No Arabic abstract
We develop new protocols for high-fidelity single qubit gates that exploit and extend theoretical ideas for accelerated adiabatic evolution. Our protocols are compatible with qubit architectures with highly isolated logical states, where traditional approaches are problematic; a prime example are superconducting fluxonium qubits. By using an accelerated adiabatic protocol we can enforce the desired adiabatic evolution while having gate times that are comparable to the inverse adiabatic energy gap (a scale that is ultimately set by the amount of power used in the control pulses). By modelling the effects of decoherence, we explore the tradeoff between speed and robustness that is inherent to shortcuts-to-adiabaticity approaches.
Optimal control theory is a versatile tool that presents a route to significantly improving figures of merit for quantum information tasks. We combine it here with the geometric theory for local equivalence classes of two-qubit operations to derive an optimization algorithm that determines the best entangling two-qubit gate for a given physical setting. We demonstrate the power of this approach for trapped polar molecules and neutral atoms.
Cavity quantum electrodynamic schemes for quantum gates are amongst the earliest quantum computing proposals. Despite continued progress, and the dramatic recent demonstration of photon blockade, there are still issues with optimal coupling and gate operation involving high-quality cavities. Here we show dynamic control techniques that allow scalable cavity-QED based quantum gates, that use the full bandwidth of the cavities. When applied to quantum gates, these techniques allow an order of magnitude increase in operating speed, and two orders of magnitude reduction in cavity Q, over passive cavity-QED architectures. Our methods exploit Stark shift based Q-switching, and are ideally suited to solid-state integrated optical approaches to quantum computing.
We introduce the method of using an annealing genetic algorithm to the numerically complex problem of looking for quantum logic gates which simultaneously have highest fidelity and highest success probability. We first use the linear optical quantum nonlinear sign (NS) gate as an example to illustrate the efficiency of this method. We show that by appropriately choosing the annealing parameters, we can reach the theoretical maximum success probability (1/4 for NS) for each attempt. We then examine the controlled-z (CZ) gate as the first new problem to be solved. We show results that agree with the highest known maximum success probability for a CZ gate (2/27) while maintaining a fidelity of 0.9997. Since the purpose of our algorithm is to optimize a unitary matrix for quantum transformations, it could easily be applied to other areas of interest such as quantum optics and quantum sensors.
$ $In its usual form, Grovers quantum search algorithm uses $O(sqrt{N})$ queries and $O(sqrt{N} log N)$ other elementary gates to find a solution in an $N$-bit database. Grover in 2002 showed how to reduce the number of other gates to $O(sqrt{N}loglog N)$ for the special case where the database has a unique solution, without significantly increasing the number of queries. We show how to reduce this further to $O(sqrt{N}log^{(r)} N)$ gates for any constant $r$, and sufficiently large $N$. This means that, on average, the gates between two queries barely touch more than a constant number of the $log N$ qubits on which the algorithm acts. For a very large $N$ that is a power of 2, we can choose $r$ such that the algorithm uses essentially the minimal number $frac{pi}{4}sqrt{N}$ of queries, and only $O(sqrt{N}log(log^{star} N))$ other gates.
Designing proper time-dependent control fields for slowly varying the system to the ground state that encodes the problem solution is crucial for adiabatic quantum computation. However, inevitable perturbations in real applications demand us to accelerate the evolution so that the adiabatic errors can be prevented from accumulation. Here, by treating this trade-off task as a multiobjective optimization problem, we propose a gradient-free learning algorithm with pulse smoothing technique to search optimal adiabatic quantum pathways and apply it to the Landau-Zener Hamiltonian and Grover search Hamiltonian. Numerical comparisons with a linear schedule, local adiabatic theorem induced schedule, and gradient-based algorithm searched schedule reveal that the proposed method can achieve significant performance improvements in terms of the adiabatic time and the instantaneous ground-state population maintenance. The proposed method can be used to solve more complex and real adiabatic quantum computation problems.