Quantum mechanics is indeed a probabilistic theory that describes the behavior of particles and systems at the quantum level. However, quantum computers can still provide deterministic outcomes for certain types of calculations, even though the underlying physics is probabilistic. This apparent contradiction is often referred to as the "measurement problem" in quantum mechanics.
In a quantum computer, information is stored in quantum bits, or qubits, which can exist in superpositions of multiple states simultaneously. Quantum algorithms take advantage of this superposition property and use quantum operations to manipulate qubits in parallel, allowing for potentially faster computations in certain cases.
When a quantum computer performs a calculation, the final result is obtained by measuring the qubits at the end of the computation. At the moment of measurement, the probabilistic nature of quantum mechanics comes into play. The measurement collapses the superposition of states into a definite outcome, and the result is a classical bit (0 or 1).
However, the probabilities associated with different outcomes can be influenced by the quantum algorithm and the specific state of the qubits. Through careful design and optimization of the quantum algorithm, it is possible to bias the probabilities in such a way that the desired outcome has a higher likelihood of being observed upon measurement.
Furthermore, quantum error correction techniques are employed to minimize the effects of noise and decoherence, which can introduce errors and randomness into the computation. By redundantly encoding information in a larger number of qubits and implementing error correction protocols, quantum computers can mitigate the impact of probabilistic errors and improve the reliability of the results.
So, while the underlying physics of quantum mechanics is probabilistic, quantum computers can leverage probabilistic phenomena and algorithms to achieve deterministic outcomes for specific calculations, aided by error correction techniques.