Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning

In this paper, we present a machine learning framework to design high-fidelity multi-qubit gates for quantum processors based on quantum dots in silicon, with qubits encoded in the spin of single electrons. In this hardware architecture, the control landscape is vast and complex, so we use the deep reinforcement learning method to design optimal control pulses to achieve high fidelity multi-qubit gates. In our learning model, a simulator models the physical system of quantum dots and performs the time evolution of the system, and a deep neural network serves as the function approximator to learn the control policy. We evolve the Hamiltonian in the full state-space of the system, and enforce realistic constraints to ensure experimental feasibility

Machine-learning Based Three-Qubit Gate for Realization of a Toffoli Gate with cQED-based Transmon Systems

We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of \u3e99.99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures. We explain our gate design procedure where we enforce realistic constraints, and analyze the new gate’s robustness under decoherence, distortion, and random noise. Our controlled-controlled phase gate in combination with two single-qubit gates realizes a Toffoli gate which is widely used in quantum circuits, logic synthesis, quantum error correction, and quantum games

Designing Gates and Architectures for Superconducting Quantum Systems

Large-scale quantum computers can solve certain problems that are not tractable by currently available classical computational resources. The building blocks of quantum computers are qubits. Among many different physical realizations for qubits, superconducting qubits are one of the promising candidates to realize gate model quantum computers. In this dissertation, we present new multi-qubit gates for nearest-neighbor superconducting quantum systems. In the absence of a physical hardware, we simulate the dynamics of the quantum system and use the simulated environment as a framework for test, design, and optimization of quantum gates and architectures. We explore three different simulation-based gate design methodologies: analytical approach, heuristic method, and machine learning techniques. Furthermore, we propose novel quantum error correction architectures utilizing our new gates, which have reduced computational overhead with better performance and reliability