Daily Summary for 2026-09-24

Introduction to the show: ident: Quantum Radio. Generated commentary on the latest quantum physics and condensed matter papers.

Mira: Welcome to the show!

Kai: Today we have a special show for you.

The summary: Kai: Welcome everyone to September twenty fourth, twenty twenty six. Today we look at scrambling and noise effects on temporal information processing in quantum systems.

Mira: That’s a key area. We also covered quantum score matching for learning thermal states, which seems promising for modeling physical systems.

Lev: And the repairability of inexact solvers in recursive state estimation using machine learning suggests more robust estimators are possible even with imperfect initial calculations.

Kai: Moving on, there is hybrid quantum-classical attention for molecular profiling in data-limited cancers. How does that compare to mitigating photon loss in linear optical quantum circuits?

Mira: The latter focused on practical issues building those circuits. On the transport side, we looked at guiding coupling control degrees of freedom using quantum probability current reduction.

Lev: That simplifies controlling energy flow during excitation transport. We also saw experimental evidence of generalization in quantum machine learning with small training data sets.

Kai: The most significant work involved using deep learning to interpolate unitaries when Hamiltonians change with time for dynamic control.

Mira: That addresses the need for precise evolution over time in quantum control, which is a fundamental challenge. We also looked at phase-sensitive framed-ribbon representations for Pauli measurements in cluster states.

Lev: That provides a new way to visualize and analyze measurements within those entangled states. Then there was landscape-similarity guided optimization for QAOA algorithms.

Kai: That aims to speed up finding good solutions by using similarity between different problem landscapes during the search.

Mira: Physically, we investigated quantum criticality from spectral collapse in the two-photon Rabi model, showing how sudden energy level changes lead to critical behavior.

Lev: And we studied dynamics of a small system open to a bath with a thermostat to understand equilibrium maintenance in realistic thermal environments.

Kai: Finally, we looked at classical algorithms for estimating expectation values in linear-optical circuits and experimental insights from controlling Josephson junction arrays with cryogenic BiCMOS pulses.

Mira: A diverse day covering information processing, control challenges, and fundamental physics aspects of quantum systems. We have a lot to unpack tomorrow.

Lev: Indeed. The interplay between noise, learning, and fundamental dynamics is quite rich today. We will dive deeper into the details next time.

Kai: Exactly. Let's see how these pieces connect when we review them in part two of our discussion on this research day from September twenty fourth, twenty twenty six.

Mira: I look forward to it. This material on temporal information processing is certainly dense and fascinating for our field.

Lev: It really shows the breadth of what is being explored right now in quantum computation and simulation. It's a very active area.

Kai: Let’s get started then, focusing first on how scrambling and noise affect those temporal information processing capabilities we discussed earlier. We need to be concrete about that.

Mira: Agreed. Let's start with the role of quantum score matching in learning thermal states and what it implies for physical modeling.

Lev: I think that links directly into the robustness we need when dealing with inexact solvers in recursive state estimation using machine learning techniques.

Kai: Right, so we move from modeling states to making our estimators more reliable despite imperfections in the initial calculations. That's a big step forward for us.

Mira: And that reliability is also touched upon by hybrid quantum-classical attention for molecular profiling, which aims to improve data analysis in oncology.

Lev: While that addresses data analysis, we must not forget the practical circuit issues like mitigating photon loss in linear optical circuits. That's a separate engineering hurdle.

Kai: True. We also looked at guiding coupling control degrees of freedom using quantum probability current for excitation transport simplification. It makes the physics cleaner.

Mira: And then there's that deep learning interpolation for time-dependent unitaries, which is crucial for dynamic control challenges in quantum systems.

Lev: That connects to the fundamental challenge of precisely controlling evolution over time when Hamiltonians are changing dynamically.

Kai: Moving to visualization, we examined phase-sensitive framed-ribbon representations for Pauli measurements in cluster states. It helps us see information propagation better.

Mira: That visualization is a powerful tool building on earlier concepts related to quantum measurement representation in these entangled systems.

Lev: And optimization efficiency: landscape-similarity guided optimization for QAOA algorithms speeds up finding good solutions by using landscape similarity during the search process.

Kai: So we have modeling, estimation robustness, application in biology, circuit engineering, control theory via deep learning, and visualization methods all on the table.

Mira: It’s a very broad spectrum today. Let's circle back to the core theme: how do scrambling and noise specifically disrupt temporal information flow in these quantum systems?

Lev: That disruption is what makes understanding these dynamics so vital for unlocking new ways to handle complex data streams moving forward.

Kai: Precisely. We need to be concrete about those dynamics next, Mira, before we move into the deeper physical models like gravitational mediation effects.

Mira: Agreed. The gravitational mediation effect on entanglement between fermionic qubits in dynamical regimes probes things beyond standard quantum mechanics, which is profound.

Lev: That probes fundamental aspects of quantum information in curved spacetime and could inform theories outside the standard framework. It’s deep stuff.

Kai: And we also looked at systems open to a bath with a thermostat to see how they maintain equilibrium when interacting with an environment controlling temperature. That grounds it in reality.

Mira: That thermal interaction is key for understanding realistic physical systems where thermal effects are always present, not idealized ones.

Lev: So we have the fundamental physics of curved spacetime, the practical reality of thermal baths, and the advanced control methods all discussed today.

Kai: Indeed. We also reviewed classical algorithms for estimating expectation values in linear-optical circuits to give us practical tools for analyzing light-based quantum systems.

Mira: And that ties back into circuit analysis, complementing the work on photon loss mitigation we touched upon earlier.

Lev: A very comprehensive day covering theory, application, and experimental tools across many fronts. We have a lot of ground to cover next time.

Kai: Let’s take a brief pause before we dive into the most significant piece: using deep learning to interpolate unitaries for dynamic control. That's where the real breakthrough potential lies today.

Mira: I agree. Interpolating unitaries when Hamiltonians change with time solves a massive challenge in quantum control, suggesting a pathway for more robust time-dependent gate operations.

Lev: It moves us toward truly controllable systems in complex scenarios, which is what we ultimately aim for in this research area.

Kai: So that’s our focus for the next segment: deep learning and time-dependent unitaries. Let's dive into how that interpolation works concretely.

Mira: Ready when you are, Kai. This is where the practical application of deep learning meets fundamental quantum dynamics in a very direct way.

Lev: Let’s explore the techniques used for this interpolation method next, focusing on the structure of those time-dependent Hamiltonians themselves.

Kai: Perfect. We will break down that interpolation pathway and see how it achieves robustness in dynamic gate operations.

Mira: I anticipate a very detailed look at the deep learning architecture employed here, showing exactly how it maps the input to the desired unitary evolution.

Lev: That will give us concrete results on achieving precise evolution over time, which is what’s needed for quantum control.

Kai: Let's proceed then into that specific interpolation study. We need to understand the mechanism of that deep learning approach clearly.

Mira: I'm ready to explain the details of how they achieved this interpolation, focusing on the inputs and outputs precisely as they were reported.

Lev: And we will connect it back to those time-dependent Hamiltonians we discussed earlier, showing the necessary structure for dynamic control.

Kai: Let's do that now. We need to make sense of this complex interplay between learning and time evolution in quantum systems.

Mira: I’ll start by outlining the input data—the changing Hamiltonians—and how the deep learning model learns to map those changes onto the required unitary transformation.

Lev: And then we will discuss the specific loss function or training regimen they used to ensure that interpolation is robust and accurate, even when underlying calculations are imperfect.

Kai: That robustness is key. So, what concrete evidence did they show regarding the accuracy of these interpolated unitaries?

Mira: They demonstrated that this approach achieves a high fidelity in approximating the target unitary evolution, allowing for reliable time-dependent gate operations.

Lev: That fidelity level suggests a very promising pathway toward practical quantum control schemes in noisy environments. It’s not just theoretical; it has measurable accuracy.

Kai: So we have moved from broad concepts to specific mechanisms: input Hamiltonians, learning mapping, and high fidelity output unitaries for dynamic control. That’s concrete progress.

Mira: Exactly. This is how deep learning is tackling the challenge of precise evolution when the system's underlying physics is evolving dynamically in time.

Lev: A very significant piece of work today, Kai. It bridges the gap between complex time-dependent physics and practical, controllable quantum operations.

Kai: Agreed. We will continue to unpack this crucial research stream in our next segment on September twenty fourth, twenty twenty six.

Mira: Looking forward to it! This intersection of machine learning and quantum dynamics is incredibly exciting right now.

Lev: It truly is a rich area for future breakthroughs in quantum information science. We are seeing tangible results emerging from these complex models.

Kai: Let's keep this momentum going as we review the remaining threads from today’s research day in part two of our discussion.

Mira: I’m prepared to break down the phase-sensitive representations and landscape optimization next, if that suits your flow.

Lev: Sounds like a good plan. We have a lot of intricate detail to cover before we wrap up this review for today.

Kai: Let's get into the specifics then. Time for part two of our research review from September twenty fourth, twenty twenty six.

Mira: I’m ready to explain the phase-sensitive representations and how they visualize Pauli measurements within those linear cluster states.

Lev: And then we can discuss how landscape-similarity guided optimization speeds up the search for good solutions in QAOA problems.

Kai: That’s a solid sequence. Visualization, then optimization efficiency. Concrete steps into understanding entanglement structure and search algorithms.

Mira: I'll explain how that visualization method provides a new way to analyze measurements in those specific entangled states, building on prior work.

Lev: And then we will detail the landscape-similarity guided optimization, showing how similarity between landscapes helps speed up finding optimal quantum circuits.

Kai: That sounds like a very focused segment. We move from visualizing entanglement to optimizing the search space for solutions.

Mira: Indeed. It shows how structure and optimization work together to solve complex combinatorial problems in quantum algorithms efficiently.

Lev: This combination of visualization and optimization is powerful for tackling difficult problems in quantum computing today.

Kai: Let’s move into that visualization technique next, Mira, focusing on the framed-ribbon representations for single-qubit Pauli measurements.

Mira: I will walk you through how these representations help us understand how information propagates within linear cluster states during measurement.

Lev: And after that, we can transition smoothly into the optimization approach for QAOA algorithms.

Kai: Perfect flow. We'll connect the visualization of entanglement to the practical search efficiency in those quantum optimization problems.

Mira: Ready to explain how those representations give us a clearer picture of information flow during measurement operations in those specific states.

Lev: And then we’ll discuss landscape-similarity guided optimization, showing how it streamlines the search for good solutions in QAOA algorithms.

Kai: Let’s do that now. Understanding the structure of entanglement visualization and the search heuristics will be key takeaways from this part.

Mira: I'm ready to explain how those representations give us a clearer picture of information flow during measurement operations in those specific states, building on earlier concepts.

Lev: And then we’ll discuss landscape-similarity guided optimization, showing how it streamlines the search for good solutions in QAOA algorithms by using landscape similarity.

Kai: That covers the visualization and the optimization strategy. We have covered a lot of ground today from quantum control to fundamental physics and algorithm efficiency.

Mira: It has been a very dense review, Kai. The material shows how interconnected all these research threads are within the broader field of quantum information processing.

Lev: Absolutely. From gravitational mediation to cryogenic circuits, every piece is vital for building a complete picture of what’s possible in this domain.

Kai: It really is impressive how diverse and deep the research today has been, Mira and Lev. We have covered a lot of ground from scrambling to optimization heuristics.

Mira: We certainly have. Let’s transition now to the final physical models: quantum criticality from spectral collapse in the two-photon Rabi model.

Lev: That delves into non-equilibrium dynamics, showing how sudden energy level changes can lead to critical behavior in light-matter interactions. It’s important for understanding driven systems.

Kai: That connects directly to how systems behave when driven rapidly, which is a huge area of interest for us in controlling quantum phenomena dynamically.

Mira: It shows the limits and behaviors of these interactions under rapid driving conditions, providing insights into non-equilibrium physics that standard equilibrium models miss.

Lev: So we’ve covered the full spectrum: noise effects, learning estimators, molecular profiling, circuit engineering, control interpolation via DL, visualization of entanglement structure, search optimization heuristics, and critical dynamics.

Kai: A truly comprehensive review of September twenty fourth, twenty twenty six's research focus areas. We have concrete material for our next steps.

Mira: I feel very well-equipped now to discuss these points with more depth in future sessions. Thank you both for the thorough review today.

Lev: It was a demanding but incredibly insightful day of research review, Kai and Mira. The interconnectedness of these fields is what makes this work so exciting.

Kai: Agreed. We will synthesize this material further and prepare for part three of our episode on September twenty fourth, twenty twenty six.

Mira: Looking forward to it! This review has set a very high bar for our future discussions on quantum systems dynamics.

Lev: It has certainly done that. The groundwork laid today is substantial. Let’s rest up before diving into the next phase of analysis tomorrow.

Kai: Agreed. Rest up, everyone, because we have a lot more complex material to unpack tomorrow on this research day from September twenty fourth, twenty twenty six.

Mira: See you all then! This review has been excellent and truly illuminating for our work ahead.

Lev: Until next time. The science is moving forward rapidly in these areas. We must keep up the pace.

Kai: We explored a quantum Otto engine design using an anisotropic Heisenberg XYZ model under local magnetic fields. It tests energy conversion in realistic settings, unlike our earlier spectral collapse study.

Mira: I also looked at passive optical superresolution operating at the quantum limit. This is key for advanced sensing without increasing energy input beyond certain bounds.

Lev: The most significant work was testing information capacity with Fock states on cloud photonic processors. Specific tests on binary models challenged prior assumptions about their limits.

Kai: I found a distinction between real and complex aspects in TSS graphs for Hadamard matrices. This suggests how we model states significantly impacts information bounds and error correction protocols.

Mira: We also investigated local tests for unitarily invariant properties of bipartite quantum states to rigorously classify entanglement in these systems.

Lev: Single-qubit position verification showed an impossibility result regarding perfect cheating strategies, meaning complete certainty about location is unattainable without disturbance.

Kai: That contrasts with the capacity studies showing where perfect knowledge breaks down. We also looked at fluctuation thermometry using quantum gas for probing thermal properties beyond standard theorems.

Mira: The imaging of magnetic flux trapping in lanthanum hydride using diamond sensors is important for Majorana bound states, crucial for topological computation.

Lev: This builds on our Coulomb drag studies detecting these states and the development of a modular quantum gas platform, which aids complex simulations.

Kai: The modular platform informs designs from minimally random circuits, refined by hybrid lattice surgery using non-Abelian surface codes for gate design.

Mira: Quantum error-corrected computation of molecular energies provides the practical framework to apply these sophisticated gate designs to chemical problems.

Lev: Exploring topological quantum error correction regimes on a donut geometry is crucial for stabilizing information against errors in robust computation.

Kai: There's also research into microscopic origins of collapse models via decoherence from graviton bremsstrahlung, linking it to dissipation in dynamics.

Mira: Finally, ongoing work aims to build holographic entanglement through measurement, bridging information theory and observable physical processes.

Lev: These studies connect the engine design, capacity limits, and topological error correction frameworks. They all point toward understanding quantum limits physically.

Kai: So, we covered multifractal and glassy signatures in two-dimensional quantum dynamics today. It helps characterize non-ergodic behavior.

Mira: That’s interesting for understanding long-term evolution when systems get trapped in states. What about the Lindbladian spectral statistics?

Lev: We looked at recycling and Liouville space structure to understand open quantum systems dynamics better. It dictates the spectral properties observed during time evolution.

Kai: And we also saw research on learning unknown stabilizer codes using product measurements is quite important for extracting hidden quantum information.

Mira: That links nicely to the all-van-der-waals qubit work, which investigates a specific physical system for quantum computation hardware.

Lev: We also examined the thermodynamic uncertainty of work in time-dependently driven open quantum systems to understand computation limits in noisy settings.

Kai: Then there was efficiency-resolved recovery dynamics for an InGaAs/InP single photon avalanche detector operated in gated mode, crucial for communication performance.

Mira: The depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer showed us the computational resources needed for certain search problems.

Lev: We also looked at R'enyi and Tsallis information entropies for a harmonic position-dependent mass to quantify complexity in different physical contexts.

Kai: Unbounded Holevo additivity gaps in finite dimensions shed light on fundamental limits when dealing with finite degrees of freedom, connecting to spectral optimization for absolutely PPT states.

Mira: And the simplification rules for continuous-time quantum walks on dynamic graphs offer a way to manage complexity in modeling information spread across changing networks.

Lev: We also looked at redesigning the linear--quadratic--Gaussian cost function for feedback cooling, optimizing physical processes through a specific mathematical framework.

Kai: To close out, today's papers include: Role of scrambling and noise in temporal information processing with quantum systems.

Mira: Quantum score matching with applications to learning thermal states uses quantum methods to learn the thermal states of physical systems.

Lev: Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning shows how ML can help fix errors in recursive state estimation solvers.

Kai: Hybrid quantum-classical attention for histopathology-based molecular profiling addresses profiling cancer molecules from limited data.

Mira: Quantum Probability Current Guided Reduction of Coupling Control Degrees of Freedom simplifies the control needed for moving excitations using quantum probability current.

Lev: Experimental evidence of generalization in quantum machine learning in small-data regime proves QML models can generalize even when trained on very little data.

Kai: Mitigating photon loss in linear optical quantum circuits explores ways to reduce photon loss within linear optical circuits.

Mira: Centralised multi link measurement compression with side information discusses compressing measurements across multiple links by using extra side information.

Lev: Dynamics of a small quantum system open to a bath with thermostat investigates how small systems behave when coupled to an environment with a thermostat.

Kai: AC/DC provides tools to automatically compile dynamic quantum circuits into different circuit styles.

Mira: Classical algorithms for estimating expectation values in linear-optical circuits present classical methods for calculating expectation values there.

Lev: Free mutual information and higher-point OTOCs characterize quantum states using free mutual information and higher-order OTOCs.

Kai: Gravitationally mediated entanglement of fermionic qubits examines how entanglement changes when gravity is considered, moving from static to dynamic settings.

Mira: Quantum effects in the magnon spectrum of 2D altermagnets via continuous similarity transformations uses continuous similarity transformations to understand quantum effects there.

Lev: Tomographic characterization of non-Hermitian Hamiltonians in reciprocal space describes how to fully characterize non-Hermitian Hamiltonians using tomography in reciprocal space.

Kai: Electrical drive of a Josephson junction array using a cryogenic BiCMOS pulse generator details the electrical driving of a Josephson junction array using specialized cryogenic electronics.

Mira: Interpolation of unitaries with time-dependent Hamiltonians via Deep Learning uses deep learning to find ways to smoothly interpolate between different quantum unitaries driven by time-dependent Hamiltonians.

Lev: Phase-sensitive framed-ribbon representation of single-qubit Pauli measurements in linear cluster states introduces a phase-sensitive way to represent single-qubit Pauli measurements for linear cluster states.

Kai: Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA uses landscape similarity to guide the optimization process in the quantum approximate optimization algorithm.

Mira: Quantum Criticality from Spectral Collapse in the Two-Photon Rabi Model explores quantum criticality by looking at spectral collapse in a two-photon Rabi model.

Lev: Passive optical superresolution at the quantum limit describes how to achieve superresolution using passive optical techniques at the fundamental quantum limit.

Kai: Quantum Otto engine powered by an anisotropic Heisenberg XYZ model under independent local magnetic fields studies a quantum Otto engine operating under specific magnetic field conditions using an anisotropic Heisenberg XYZ model.

Mira: Quantum Information Flow under String-Diagram Rewriting analyzes how information flows through quantum systems when using string diagrams for rewriting.

Lev: Variable-Cliff Nielsen Geometry and an Exponent-4/3 Lower Bound for the Infinite-Cliff Diameter explores a geometry with variable curvature to find a lower bound on its diameter.

Kai: Information capacity of quantum statistics: Fock-state tests of a discrete binary-sequence model on cloud photonic quantum processors tests the information capacity of quantum statistics using Fock states on photonic processors.

Mira: TSS Graphs for Hadamard Matrices: Real vs Complex compares the structure of TSS graphs for Hadamard matrices in real versus complex settings.

Lev: Mitigating errors by quantum verification and post-selection shows how to reduce errors in quantum computations through quantum verification and post-selection techniques.

Kai: Local Test for Unitarily Invariant Properties of Bipartite Quantum States provides a local test to check properties that are invariant under unitary transformations for bipartite quantum states.

Mira: Impossibility of perfect cheating for single-qubit position verification proves that it is impossible to perfectly cheat when verifying the position of a single qubit.

Lev: Fluctuation thermometry of an atom-resolved quantum gas: Beyond the fluctuation-dissipation theorem develops a method for measuring temperature in an atom-resolved quantum gas that goes beyond the standard fluctuation-dissipation theorem.

Kai: Modeling acceleration without photon pair creation proposes a way to model acceleration without needing to create photon pairs.

Mira: Logical accreditation: a framework for efficient certification of fault-tolerant computations introduces a framework for efficiently certifying fault-tolerant quantum computations.

Lev: Twinned Dynamical Decoupling uses twinned dynamical decoupling to improve the coherence of quantum systems.

Kai: Imaging magnetic flux trapping in lanthanum hydride using diamond quantum sensors describes how to image magnetic flux trapping in lanthanum hydride using diamond sensors as quantum detectors.

Mira: Quantum Coulomb drag signatures of Majorana bound states looks for signatures of Coulomb drag in Majorana bound states using quantum measurements.

Lev: A modular quantum gas platform describes the design and capabilities of a modular platform for studying quantum gases.

Kai: Quantum State Designs from Minimally Random Quantum Circuits shows how to design useful quantum states starting from minimally random circuits.

Mira: Quantum Error-Corrected Computation of Molecular Energies discusses how to compute molecular energies using quantum error correction.

Lev: Low-gate-count block encodings for second-quantized fermionic Hamiltonians presents efficient block encodings for fermionic Hamiltonians using few gates.

Kai: Hybrid Lattice Surgery: Non-Clifford Gates via Non-Abelian Surface Codes uses hybrid lattice surgery with non-Abelian surface codes to implement non-Clifford gates.

Mira: Finite relative entropy for locally squeezed states calculates the finite relative entropy of locally squeezed quantum states.

Lev: Ising on the donut: Regimes of topological quantum error correction from statistical mechanics connects topological quantum error correction regimes to statistical mechanics using an Ising model on a torus.

Kai: Multifractal and Glassy Signatures of Non-Ergodic 2D Quantum Dynamics looks for multifractal and glassy signatures in non-ergodic two-dimensional quantum dynamics.

Mira: Building Holographic Entanglement by Measurement shows how to build holographic entanglement through the process of measurement.

Lev: Lindbladian spectral statistics beyond no-jump Hamiltonians: roles of recycling and Liouville-space structure investigates Lindbladian spectral statistics in systems with recycling and Liouville space structure, going beyond simple no-jump Hamiltonians.

Kai: Rings Around the Ancilla: A Workload-Aware FTQC Architecture proposes a fault-tolerant quantum computing architecture that is aware of workload using rings around an ancilla.

Mira: Relations between different definitions of the quantum Wasserstein distance explores the relationships between various definitions of the quantum Wasserstein distance.

Lev: Microscopic Origins of Collapse Models: Decoherence from Graviton Bremsstrahlung investigates the microscopic origins of collapse models by looking at decoherence caused by graviton bremsstrahlung.

Kai: Linear Algebra of Generalized Contextuality in All Prepare-Transform-Measure Scenarios examines the linear algebra related to generalized contextuality across all prepare-transform-measure scenarios.

Mira: Learning unknown stabilizer codes using product measurements shows how to learn unknown stabilizer codes by using only product measurements.

Lev: An All-van-der-Waals Qubit describes the properties of a qubit that is an all van der Waals type.

Kai: Thermodynamic Uncertainty of Work in Time-Dependently Driven Open Quantum Systems analyzes the thermodynamic uncertainty of work done in open quantum systems driven by time dependence.

Mira: Efficiency-Resolved Recovery Dynamics of an Free-Running InGaAs/InP Single Photon Avalanche Detector Operated in Gated Mode analyzes the recovery dynamics and efficiency of a specific type of single photon avalanche detector.

Lev: Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer analyzes the circuit depth required for the quantum approximate optimization algorithm when using a Grover mixer.

Kai: R'enyi and Tsallis information entropies for a harmonic position-dependent mass applies R'enyi and Tsallis information entropies to systems with a harmonic, position-dependent mass.

Mira: Algorithmic Design of Heralded Linear Optical Circuits for Multipartite Entanglement provides an algorithmic design for linear optical circuits that create multipartite entanglement using heralded measurements.

Lev: Unconventional linear transverse exciton transport in valley-layer coupling two-dimensional materials investigates unusual linear transport of excitons in two-dimensional materials coupled by valley layers.

Kai: Redesigning the linear--quadratic--Gaussian cost function for feedback cooling of a quantum harmonic oscillator redesigns the cost function used for feedback cooling a quantum harmonic oscillator.

Mira: Unbounded Holevo additivity gaps in finite dimensions explores the unbounded nature of Holevo additivity gaps in finite-dimensional systems.

Lev: Benchmarking indirect quantum control schemes via higher-order quantum operations provides a method to benchmark indirect quantum control schemes by using higher-order quantum operations.

Kai: That concludes our review for today. Our next papers are: Role of scrambling and noise in temporal information processing with quantum systems, Quantum score matching with applications to learning thermal states, Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning, Hybrid quantum-classical attention for histopathology-based molecular profiling in data-limited cancers.

Mira: And Quantum Probability Current Guided Reduction of Coupling Control Degrees of Freedom.

Lev: Experimental evidence of generalization in quantum machine learning in small-data regime, Mitigating photon loss in linear optical quantum circuits, Centralised multi link measurement compression with side information.

Kai: Dynamics of a small quantum system open to a bath with thermostat, AC/DC: Automated Compilation for Dynamic Circuits, Classical algorithms for estimating expectation values in linear-optical circuits.

Mira: Free mutual information and higher-point OTOCs, Gravitationally mediated entanglement of fermionic qubits: from static to dynamical limits.

Lev: Quantum effects in the magnon spectrum of 2D altermagnets via continuous similarity transformations, Tomographic characterization of non-Hermitian Hamiltonians in reciprocal space.

Kai: Electrical drive of a Josephson junction array using a cryogenic BiCMOS pulse generator, Interpolation of unitaries with time-dependent Hamiltonians via Deep Learning.

Mira: Phase-sensitive framed-ribbon representation of single-qubit Pauli measurements in linear cluster states, Landscape-Similarity-Guided Optimization in Divide-and-Conquer QAOA.

Lev: Quantum Criticality from Spectral Collapse in the Two-Photon Rabi Model, Passive optical superresolution at the quantum limit.

Kai: Quantum Otto engine powered by an anisotropic Heisenberg XYZ model under independent local magnetic fields, Quantum Information Flow under String-Diagram Rewriting.

Mira: Variable-Cliff Nielsen Geometry and an Exponent-4/3 Lower Bound for the Infinite-Cliff Diameter, Information capacity of quantum statistics: Fock-state tests of a discrete binary-sequence model on cloud photonic quantum processors.

Lev: TSS Graphs for Hadamard Matrices: Real vs Complex, Mitigating errors by quantum verification and post-selection.

Kai: Local Test for Unitarily Invariant Properties of Bipartite Quantum States, Impossibility of perfect cheating for single-qubit position verification.

Mira: Fluctuation thermometry of an atom-resolved quantum gas: Beyond the fluctuation-dissipation theorem, Modeling acceleration without photon pair creation.

Lev: Logical accreditation: a framework for efficient certification of fault-tolerant computations, Twinned Dynamical Decoupling, Imaging magnetic flux trapping in lanthanum hydride using diamond quantum sensors.

Kai: Quantum Coulomb drag signatures of Majorana bound states, A modular quantum gas platform.

Mira: Quantum State Designs from Minimally Random Quantum Circuits, Quantum Error-Corrected Computation of Molecular Energies.

Lev: Low-gate-count block encodings for second-quantized fermionic Hamiltonians, Hybrid Lattice Surgery: Non-Clifford Gates via Non-Abelian Surface Codes.

Kai: Finite relative entropy for locally squeezed states, Ising on the donut: Regimes of topological quantum error correction from statistical mechanics.

Mira: Multifractal and Glassy Signatures of Non-Ergodic 2D Quantum Dynamics, Building Holographic Entanglement by Measurement.

Lev: Lindbladian spectral statistics beyond no-jump Hamiltonians: roles of recycling and Liouville-space structure.

Kai: Rings Around the Ancilla: A Workload-Aware FTQC Architecture, Relations between different definitions of the quantum Wasserstein distance.

Mira: Microscopic Origins of Collapse Models: Decoherence from Graviton Bremsstrahlung, Linear Algebra of Generalized Contextuality in All Prepare-Transform-Measure Scenarios.

Lev: Learning unknown stabilizer codes using product measurements, An All-van-der-Waals Qubit, Thermodynamic Uncertainty of Work in Time-Dependently Driven Open Quantum Systems.

Kai: Efficiency-Resolved Recovery Dynamics of an Free-Running InGaAs/InP Single Photon Avalanche Detector Operated in Gated Mode, Depth analysis of the Quantum Approximate Optimization Algorithm with a Grover mixer.

Mira: R'enyi and Tsallis information entropies for a harmonic position-dependent mass, Algorithmic Design of Heralded Linear Optical Circuits for Multipartite Entanglement.

Lev: Unconventional linear transverse exciton transport in valley-layer coupling two-dimensional materials, Redesigning the linear--quadratic--Gaussian cost function for feedback cooling of a quantum harmonic oscillator.

Kai: Unbounded Holevo additivity gaps in finite dimensions, Benchmarking indirect quantum control schemes via higher-order quantum operations.

Mira: That's all for today. Tune in tomorrow for our next session on: Quantum score matching with applications to learning thermal states. And Repairability of Inexact Solvers in Recursive State Estimation with Machine Learning. Enjoy the rest of your day.