We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits.We illustrate the approach with application to the protection of quantum gates performed on i) a central spin qubit coupling to bath spins through isotropic Heisenberg interactions, ii) superconducting transmon qubits coupling to environmental two-level-systems (TLS) through dipole-dipole interactions, and iii) qubits coupled to both TLS and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient (PG) to optimize the Hamiltonian switching control protocols, using a fidelity objective defined with respect to specific target quantum gates. We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.
We propose a machine learning algorithm for continuous quantum error correction that is based on the use of a recurrent neural network to identity bit-flip errors from continuous noisysyndrome measurements. The algorithm is designed to operate on measurement signals deviating from the ideal behavior in which the mean value corresponds to a code syndrome value and the measurement has white noise. We analyze continuous measurements taken from a superconducting architecture using three transmon qubits to identify three significant practical examples of non-ideal behavior, namely auto-correlation at temporal short lags, transient syndrome dynamics after each bit-flip, and drift in the steady-state syndrome values over the course of many experiments. Based on these real-world imperfections, we generate synthetic measurement signals from which to train the recurrent neural network, and then test its proficiency when implementing active error correction, comparing this with a traditional double threshold scheme and a discrete Bayesian classifier. The results show that our machine learning protocol is able to outperform the double threshold protocol across all tests, achieving a final state fidelity comparable to the discrete Bayesian classifier.
Nonlinear amplifiers, such as the transistor, are ubiquitous in classical technology. Little is understood about the noise properties and applications of quantum nonlinear amplifiers.We introduce a class of nonlinear amplifiers that allow one to measure any normal operator with a linear detector while adding a half-quantum of vacuum fluctuations as noise at the output. When these nonlinear amplifiers are used in conjunction with noisy linear detectors, the resulting measurement in the large gain limit becomes equivalent to ideal projective measurement of the normal operator.
In quantum mechanics, measurement restores a classical notion of reality via collapse of the wavefunction, which yields a precisely defined outcome. On the other hand, the Heisenberguncertainty principle dictates that incompatible observables, such as position and momentum, cannot both take on arbitrarily precise values. But how does a wavefunction evolve when two such quantities are probed simultaneously, and how does the uncertainty principle dynamically inhibit precise measurement outcomes? To realize this unexplored regime, we simultaneously apply two continuous quantum non-demolition probes of non-commuting observables on a superconducting qubit. We achieve this capability by developing a novel measurement scheme that allows us to control the axes of multiple readout channels. We show that the uncertainty principle directly governs the dynamics of the state, and consequently standard wavefunction collapse is replaced by a persistent diffusion that exhibits several distinct regimes. Although evolution of the state now differs drastically from that of a conventional measurement, information about both non-commuting observables is extracted by keeping track of the time ordering of the measurement record, enabling quantum state tomography without alternating measurements. Our work creates new capabilities for quantum control, including rapid state purification, adaptive measurement, measurement-based state steering and continuous quantum error correction. As physical quantum systems interact with their environments via non-commuting degrees of freedom, our work offers a new, more natural approach to experimentally study contemporary quantum foundations.
We use quantum optimal control theory to systematically map out the experimentally reachable parameter landscape of superconducting transmon qubits. With recent improvements in decoherencetimes, transmons have become a promising platform for quantum computing. They can be engineered over a wide range of parameters, giving them great flexibility, but also requiring us to identify good regimes to operate at. Using state-of-the-art control techniques, we exhaustively explore the landscape for the potential creation and distribution of entanglement, for a wide range of system parameters and applied microwave fields. We find the greatest success outside the usually considered dispersive regime. A universal set of gates is realized for gate durations of 50 ns, with gate errors approaching the theoretical limit. Our quantum optimal control approach is easily adapted to other platforms for quantum technology.
The creation of a quantum network requires the distribution of coherent information across macroscopic distances. We demonstrate the entanglement of two superconducting qubits, separatedby more than a meter of coaxial cable, by designing a joint measurement that probabilistically projects onto an entangled state. By using a continuous measurement scheme, we are further able to observe single quantum trajectories of the joint two-qubit state, confirming the validity of the quantum Bayesian formalism for a cascaded system. Our results allow us to resolve the dynamics of continuous projection onto the entangled manifold, in quantitative agreement with theory.