Science – Page 35 – Investment Policy

Holding On to Qubits: A New Strategy for Stable Trapped Ion Chains

21.11.2025 by Ray Dalio

Researchers have developed a method to significantly reduce errors caused by ion loss in long-chain trapped ion quantum computers, paving the way for more scalable quantum systems.

Taming Quantum Errors with Medusa

21.11.2025 by Ray Dalio

A novel approach to error mitigation uses strategically placed ‘flag’ qubits to improve the reliability of quantum circuits.

Smarter Quantum Circuits: A New Approach to Design

21.11.2025 by Ray Dalio

A novel reward function, QASER-characterized by its exponential formulation and ability to integrate multiple cost factors-prioritizes electronic circuits demonstrating both minimal energy consumption and shallow circuit depth, offering a more nuanced reward signal than traditional approaches to reinforcement learning in quantum architecture search.

Researchers have developed a novel method for automatically designing quantum circuits that achieve both high accuracy and reduced complexity.

Taming Infinity: Robust Control of Distributed Systems

21.11.2025 by Ray Dalio

$The study demonstrates an equivalence between ordinary differential equations, partial differential equations, and polynomial integral equation linear fractional representations, revealing how a nominal system’s stability-modulated by the $\Delta$ operator-and performance-governed by the $\hat{\Delta}$ operator-are intrinsically linked within this framework.$

A new framework extends powerful analysis tools to handle the complexities of spatially distributed systems described by partial integral equations.

Quantum Channels: Boosting Signal Fidelity with Adaptive Diversity

21.11.2025 by Ray Dalio

Researchers have developed a novel framework for optimizing data transmission through quantum multiple-input multiple-output (MIMO) channels, enhancing signal reliability in noisy environments.

Guarding Quantum Minds: Detecting Hidden Attacks in Neural Networks

20.11.2025 by Ray Dalio

The QSentry defense framework integrates poisoning training with forward measurement collection and clustering, creating a system poised to vanish any constructed theory beyond its event horizon.

A new framework, QSentry, offers a robust defense against subtle backdoor attacks targeting the rapidly evolving field of quantum machine learning.

Quantum Images: Preserving Detail for Smarter Neural Networks

20.11.2025 by Ray Dalio

The visualization demonstrates fidelity, a crucial metric in model building where a close alignment between the model's internal representation and the underlying data-often imperfect and noisy-is not merely desirable, but essential for avoiding the amplification of inherent biases and ensuring the model reflects reality, not a distorted echo of it.

A new encoding method boosts the performance of quantum neural networks by ensuring crucial structural information isn’t lost when converting images into a quantum format.

Logic Gates for Qubits: A New Algebraic Approach to Quantum Error Correction

20.11.2025 by Ray Dalio

Researchers have discovered a surprising connection between three-valued logic and the building blocks of quantum computers, offering a novel framework for protecting qubits from errors.

Fragile Quantum Minds: Assessing Machine Learning’s Weakness to Attack

20.11.2025 by Ray Dalio

The study delineates the attack surface within a Quantum Machine Learning pipeline, demonstrating potential threat vectors deployable via black-box, gray-box, or white-box access paradigms.

A new analysis reveals how vulnerable quantum machine learning systems are to both classical and quantum adversarial attacks, demanding a shift towards quantum-native security measures.

Decoding the Unlikely: Probing Quantum Error Correction’s Weak Spots

20.11.2025 by Ray Dalio

$Asymmetric toric codes, when evaluated using a multi-seeded splitting method-specifically with an initial splitting probability of $p_0 = 0.03$ favoring downward splits-yield logical error rate estimates that align with Monte Carlo simulations, with the precision of these estimates influenced by the initial thermalization time $T_{init}$ but not its fundamental value; further analysis reveals that the average weight of failing configurations decreases predictably as the physical error rate diminishes across all chains.$

New techniques allow researchers to accurately characterize the performance of quantum error correction codes, even at extremely low error rates where failures are rare.