Quantum state preparation protocol for encoding classical data into the amplitudes of a quantum information processing register's wave function
arXiv:2107.14127 · doi:10.1103/PhysRevResearch.4.013091
Abstract
We present a protocol for encoding real numbers stored in memory registers into the amplitudes of the quantum superposition that describes the state of qubits. This task is one of the main steps in quantum machine learning algorithms applied to classical data. The protocol combines partial CNOT gate rotations with probabilistic projection onto the desired state. The number of additional ancilla qubits used during the implementation of the protocol, as well as the number of quantum gates, scale linearly with the number of qubits in the processing register and hence logarithmically with . The average time needed to successfully perform the encoding scales logarithmically with the number of qubits, in addition to being inversely proportional to the acceptable error in the encoded amplitudes. It also depends on the structure of the data set in such a way that the protocol is most efficient for non-sparse data.
arXiv comment17 pages (preprint)
References in corpus (24)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Quantum Computing
- Supervised learning with quantum enhanced feature spaces
- Quantum support vector machine for big data classification
- Quantum machine learning in feature Hilbert spaces
- Superconducting Qubits: Current State of Play
- Strong quantum computational advantage using a superconducting quantum processor
- Quantum algorithms: an overview
- Quantum random access memory
- Natural and artificial atoms for quantum computation
- Quantum Generative Adversarial Networks for Learning and Loading Random Distributions
- Digital quantum simulation of fermionic models with a superconducting circuit
- Experimental Realization of Quantum Artificial Intelligence
- Creating superpositions that correspond to efficiently integrable probability distributions
- Quantum circuits of T-depth one
- Entanglement-Based Machine Learning on a Quantum Computer
- Circuit-Based Quantum Random Access Memory for Classical Data
- On the robustness of bucket brigade quantum RAM
- Selective darkening of degenerate transitions demonstrated with two superconducting quantum bits
- Black-box quantum state preparation without arithmetic
- Fault tolerant resource estimation of quantum random-access memories
- Efficient quantum algorithm for preparing molecular-system-like states on a quantum computer
- Measurement-Free Ultrafast Quantum Error Correction by Using Multi-Controlled Gates in Higher-Dimensional State Space
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