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Much of the research on quantum algorithms that may have applications to AI is centered on quantum machine learning (QML). While I'd argue there are quite a few hypothetical reasons that QML could be used in machine learning some time in the future, QML research is in its infancy relative to classical machine learning research and its practical benefits ...


4

There is good reason to believe that quantum computers will eventually play an important role in drug discovery. Perhaps the best way to show that it's not science fiction is to talk about the startups forming that are focused on QC-enabled drug discovery. The companies that I know of that fit this description are ProteinQure HQS GTN Qulab Riverlane


3

Thinking about the theoretical capabilities of quantum computers has led to important insights on the theory of classical computers. One example is the proof that the (classical) complexity class PP is closed under intersection. While there was already a purely classical proof due to Beigel, Reingold, and Spielman, there exists a simpler proof that uses ...


3

On the practical side: A lot of work is currently going into getting small scale quantum computers to work, and this involves fundamental understanding and manipulation of small quantum systems, be it ions, photons or whatever else. I can only imagine what other uses we would be able to find for having a better grasp on handling these fundamental systems. A ...


2

Quantum Machine Learning (QML) is both young and highly cross-disciplinary, meaning that it will be hard to find courses in specific disciplines (math, physics, computer science, etc) that provide background tailored to the subject. One way to approach the field is to work backwards, by first choosing a specific type of algorithm that's currently popular ...


2

Given that the QFT is exponentially faster than the FFT, The problem with quantum computing is that they are not actually parallel computers: One is tweaking the qubits in such a way that when reading out the result, the desired result gets a high probability. The power of quantum computing comes from the vast phase-space that grows exponentially with ...


2

QFT is used for phase and amplitude estimation and hence it can be found in many application of quantum computing in finance, for example portfolio construction using HHL in its core, Monte Carlo simulation and quantum principal component analysis. There is also application in travelling salesman problem. See list of articles on these applications here: ...


2

Executing a NISQ-device in a manner that asymptotically outperforms a classical computer invalidates the Extended Church-Turing Thesis (ECT). Voluminous tomes written about the (non-extended) Church-Turing Thesis, with implications for branches of philosophy such as the philosophy of mind. The fact that the ECT was not only falsifiable but also is likely ...


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You can participate in and contribute to open-source Qiskit. You can write tools to work with Qiskit and/or other development kits, e.g., my qis_job which makes it easy to run a .qasm file right away. You can write your own toys! See my quantum_yiqing.


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At this early stage of quantum technology development, a useful starting point to understand the "expectation of profitability" is to look at the private markets (angel investing, venture capital, private equity, etc) in addition to public companies. Today there are 76 companies active in quantum computing, those were involved in 178 deals where 260 ...


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