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"A cross-disciplinary introduction to quantum annealing-based algorithms" [37] presents an introduction to combinatorial optimization problems, the general structure of quantum annealing-based algorithms and two examples of this kind of algorithms for solving instances of the max-SAT (maximum satisfiable problem) and Minimum Multicut problems ...
In quantum computing, a quantum algorithm is an algorithm that runs on a realistic model of quantum computation, the most commonly used model being the quantum circuit model of computation. [ 1 ] [ 2 ] A classical (or non-quantum) algorithm is a finite sequence of instructions, or a step-by-step procedure for solving a problem, where each step ...
Adiabatic quantum computing has been shown to be polynomially equivalent to conventional quantum computing in the circuit model. [ 6 ] The time complexity for an adiabatic algorithm is the time taken to complete the adiabatic evolution which is dependent on the gap in the energy eigenvalues ( spectral gap ) of the Hamiltonian.
Quantum processors are difficult to compare due to the different architectures and approaches. Due to this, published physical qubit numbers do not reflect the performance levels of the processor. This is instead achieved through the number of logical qubits or benchmarking metrics such as quantum volume , randomized benchmarking or circuit ...
Neuromorphic quantum computing (abbreviated as ‘n.quantum computing’) is an unconventional type of computing that uses neuromorphic computing to perform quantum operations. It was suggested that quantum algorithms, which are algorithms that run on a realistic model of quantum computation, can be computed equally efficiently with ...
For combinatorial optimization, the quantum approximate optimization algorithm (QAOA) [6] briefly had a better approximation ratio than any known polynomial time classical algorithm (for a certain problem), [7] until a more effective classical algorithm was proposed. [8] The relative speed-up of the quantum algorithm is an open research question.
It uses a quantum annealing process. You essentially define the problem at hand in the form of peaks and valleys on a map, then let quantum particles settle into this map and find the most energy ...
Beyond quantum computing, the term "quantum machine learning" is also associated with classical machine learning methods applied to data generated from quantum experiments (i.e. machine learning of quantum systems), such as learning the phase transitions of a quantum system [18] [19] or creating new quantum experiments. [20] [21] [22]