Qubit

About: Qubit is a research topic. Over the lifetime, 29978 publications have been published within this topic receiving 723084 citations. The topic is also known as: quantum bit & qbit.

Coherent spin–photon coupling using a resonant exchange qubit

Abstract: Electron spins hold great promise for quantum computation because of their long coherence times. Long-distance coherent coupling of spins is a crucial step towards quantum information processing with spin qubits. One approach to realizing interactions between distant spin qubits is to use photons as carriers of quantum information. Here we demonstrate strong coupling between single microwave photons in a niobium titanium nitride high-impedance resonator and a three-electron spin qubit (also known as a resonant exchange qubit) in a gallium arsenide device consisting of three quantum dots. We observe the vacuum Rabi mode splitting of the resonance of the resonator, which is a signature of strong coupling; specifically, we observe a coherent coupling strength of about 31 megahertz and a qubit decoherence rate of about 20 megahertz. We can tune the decoherence electrostatically to obtain a minimal decoherence rate of around 10 megahertz for a coupling strength of around 23 megahertz. We directly measure the dependence of the qubit–photon coupling strength on the tunable electric dipole moment of the qubit using the ‘AC Stark’ effect. Our demonstration of strong qubit–photon coupling for a three-electron spin qubit is an important step towards coherent long-distance coupling of spin qubits. Coherent coupling between a three-electron spin qubit and single photons in a microwave resonator is demonstrated, which, unlike previous demonstrations, does not require ferromagnetic components near the qubit.

Differentiable learning of quantum circuit Born machines

Abstract: Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. One can efficiently draw samples from the quantum circuits via projective measurements on qubits. However, similar to the leading implicit generative models in deep learning, such as the generative adversarial networks, the quantum circuits cannot provide the likelihood of the generated samples, which poses a challenge to the training. We devise an efficient gradient-based learning algorithm for the quantum circuit Born machine by minimizing the kerneled maximum mean discrepancy loss. We simulated generative modeling of the BARS-AND-STRIPES dataset and Gaussian mixture distributions using deep quantum circuits. Our experiments show the importance of circuit depth and the gradient-based optimization algorithm. The proposed learning algorithm is runnable on near-term quantum device and can exhibit quantum advantages for probabilistic generative modeling.

Deterministic secure communication protocol without using entanglement

Abstract: We show a deterministic secure direct communication protocol using single qubit in mixed state. The security of this protocol is based on the security proof of BB84 protocol. It can be realized with current technologies.

Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity

Abstract: We construct quantum circuits which exactly encode the spectra of correlated electron models up to errors from rotation synthesis. By invoking these circuits as oracles within the recently introduced "qubitization" framework, one can use quantum phase estimation to sample states in the Hamiltonian eigenbasis with optimal query complexity $O(\lambda / \epsilon)$ where $\lambda$ is an absolute sum of Hamiltonian coefficients and $\epsilon$ is target precision. For both the Hubbard model and electronic structure Hamiltonian in a second quantized basis diagonalizing the Coulomb operator, our circuits have T gate complexity $O({N + \log (1/\epsilon}))$ where $N$ is number of orbitals in the basis. This enables sampling in the eigenbasis of electronic structure Hamiltonians with T complexity $O(N^3 /\epsilon + N^2 \log(1/\epsilon)/\epsilon)$. Compared to prior approaches, our algorithms are asymptotically more efficient in gate complexity and require fewer T gates near the classically intractable regime. Compiling to surface code fault-tolerant gates and assuming per gate error rates of one part in a thousand reveals that one can error correct phase estimation on interesting instances of these problems beyond the current capabilities of classical methods using only about a million superconducting qubits in a matter of hours.

qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor

Abstract: The resources required to run a high-accuracy variational quantum eigensolver algorithm with a dynamically created ansatz are quantified and reduced significantly, easing the quantum simulation of many-body systems.