Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session DD03: V: Quantum ControlFocus Virtual Only

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Sponsoring Units: DQI Chair: Lindsay Bassman Oftelie, CNR  Pisa; Aditi MisraSpieldenner, Universität des Saarlandes Room: Virtual Room 03 
Tuesday, March 5, 2024 5:30AM  6:06AM 
DD03.00001: A graphtheoretical approach to analyze controllability and its application to qubit systemsMonika Leibscher Invited Speaker: Monika Leibscher The ability to implement any quantum logic gate in a system of coupled qubits is equivalent to evolutionoperator controllability of the qubit system. Controllability analysis can thus be utilized to determine the minimal number of external controls and qubitqubit couplings required for universal quantum computing in a given qubit array [1]. Analyzing controllability of qubit systems faces two main challenges, the exponential scaling of the Hilbert space dimension with the number of qubits and the inherent existence of multiple transitions with equal energy gaps. We present a graphical method that is suitable to handle both challenges [2,3]. As a working example, we apply this graph test to arrays of five qubits, inspired by the ibmq_quito architecture. We find that the number of controls can be reduced from five to one for complex qubit–qubit couplings and to two for standard qubit–qubit couplings [1]. 
Tuesday, March 5, 2024 6:06AM  6:18AM 
DD03.00002: Quantum Optimal Control from a Robotics Perspective Aditya Bhardwaj, Aaron Trowbridge, Kevin He, David I Schuster, Zachary Manchester

Tuesday, March 5, 2024 6:18AM  6:30AM 
DD03.00003: DecoherenceFree Subspaces for Spontaneous Emission modeled by Continuous Spontaneous Localization Alfred Li, Herschel A Rabitz, Benjamin Lienhard Practical quantum information processing depends on long coherence times and fast and accurate quantum control. Understanding the boundaries of quantum system control is crucial to improving quantum processor design. One common challenge of quantum system control is the undetected spontaneous decay of excited states through spontaneous emission. The framework of continuous spontaneous localization can model spontaneous emission. Continuous spontaneous localization is a modification of quantum mechanics that addresses the measurement problem. It suggests that all particles have a constant and random localization process, which leads to an exponential suppression of superposition states with an increasing number of particles. This theory proposes that localization occurs continuously rather than due to a measurement or environmental interaction. In this study, we investigate the compatibility and realizability of decoherencefree subspaces to protect against spontaneous emission modeled by continuous spontaneous localization. Decoherencefree subspaces are quantum subspaces resistant to certain types of noise and decoherence, resulting in nonunitary dynamics due to systembath couplings. By encoding quantum information in a decoherencefree subspace, the information can be protected from environmental disturbances and remain coherent for extended periods of time. 
Tuesday, March 5, 2024 6:30AM  6:42AM 
DD03.00004: Characterization and Calibration of Quantum Processors using Machine Learning Benjamin Lienhard, Herschel A Rabitz The effort required for quantum system control, particularly in terms of measurements during calibration, needs to be low enough to compensate for system parameter changes or fast enough to allow for periodic recalibration. Complete system characterization can provide accurate numerical models for gate calibration. On the other hand, modelfree learning control, although costly and laborious, represents a datadriven calibration technique. As quantum systems increase in size, these methods become more measurementintensive. Here, we use a technique to extract the quantum processor dynamics and map them to a known Hamiltonian and a neural network capturing the unknown dynamics. This combined approach, known as residual modeling, allows for a comprehensive representation of the experimentally observed dynamics with minimal effort. This characterization technique enables loweffort calibration of the closedsystem model. Subsequently, a reinforcementlearning gatecalibration agent is equipped with the acquired model and initiated using a numerically derived guess. Such a lowresource approach could enable scaling up quantum processors calibrated by reinforcement learning agents and sidestep the looming bottleneck of quantum control. 
Tuesday, March 5, 2024 6:42AM  6:54AM 
DD03.00005: Fast quantum logic gates using nonadiabatic LandauZenerStückelbergMajorana transitions Artem Ryzhov, Oleh Ivakhnenko, Sergey Shevchenko, Miguel Fernando GonzalezZalba, Franco Nori A conventional realization of quantum logic gates and control is based on resonant driving, which causes periodic resonant Rabi oscillations of the occupation probability of the quantum system. We study an alternative paradigm for implementing quantum logic gates based on LandauZenerStückelbergMajorana (LZSM) interferometry with nonresonant driving and the alternation of adiabatic evolution and nonadiabatic transitions. Compared to the Rabi oscillations method, the main differences are a nonresonant frequency, large amplitude, and a small number of periods in the external driving. 
Tuesday, March 5, 2024 6:54AM  7:06AM 
DD03.00006: Control Optimization Landscape Analysis of Interacting Quantum Systems Weichen Xie, Baris Ozguler, Gabriel N Perdue, Benjamin Lienhard Finetuning quantum processors is laborintensive, typically relying on intricate experimental approaches, complicated models, and computationally intensive simulations. Constraints of computational resources restrict existing methods from simultaneously calibrating individual quantum gates. Consequently, sequential gate calibration techniques typically neglect the impact of undesired crosstalk and system interactions and, thus, limit the performance of simultaneous gate operations. These interactions and interferences between discrete components of quantum processors and control signals represent a substantial obstacle in calibrating everlarger quantum information processors. Here, we numerically investigate the effects of quantum interactions on quantum gate optimization, particularly its optimization landscape. Prior research has shown that under ideal assumptions, such as controllability, almost all closed, finitedimensional quantum systems have trapfree optimization landscapes, meaning, during gate calibration, the global optimum is almost always reached. We explore how the suboptimal nature of optimization landscapes changes based on the selection of the loss function and the undesired strengths of interactions between qubits. Identifying limits on interaction strengths can inform the feasibility of simultaneous gate calibration routines and thus significantly reduce the calibration effort. 
Tuesday, March 5, 2024 7:06AM  7:18AM 
DD03.00007: Algorithmic cooling with N qubits Lindsay Bassman Oftelie, Michele Campisi, Antonella De Pasquale Quantum computers offer massive advantages over classical computers in terms of execution time and memory efficiency for a subset of problems. While a variety of physical implementations of quantum computers are still being explored, all must fulfill a fundamental set of requirements, one of which is the ability to initialize the qubits into a pure, fiducial quantum state. Such purified, or cooled, qubits are needed for both the initialization of qubits for the computation, as well as for ancillary qubits required throughout the computation for quantum error correction. Thus, a key hurdle in the success of quantum computers is developing methods to generate extremely cold qubits. Algorithmic cooling, a promising technique for purifying qubits, lowers the temperature of a subset of qubits by applying certain logic gates to the entire system of qubits. Here we investigate both optimal and practical algorithmic cooling of a single target qubit using N total qubits in both closed and open systems. We single out the family of algorithms that achieves the minimum temperature for the target qubit in a closed system and find its analytical expression. Within this family, we single out the subfamily of algorithms that achieves maximal cooling with minimal work cost. We also show how changing from a closed to an open system allows for further cooling of the target qubit. When implemented on real devices, distinct cooling algorithms will display distinct performance depending on their complexity. We illustrate the tradeoff between cooling, work cost, and complexity when implementing various Nqubit algorithmic cooling methods on noisy quantum computers. Exploring methods for cooling qubits below levels that physical cooling techniques can practically achieve will bring us one step closer to successful computation with quantum computers in both the near term and the future. 
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