arXiv subjects

Julian D. Teske

Publications and source records attributed to Julian D. Teske.

9 recordsLinked to original sources

arXiv2026

Simulating the Dynamics of Markovian Quantum Processes by Quantum Collision Models on Quantum Computers

Hamiltonian dynamics have been widely implemented on noisy intermediate-scale quantum devices in recent years. In contrast, experimental demonstrations of Markovian quantum dynamics remain limited, because implementing nonunitary evolution on quantum computers is challenging. Quantum collision models provide a natural approach to this problem by coupling the system to ancillas to realize dissipation. However, previous implementations of quantum collision models on quantum computers have typically been restricted to one or two system qubits and fewer than 12 time steps, owing to noise, circuit depth, the overhead of ancilla reset, and limited qubit resources. In this work, we experimentally simulate Markovian quantum processes with local and nonlocal dissipation on both trapped-ion and superconducting quantum computers. By employing hardware-specific ancilla strategies, we realize simulations with up to seven system qubits, corresponding to 13 qubits in total, and 40 time steps. Our results demonstrate that, even for the same physical model, the optimal implementation strategy depends strongly on the hardware characteristics of the quantum computer.

quant-ph↗

arXiv2026

Driving Exchange Interaction in Spin Qubits with Quasi-Zero Pulses

The implementation of high-fidelity quantum gates for spin qubits requires accurate control of exchange interactions between electrons confined in quantum dots, but pulse distortions can limit this control accuracy. Although linear-dynamical distortions can be compensated for by appropriately convolving the control signal, determining the necessary convolution requires detailed knowledge of the distortion's transfer function, and therefore the calibration of numerous parameters. Alternatively, control pulses can be designed to have a net-zero time integral canceling out linear-dynamical pulse distortions. We generalize net-zero pulse designs to quasi-zero pulses allowing net-positive but reduced time integrals. Using these pulse designs, we systematically develop complete gate sets for exchange-only qubits, and study the resulting tradeoffs between pulse duration, fidelity, and the required number of tunable parameters, both in simulation and experiment. We benchmark the optimized gate pulses on Intel's Tunnel Falls six-dot device and show they achieve fidelities similar to those obtained with a full filtering approach, with identical pulse durations and fewer tuning parameters. This reduction in complexity opens the door to fast and easily automated calibration schemes compatible with large-scale commercial quantum devices.

quant-ph↗

arXiv2026

Data-Driven Qubit Characterization and Optimal Control using Deep Learning

Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a recurrent neural network (RNN) to predict qubit behavior, our approach enables efficient gradient-based pulse optimization without the need for a detailed system model. First, we sample qubit dynamics using random control pulses with weak prior assumptions. We then train the RNN on the system's observed responses, and use the trained model to optimize high-fidelity control pulses. We demonstrate the effectiveness of this approach through simulations on a single $ST_0$ qubit.

quant-ph↗

arXiv2024

Strategies for enhancing spin-shuttling fidelities in Si/SiGe quantum wells with random-alloy disorder

Coherent coupling between distant qubits is needed for any scalable quantum computing scheme. In quantum dot systems, one proposal for long-distance coupling is to coherently transfer electron spins across a chip in a moving potential. Here, we use simulations to study challenges for spin shuttling in Si/SiGe heterostructures caused by the valley degree of freedom. We show that for devices with valley splitting dominated by alloy disorder, one can expect to encounter pockets of low valley splitting, given a long-enough shuttling path. At such locations, inter-valley tunneling leads to dephasing of the spin wavefunction, substantially reducing the shuttling fidelity. We show how to mitigate this problem by modifying the heterostructure composition, or by varying the vertical electric field, the shuttling velocity, the shape and size of the dot, or the shuttling path. We further show that combinations of these strategies can reduce the shuttling infidelity by several orders of magnitude, putting shuttling fidelities sufficient for error correction within reach.

cond-mat.mes-hall↗

arXiv2023

The SpinBus Architecture: Scaling Spin Qubits with Electron Shuttling

Quantum processor architectures must enable scaling to large qubit numbers while providing two-dimensional qubit connectivity and exquisite operation fidelities. For microwave-controlled semiconductor spin qubits, dense arrays have made considerable progress, but are still limited in size by wiring fan-out and exhibit significant crosstalk between qubits. To overcome these limitations, we introduce the SpinBus architecture, which uses electron shuttling to connect qubits and features low operating frequencies and enhanced qubit coherence. Device simulations for all relevant operations in the Si/SiGe platform validate the feasibility with established semiconductor patterning technology and operation fidelities exceeding 99.9 %. Control using room temperature instruments can plausibly support at least 144 qubits, but much larger numbers are conceivable with cryogenic control circuits. Building on the theoretical feasibility of high-fidelity spin-coherent electron shuttling as key enabling factor, the SpinBus architecture may be the basis for a spin-based quantum processor that meets the scalability requirements for practical quantum computing.

quant-ph↗

arXiv2022

Flopping-mode electron dipole spin resonance in the strong-driving regime

Achieving high fidelity control of spin qubits with conventional electron dipole spin resonance (EDSR) requires large magnetic field gradients of about 1 mT/nm, which also couple the qubit to charge noise, and large drive amplitudes of order 1 mV. The flopping-mode is an alternative method to drive EDSR of an electron in a double quantum dot, where the large displacement between both dots increases the driving efficiency. We propose to operate the flopping-mode in the strong-driving regime to use the full magnetic field difference between the two dots. In simulations, the reduced required magnetic field gradients suppress the infidelity contribution of charge noise by more than two orders of magnitude, while providing Rabi frequencies of up to 60 MHz. However, the near degeneracy of the conduction band in silicon introduces a valley degree of freedom that can degrade the performance of the strong-driving mode. This necessitates a valley-dependent pulse optimization and makes operation to the strong-driving regime questionable.

cond-mat.mes-hall↗

arXiv2021

qopt: An experiment-oriented Qubit Simulation and Quantum Optimal Control Package

Realistic modeling of qubit systems including noise and constraints imposed by control hardware is required for performance prediction and control optimization of quantum processors. We introduce qopt, a software framework for simulating qubit dynamics and robust quantum optimal control considering common experimental situations. To this end, we model open and closed qubit systems with a focus on the simulation of realistic noise characteristics and experimental constraints. Specifically, the influence of noise can be calculated using Monte Carlo methods, effective master equations or with the efficient filter function formalism, which enables the investigation and mitigation of auto-correlated noise. In addition, limitations of control electronics including finite bandwidth effects as well as nonlinear transfer functions and drive-dependent noise can be considered. The calculation of gradients based on analytic results is implemented to facilitate the efficient optimization of control pulses. The software easily interfaces with QuTip, is published under an open source license, well-tested and features a detailed documentation.

quant-ph↗

arXiv2021

Analytic Filter Function Derivatives for Quantum Optimal Control

Auto-correlated noise appears in many solid state qubit systems and hence needs to be taken into account when developing gate operations for quantum information processing. However, explicitly simulating this kind of noise is often less efficient than approximate methods. Here, we focus on the filter function formalism, which allows the computation of gate fidelities in the presence of auto-correlated classical noise. Hence, this formalism can be combined with optimal control algorithms to design control pulses, which optimally implement quantum gates. To enable the use of gradient-based algorithms with fast convergence, we present analytically derived filter function gradients with respect to control pulse amplitudes, and analyze the computational complexity of our results. When comparing pulse optimization using our derivatives to a gradient-free approach, we find that the gradient-based method is roughly two orders of magnitude faster for our test cases. We also provide a modular computational implementation compatible with quantum optimal control packages.

quant-ph↗

arXiv2019

A Machine Learning Approach for Automated Fine-Tuning of Semiconductor Spin Qubits

While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots, and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3 to 5 iterations, corresponding to 10 to 15 minutes of lab-time.

cond-mat.mes-hall↗