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IBM and collaborators demonstrated three techniques for verifiable quantum advantage on noisy hardware, tackling the challenge of validating results that cannot be classically simulated.
This paper introduces a quantum incremental learning framework using trainable mixed-state prototypes, enabling new classes to be added without increasing circuit width while mitigating catastrophic forgetting.
QuantumMind presents an auditable agentic workflow that automatically generates and screens quantum speedup hypotheses using typed role-specialized actions and a deterministic validator.
A freeCodeCamp handbook explains why quantum circuits behave differently on real hardware than in simulators due to quantum noise, and covers error mitigation and suppression techniques using Qiskit Aer and tools like Orbit.
The author conducts an independent spin audit of IBM's SQD/QSCI iron-sulfur cluster benchmarks, finding that the quantum results converge to high-spin states rather than the target singlet. Using IBM's own archived hardware data, they show the shipped mitigation fails to fix the spin problem, and a Claude-assisted review is provided.
Microsoft's quantum chief Zulfi Alam dismisses scientists' skepticism about the company's Majorana quasiparticle evidence, citing commercial sensitivity and the slow pace of peer review, while critics demand raw data and independent verification.
D-Wave published a Nature paper demonstrating entanglement on its dual-rail qubits, a key validation step for this photon-loss-detectable qubit technology that could simplify quantum error correction.
D-Wave announced a peer-reviewed Nature paper demonstrating a high-fidelity two-qubit entangling gate for its dual-rail erasure qubits, significantly reducing hardware overhead for quantum error correction and advancing practical fault-tolerant gate-model quantum computing.
The paper introduces a hybrid quantum-classical framework enhancing Quantum Physics-Informed Neural Networks (QPINNs) with adaptive collocation point sampling and loss-aware attention for solving differential equations, achieving significant accuracy improvements in fluid dynamics and reaction-diffusion benchmarks.
Quantinuum and NVIDIA, with a pharmaceutical partner, validated a proof-of-principle Generative Quantum AI (GenQAI) framework that combines HPC, AI, and quantum computing to generate and execute quantum circuits for pharmaceutical R&D.
IBM and collaborators report demonstrations of quantum advantage with built-in validation using doped Clifford sampling and error-mitigation techniques, enabling trustworthy quantum computations beyond classical verification.
A detailed overview of the ongoing transition to post-quantum cryptography, covering NIST's standardization of ML-KEM and ML-DSA, the 'harvest now, decrypt later' threat, hybrid key exchange adoption, and the fragmentation of PKI into MTC and X.509 with ML-DSA.
Quantego is a project offering free LEGO build instructions and interactive 3D models of IBM Quantum System One and System Two, with educational quantum circuit simulations.
IBM announces three new entries in its quantum advantage tracker, each using different approaches to overcome errors and validate quantum results, demonstrating quantum advantage in ways that can be trusted even when classical verification is infeasible.
A roundup covering new quantum dot qubit technologies from HRL Laboratories and a processor using diamond vacancies, highlighting progress in scalable quantum computing hardware.
A DigiCert survey reveals that 85% of IT leaders expect quantum computing to break current security standards within a decade, yet only 7% have deployed quantum-safe certificates, leaving most organizations vulnerable to 'harvest now, decrypt later' attacks.
The Genesis Mission aims to create a domestic end-to-end ecosystem integrating AI, quantum computing, and nuclear energy to accelerate scientific discovery and address climate, energy, and health challenges.
QFoldAgent is a closed-loop multi-agent framework for quantum-classical protein structure prediction that iteratively optimizes Hamiltonian penalties using VQE and feedback, achieving improved RMSD and structural validity on 5-residue fragments.
Researchers developed Quantum Elastic Weight Consolidation (QEWC), a framework that uses Quantum Fisher Information to help quantum AI systems retain knowledge during sequential learning, reducing catastrophic forgetting.
NVIDIA unveiled Ising Calibration 1.5, an open-source vision language model that fully automates quantum computer calibration with enhanced in-context learning and improved performance, now deployable on a single GPU.