I'm sure you know all this and can explain it better: the necessary initial quantum state is subject to noise or perturbation which stops the calculation. Then different techniques in section 4 "Physical Implementations" trade-off speed and how long they can stay in the quantum state -- a useful concept I think for the lay reader to understand. Especially if they are evaluating say investment perhaps?
Which certain types of calculations? What is it about factoring numbers, database search, and (if I am not confused) the nature-computation involved in energy finding the reaction center in photosynthesis that makes them amenable to quantum computing? And what is the size and distinguishing marks of the set of these "certain types of calculations" likely to be?
> **Chad Orzel**: Notes Toward "Quantum Computing for Dogs" <https://chadorzel.substack.com/p/notes-toward-quantum-computing-for>: '1. “Properties of Quantum Physics”.... 2. “Certain Types of Calculations”.... 3. “More Efficiently Than Any Classical Computer”.... 4. Physical Implementations.... 5. Errors and the Correction Thereof...
The photosynthesis thing seems like a straightforward example of quantum simulation: You need fewer qubits to simulate the quantum state of a molecule than you would need to simulate it with a classical computer. The question of what precisely defines a problem where we can expect a dramatic quantum speedup— which of the properties of factoring and database search matter the most— remains an open area of research in quantum algorithms. There’s a kind of vague sense of “these are exploring a big solution space with waves” but I think a rigorous definition that would let you reliably identify other areas is still not nailed down.
With respect to size, what matters isn’t a number but a function: it’s how the algorithms SCALE that matters. How fast does the number of operations or resources needed grow with the size of the input? The exact point at which a quantum algorithm takes less clock time than a the best classical one is a moving target, and depends on fiddly details of software and processors and the like. That’s why every claim of quantum supremacy quickly becomes a hot debate over whether they REALLY got it this time or were just fooled by running old classical code.
What’s not disputed is that there WILL BE a scale beyond which the quantum algorithm is undeniably more efficient, and that this threshold is somewhere between the toy models we have now and the actually interesting level where spies and bankers become intensely invested in the factoring of numbers.
I'm sure you know all this and can explain it better: the necessary initial quantum state is subject to noise or perturbation which stops the calculation. Then different techniques in section 4 "Physical Implementations" trade-off speed and how long they can stay in the quantum state -- a useful concept I think for the lay reader to understand. Especially if they are evaluating say investment perhaps?
Which certain types of calculations? What is it about factoring numbers, database search, and (if I am not confused) the nature-computation involved in energy finding the reaction center in photosynthesis that makes them amenable to quantum computing? And what is the size and distinguishing marks of the set of these "certain types of calculations" likely to be?
> **Chad Orzel**: Notes Toward "Quantum Computing for Dogs" <https://chadorzel.substack.com/p/notes-toward-quantum-computing-for>: '1. “Properties of Quantum Physics”.... 2. “Certain Types of Calculations”.... 3. “More Efficiently Than Any Classical Computer”.... 4. Physical Implementations.... 5. Errors and the Correction Thereof...
The photosynthesis thing seems like a straightforward example of quantum simulation: You need fewer qubits to simulate the quantum state of a molecule than you would need to simulate it with a classical computer. The question of what precisely defines a problem where we can expect a dramatic quantum speedup— which of the properties of factoring and database search matter the most— remains an open area of research in quantum algorithms. There’s a kind of vague sense of “these are exploring a big solution space with waves” but I think a rigorous definition that would let you reliably identify other areas is still not nailed down.
With respect to size, what matters isn’t a number but a function: it’s how the algorithms SCALE that matters. How fast does the number of operations or resources needed grow with the size of the input? The exact point at which a quantum algorithm takes less clock time than a the best classical one is a moving target, and depends on fiddly details of software and processors and the like. That’s why every claim of quantum supremacy quickly becomes a hot debate over whether they REALLY got it this time or were just fooled by running old classical code.
What’s not disputed is that there WILL BE a scale beyond which the quantum algorithm is undeniably more efficient, and that this threshold is somewhere between the toy models we have now and the actually interesting level where spies and bankers become intensely invested in the factoring of numbers.