![]() Table 1 presents the depth of the circuits generated using the proposed strategy, implementation of a version of 11 available at 19 and a non optimized version of the algorithm described in 10. The main difference between the divide-and-conquer state preparation and previous approaches is an exchange between circuit depth by circuit width. The circuit used in the quantum device is described in Fig. The resulting circuit has 10 CNOT operators because a quantum swap was necessary to run this circuit in the real quantum device with a limited qubit connectivity. We remove the last CNOT of the controlled operation because the qubit 2 will be discarded. ![]() The circuit used to obtain this result is described in Fig. The Rome NISQ device has an output very close to the expected result. The single-qubit error was in the order of 1e-4.įigure 5a presents the output of the experiment with 1024 executions using a quantum device simulator and the Rome quantum device. The CNOT error rates were 8.832e-3 (qubits 1 and 2) and 8.911e-3 (qubits 2 and 3). We use qubits 1, 2 and 3 of the ibmq_rome device. For this experimental validation, we chose dimension of data to be small to be compatible with currently available quantum devices, although the time advantage of the proposed method will manifest when a large number of qubits are required for loading high-dimensional data. Loading an input vector \(\vec \rangle \) in a NISQ device as a proof of concept. Even if we represent the information in a basis state that we can copy, the noisy operations and decoherence will corrupt the stored state, and it will be necessary to reload the information from the classical to the quantum device. When a quantum operation is applied, its input is transformed or is destroyed (collapsed). The no-cloning theorem shows that it is not possible to perform a copy of an arbitrary quantum state. The situation is not the same in quantum devices because of the no-cloning theorem 7, noisy quantum operations 8, and the decoherence of quantum information 9. In classical devices, we can use the loaded information several times while we do not erase it. For instance, deep neural networks 4 learning algorithms run in specialized hardware 5, and the computational cost to transfer the information needs to be considered in the total computational cost as data loading can dominate the training time on large-scale systems 6. Loading information into a device is a common task in computer science applications. However, in practical applications, the cost to load the classical information in a quantum device can dominate the asymptotic computational cost of the quantum algorithm 2, 3. ![]() The development of quantum computers can dramatically reduce the time to solve certain computational tasks 1. ![]()
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