Enhancing quantum control by bootstrapping a quantum processor of 12 qubits

Abstract

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate. Recently, Li et al. [24] and later Rebentrost et al. [25] showed that a quantum processor can be used to calculate f and g efficiently. A technique called measurement-based quantum feedback control (MQFC) enables direct measurement of f and g (see Fig. 1), allowing the quantum processor arXiv:1701.01198v2 [quant-ph]

Amplification of the parametric dynamical Casimir effect via optimal control

“…Moreover, if closed loop optimal control is employed, the optimization incorporates unknown and unpredictable drifts into the pulse design as well as makes the pulses robust against statistical disturbances (noise on the pulses and the figure of merit) [33,41,43]. Indeed, we could confirm the robustness of the optimal strategies by numerical simulations of the system evolution steered by the optimized dCRAB pulse (from Fig.…”

“…Finally, we point out that the optimal pulses identified here are expected to work equally well in a (reasonable) noisy environment, due to their intrinsic robustness against small variations, as it has been already theoretically and experimentally showed in many different scenarios [28,[40][41][42][43][44]. Moreover, if closed loop optimal control is employed, the optimization incorporates unknown and unpredictable drifts into the pulse design as well as makes the pulses robust against statistical disturbances (noise on the pulses and the figure of merit) [33,41,43].…”

The experimental realization of high-fidelity ‘shortcut-to-adiabaticity’ quantum gates in a superconducting Xmon qubit

“…A gate error has to be controlled below a fault-tolerant threshold in scale-up quantum computation. Since this error threshold is usually small (0.1%-1%), the experimental realization of high-fidelity quantum gates is an essential task in various artificial quantum systems such as nuclear magnetic resonance [3,4], ion traps [5] and superconducting circuits [6].…”

Relaxation of Rabi dynamics in a superconducting multiple-qubit circuit

“…Additionally, hybridizing these solid-state devices with the atoms may enable the information transfer between macroscopic and microscopic quantum systems [8][9][10][11][12][13], where the superconducting circuits play the role of rapid processor while the atoms act as the long-term memory. Nonetheless, the strong coupling to the environmental noise significantly limits the energy-relaxation (T 1 ) and dephasing (T 2 ) times of superconducting circuits [14][15][16].Recently, there has been focus on large-scale QIP [17,18]. Several network schemes have already been demonstrated in experiments: one-dimensional spin chain with the nearest-neighbor interaction [19,20], twodimensional lattice with quantum-bus-linked qubits [21,22], multiple artificial atoms interacting with the same resonator [17], and many cavities coupled to a superconducting qubit [23].…”