Bulletin of the American Physical Society
APS March Meeting 2024
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session M56: Quantum Stochastic ProcessesInvited

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Sponsoring Units: GSNP DQI DCMP Chair: Gabriel Landi, University of Rochester Room: 205AB 
Wednesday, March 6, 2024 8:00AM  8:36AM 
M56.00001: Quantum Characterization and Control Using Continuous Weak Measurements Invited Speaker: Irfan Siddiqi Continuous weak measurement provides a unique resource for probing the time evolution of quantum systems. This functionality has been used to faithfully reconstruct individual quantum trajectories in isolated qubits and in entangled pairs coupled to a Markovian bath. We now extend these techniques to execute more complex quantum information processing tasks including tracking nonMarkovian dynamics, continuous quantum error correction, and Hamiltonian reconstruction in superconducting circuits. In particular, we can reconstruct an a priori unknown timedependent process with an algorithm to recover the density matrix from an incomplete set of continuous measurements. We show that it reliably extracts amplitudes of a variety of singlequbit and entangling twoqubit Hamiltonia. We further demonstrate how this technique reveals deviations from a theoretical control Hamiltonian that would have otherwise been missed by conventional techniques, thereby suggesting methods for identifying nonidealities in gates, certifying analog quantum simulators, and performing quantum metrology. 
Wednesday, March 6, 2024 8:36AM  9:12AM 
M56.00002: Thermodynamics of quantum trajectories and its implementation on a quantum computer Invited Speaker: Igor Lesanovsky Quantum computers have recently become available as noisy intermediatescale quantum devices. These machines yield a useful environment for research on quantum systems and dynamics. Building on this opportunity, we investigate opensystem dynamics that are simulated on a quantum computer by coupling a system of interest to an ancilla. After each interaction the ancilla is measured, and the sequence of measurements defines a quantum trajectory. Using a thermodynamic analogy, which identifies trajectories as microstates [1], we show how to bias the dynamics of the open system in order to enhance the probability of quantum trajectories with desired properties, e.g., particular measurement patterns or temporal correlations. We discuss how such a biased, generally nonMarkovian, dynamics can be implemented on a unitary, gatebased quantum computer and show proofofprinciple results on the ibmq_jakarta machine [2]. While our analysis is solely conducted on small systems, it shows a practical way for implementing biased quantum dynamics that allows to investigate fluctuations and access rare events [3]. 
Wednesday, March 6, 2024 9:12AM  9:48AM 
M56.00003: Simulating complex, stochastic processes with quantum physics Invited Speaker: Felix C Binder Stochastic processes with memory are as ubiquitous throughout the quantitative sciences as they are notorious for being difficult to simulate and predict. Weather patterns, stock prices, and biological evolution are just some of the most prominent examples. 
Wednesday, March 6, 2024 9:48AM  10:24AM 
M56.00004: Timeseries quantum reservoir computing with quantum measurements Invited Speaker: Roberta Zambrini The impact of interaction with the environment and measurement is significant in most quantum technologies, but it becomes even more critical in platforms requiring continuous monitoring. A challenging example is (classical) timeseries processing such as speech recognition and chaotic series prediction and the search for enhanced data processing capabilities is driving research into quantum approaches. A promising avenue for sequential data analysis is quantum machine learning, with computational models such as quantum neural networks and reservoir computing (RC). Classical RC displays appealing features such as easy training and energy efficiency, and has recently been proposed in quantum settings. Quantum RC is better suited to quantum state processing and promises enhanced capabilities exploiting the enlarged Hilbert space. However, realtime processing and the achievement of a quantum advantage with efficient use of resources are prominent challenges towards viable experimental realizations. Our goal is to establish how quantum measurements can be efficiently incorporated into a realistic protocol. We discuss the conditions for efficient timeseries processing while maintaining the necessary processing memory and preserving the quantum advantage offered by large Hilbert spaces. Efficient quantum RC is demonstrated, considering a transversefield Ising network as a reservoir, for memory and prediction tasks with two successful measurement protocols. One repeats part of the experiment after each projective measurement. An alternative one uses weak measurements operating online where information can be extracted accurately and without compromising the required memory, despite backaction effects. We also propose a photonic platform suitable for realtime quatum RC. This is based on optical pulses circulating in a closed loop and operating in the continuous variable regime. While ideal operation achieves maximum capacities, statistical noise is shown to undermine any quantum improvement. We propose a strategy to overcome this limitation and maintain QRC performance as the size of the system is scaled up. The role of quantum squeezing is also discussed. 
Wednesday, March 6, 2024 10:24AM  11:00AM 
M56.00005: Thermodynamic unification of optimal transport Invited Speaker: Tan Van Vu Optimal transport is a mature field of mathematics and statistics, focused on the theory of optimal planning and cost associated with the transportation of probability distributions. Recently, a profound connection between optimal transport and stochastic thermodynamics has emerged, particularly in the context of continuousstate overdamped Langevin dynamics. This connection has revealed that the problem of minimizing entropy production can be mapped to the optimal transport problem. Moreover, this connection has led to critical applications, including the establishment of tight speed limits and the finitetime Landauer principle. 
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