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Register a new userProspects for measurement-based quantum computing with solid state spins
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This article aims to review the developments, both theoretical and experimental, that have in the past decade laid the ground for a new approach to solid state quantum computing. Measurement-based quantum computing (MBQC) requires neither direct interaction between qubits nor even what would be considered controlled generation of entanglement. Rather it can be achieved using entanglement that is generated probabilistically by the collapse of quantum states upon measurement. Single electronic spins in solids make suitable qubits for such an approach, offering long coherence times and well defined routes to optical measurement. We will review the theoretical basis of MBQC and experimental data for two frontrunner candidate qubits -- nitrogen-vacancy (NV) centres in diamond and semiconductor quantum dots -- and discuss the prospects and challenges that lie ahead in realising MBQC in the solid state.
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This is a brief review of the experimental and theoretical quantum computing. The hopes for eventually building a useful quantum computer rely entirely on the so-called threshold theorem. In turn, this theorem is based on a number of assumptions, treated as axioms, i.e. as being satisfied exactly. Since in reality this is not possible, the prospects of scalable quantum computing will remain uncertain until the required precision, with which these assumptions should be approached, is established. Some related sociological aspects are also discussed. .
Phononic quantum networks feature distinct advantages over photonic networks for on-chip quantum communications, providing a promising platform for developing quantum computers with robust solid-state spin qubits. Large mechanical networks including one-dimensional chains of trapped ions, however, have inherent and well-known scaling problems. In addition, chiral phononic processes, which are necessary for conventional phononic quantum networks, are difficult to implement in a solid-state system. To overcome these seemingly unsolvable obstacles, we have developed a new network architecture that breaks a large mechanical network into small and closed mechanical subsystems. This architecture is implemented in a diamond phononic nanostructure featuring alternating phononic crystal waveguides with specially-designed bandgaps. The implementation also includes nanomechanical resonators coupled to color centers through phonon-assisted transitions as well as quantum state transfer protocols that can be robust against the thermal environment.
Bells theorem states that no local hidden variable model is compatible with quantum mechanics. Surprisingly, even if we release the locality constraint, certain nonlocal hidden variable models, such as the one proposed by Leggett, may still be at variance with the predictions of quantum physics. Here, we report an experimental test of Leggetts nonlocal model with solid-state spins in a diamond nitrogen-vacancy center. We entangle an electron spin with a surrounding weakly coupled $^{13}C$ nuclear spin and observe that the entangled states violate Leggett-type inequalities by more than four and seven standard deviations for six and eight measurement settings, respectively. Our experimental results are in full agreement with quantum predictions and violate Leggetts nonlocal hidden variable inequality with a high level of confidence.
Solid-state electronic spins are extensively studied in quantum information science, as their large magnetic moments offer fast operations for computing and communication, and high sensitivity for sensing. However, electronic spins are more sensitive to magnetic noise, but engineering of their spectroscopic properties, e.g. using clock transitions and isotopic engineering, can yield remarkable spin coherence times, as for electronic spins in GaAs, donors in silicon and vacancy centres in diamond. Here we demonstrate simultaneously induced clock transitions for both microwave and optical domains in an isotopically purified $^{171}$Yb$^{3+}$:Y$_2$SiO$_5$ crystal, reaching coherence times of above 100 $mu$s and 1 ms in the optical and microwave domain, respectively. This effect is due to the highly anisotropic hyperfine interaction, which makes each electronic-nuclear state an entangled Bell state. Our results underline the potential of $^{171}$Yb$^{3+}$:Y$_2$SiO$_5$ for quantum processing applications relying on both optical and spin manipulation, such as optical quantum memories, microwave-tooptical quantum transducers, and single spin detection, while they should also be observable in a range of different materials with anisotropic hyperfine interaction.
Efficient quantum state measurement is important for maximizing the extracted information from a quantum system. For multi-qubit quantum processors in particular, the development of a scalable architecture for rapid and high-fidelity readout remains a critical unresolved problem. Here we propose reservoir computing as a resource-efficient solution to quantum measurement of superconducting multi-qubit systems. We consider a small network of Josephson parametric oscillators, which can be implemented with minimal device overhead and in the same platform as the measured quantum system. We theoretically analyze the operation of this Kerr network as a reservoir computer to classify stochastic time-dependent signals subject to quantum statistical features. We apply this reservoir computer to the task of multinomial classification of measurement trajectories from joint multi-qubit readout. For a two-qubit dispersive measurement under realistic conditions we demonstrate a classification fidelity reliably exceeding that of an optimal linear filter using only two to five reservoir nodes, while simultaneously requiring far less calibration data textendash{} as little as a single measurement per state. We understand this remarkable performance through an analysis of the network dynamics and develop an intuitive picture of reservoir processing generally. Finally, we demonstrate how to operate this device to perform two-qubit state tomography and continuous parity monitoring with equal effectiveness and ease of calibration. This reservoir processor avoids computationally intensive training common to other deep learning frameworks and can be implemented as an integrated cryogenic superconducting device for low-latency processing of quantum signals on the computational edge.
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