tools from various branches of mathematics such as algebra, analysis, and geometry is a completely different approach. Changing the basis of calculations from binary scalar states to vectors, matrices and other high-dimensional tools such as tensors and manifolds is a style that is very close to quantum states and gates. Of course, the origin of this style was trying to simulate states and quantum computing with mathematical algorithms on classical computers, as well as managing big data and multidimensional data with high dimensions.
For more information, please see:
https://www.linkedin.com/posts/hamid-reza-yazdani-74565910b_quantum-quantumcomputing-tensor-activity-7188787756947423232-tjqG?utm_source=share&utm_medium=member_desktop
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