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

Superconducting Quantum Computing: A Review

Over the last two decades, tremendous advances have been made for constructing large-scale quantum computers. In particular, the quantum processor architecture based on superconducting qubits has become the leading candidate for scalable quantum computing platform, and the milestone of demonstrating quantum supremacy was first achieved using 53 superconducting qubits in 2019. In this work, we provide a brief review on the experimental efforts towards building a large-scale superconducting quantum computer, including qubit design, quantum control, readout techniques, and the implementations of error correction and quantum algorithms. Besides the state of the art, we finally discuss future perspectives, and which we hope will motivate further research.Comment: Updated version, Typos corrected, New references added, New discussions adde

Flux-tunable superconducting transmons for quantum information processing

In this thesis, I describe a series of experiments using flux-tunable transmon qubits for quantum information processing. These qubits are designed with different levels of Josephson junction asymmetry. The first two chapters of this thesis will introduce the reader to superconducting qubits and circuit quantum electrodynamics. I will present experiments using the cQED architecture to implement fast photon swapping between an asymmetric qubit and a superconducting resonator using flux-driven sidebands. This is the first experimental observation of flux-driven sidebands in a superconducting system. This process also allows photon swaps between qubit and resonator to first order in the qubit-resonator coupling strength. I will detail an experiment to study and optimize an all-microwave two-qubit gate using the cross-resonance effect. This work constitutes the first experimental study of the cross-resonance effect vs. frequency and confirms effects from the higher energy levels of the transmon in the effective coupling during a cross-resonant drive. Lastly, I will outline a theoretical analysis and initial experiments to study the coherence properties of asymmetric transmons

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

Understanding Quantum Technologies 2022

Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma