Learning from ChatGPT

#4649 Ising Model & Acoustic & Wave Mechanics Analog Computing D-Wave shows off its new entry in quantum computing race https://arstechnica.com/science/2026/08/d-wave-on-rails-company-tests-entanglement-on-its-dual-rail-qubits/ Ising Model https://en.wikipedia.org/wiki/Ising_model #4649  Evaluate https://arstechnica.com/science/2026/08/d-wave-on-rails-company-tests-entanglement-on-its-dual-rail-qubits/ Summary & Evaluation The article evaluates a milestone reported by D-Wave Quantum Inc. and published in Nature (August 5, 2026) titled "An entangling gate for dual-rail erasure qubits." The research demonstrates a two-qubit entangling gate using a superconducting dual-rail qubit architecture , marking progress toward scalable, fault-tolerant gate-model quantum computing. Key Technical Achievements Dual-Rail Architecture & Built-in Erasure Detection Mechanism: Logical qubits are constructed using a single microwave photon shared between two 3D superconducting...

#4633 Robust AI-Generated Quantum Computing Solutions Framework #4633  Describe a two-pass (or more) framework or process for AI-generating Quantum Computing solutions and validating them via AI Because LLMs are notoriously prone to "hallucinations" in quantum computing—such as inventing non-unitary operations, applying gates across non-adjacent qubits, or miscalculating circuit depth—a single-prompt approach rarely yields a working quantum solution. A multi-pass (Generator-Validator-Refiner) framework treats quantum code generation as a closed-loop engineering task rather than a single text completion. The 3-Pass Quantum AI Generation Framework [ Problem Input ] │ ▼ ┌──────────┐ Draft Code ┌──────────┐ │ PASS 1 │ ──────────> │ PASS 2 │ │ Generator │ │ Verifier │ └──────────┘ └────┬─────┘ ▲ ...

#4577 Emulating Probabilistic Bits (P-Bits) on an RTX 5090 #4577  Describe how can to emulate Probabilistic Bits (p-bits) on an RTX 5090 with 10000+ tensor cores Emulating Probabilistic Bits (p-bits) on a high-density consumer flagship like the RTX 5090 (Blackwell architecture) requires mapping the stochastic network equations onto the GPU's primary strength: massive, low-precision General Matrix Multiply ( GEMM ) operations. A standard p-bit $m_i \in \{-1, +1\}$ updates its state based on its total received local input $I_i$ , governed by a sigmoidal activation function: $$I_i = \sum_{j} J_{ij} m_j + h_i$$ $$m_i = \text{sgn}\left(\tanh(\beta I_i) - r_i\right)$$ Where $J_{ij}$ is the coupling weight matrix (Ising interaction), $h_i$ is the local bias, $\beta$ is the inverse temperature, and $r_i$ is a uniform random number in $[-1, 1]$ . To fully saturate 10,000+ Tensor Cores, you must transform independent scalar updates into a parallelized, batched matrix operation. Here is ...