Quantum gate learning in engineered qubit networks: Toffoli gate with always-on interactions

Abstract

We put forward a strategy to encode a quantum operation into the unmodulated dynamics of a quantum network without the need of external control pulses, measurements or active feedback. Our optimization scheme, inspired by supervised machine learning, consists in engineering the pairwise couplings between the network qubits so that the target quantum operation is encoded in the natural reduced dynamics of a network section. The efficacy of the proposed scheme is demonstrated by the finding of uncontrolled four-qubit networks that implement either the Toffoli gate, the Fredkin gate, or remote logic operations. The proposed Toffoli gate is stable against imperfections, has a high-fidelity for fault tolerant quantum computation, and is fast, being based on the non-equilibrium dynamics.

8 pages, 3 figures

References in corpus (13)

• QuTiP 2: A Python framework for the dynamics of open quantum systems
• Realization of a scalable Shor algorithm
• Realization of the quantum Toffoli gate with trapped ions
• Realizable Hamiltonians for Universal Adiabatic Quantum Computers
• High-Fidelity Single-Shot Toffoli Gate via Quantum Control
• Non-Perturbative Entangling Gates between Distant Qubits using Uniform Cold Atom Chains
• Simulation of Many-Body Hamiltonians using Perturbation Theory with Bounded-Strength Interactions
• Experimental Estimation of Average Fidelity of a Clifford Gate on a 7-qubit Quantum Processor
• Dicke simulators with emergent collective quantum computational abilities
• Quantum Logic between Remote Quantum Registers
• Perfect wave-packet splitting and reconstruction in a one-dimensional lattice
• The distribution of quantum fidelities
• Scalable quantum computation architecture using always-on Ising interactions via quantum feedforward

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