Abstract
Quantum computers require precise control over parameters and careful engineering of the underlying physical system. In contrast, neural networks have evolved to tolerate imprecision and inhomogeneity. Here, using a reservoir computing architecture we show how a random network of quantum nodes can be used as a robust hardware for quantum computing. Our network architecture induces quantum operations by optimising only a single layer of quantum nodes, a key advantage over the traditional neural networks where many layers of neurons have to be optimised. We demonstrate how a single network can induce different quantum gates, including a universal gate set. Moreover, in the few-qubit regime, we show that sequences of multiple quantum gates in quantum circuits can be compressed with a single operation, potentially reducing the operation time and complexity. As the key resource is a random network of nodes, with no specific topology or structure, this architecture is a hardware friendly alternative paradigm for quantum computation.
Highlights
Quantum computers require precise control over parameters and careful engineering of the underlying physical system
We have presented a platform for quantum computing where an underlying set of quantum nodes connects computing qubits and a learning algorithm is used to adapt the system to a particular quantum operation
Several previous works have considered how quantum neural networks can enhance the efficiency of solving classical tasks[25], while others have considered the use of assumed quantum computers[65,66] and quantum annealers[67] in neuromorphic architectures
Summary
A QN is formed with a network of two-level quantum nodes, which are interconnected via quantum tunnelling with random weights and excited with a classical field. Our proposition is to induce quantum operations on the qubits qk by switching on the tunnelling amplitudes Jkl for a time τ. We sample a set of pure input states for the qubits and compute fidelity of the states resulting from the QN compared to the ideal states corresponding to a desired quantum operation. In the quantum circuit model, an arbitrary unitary is approximated with a sequence of single- and two-qubit gates. A single QN induces different quantum gates in a quantum circuit by changing the output tunnelling amplitudes Jkl. a QN has an important advantage. A two-qubit Grover’s algorithm[59,60] can be implemented in one step with a QN, while the circuit model requires several quantum gates in sequence (see Fig. 5). The reduction in the number of gates may help less developed systems to implement examples of complete algorithms, even without having the relatively larger number of coupled qubits available in advanced ultracold ion traps
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
