CritPt benchmark shows state-of-the-art LLMs reach only 5.7% average accuracy on full-scale unpublished physics research tasks, rising to about 10% with coding tools.
Quantum fault tolerance in small experiments
8 Pith papers cite this work. Polarity classification is still indexing.
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abstract
I discuss a variety of issues relating to near-future experiments demonstrating fault-tolerant quantum computation. I describe a family of fault-tolerant quantum circuits that can be performed with 5 qubits arranged on a ring with nearest-neighbor interactions. I also present a criterion whereby we can say that an experiment has succeeded in demonstrating fault tolerance. Finally, I discuss the possibility of using future fault-tolerant experiments to answer important questions about the interaction of fault-tolerant protocols with real experimental errors.
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Error detection is integrated into adaptive quantum circuits for non-equilibrium phase transition simulations by mapping errors to resets, achieving post-selection-free logical simulations near break-even on current hardware.
A 256-atom neutral ytterbium processor demonstrates fault-tolerant entanglement of 24 logical qubits and runs Bernstein-Vazirani on 28 logical qubits with better-than-physical error rates using erasure conversion.
Logical error rates in [[7,1,3]] and [[12,2,4]] codes are suppressed 9.8-800 times below physical rates on trapped-ion hardware, with repeated correction cycles approaching the error rate of two physical CNOTs.
A new encoding scheme for exp(-iθP) into stabilizer codes like [[n,n-2,2]] and [[5,1,3]] achieves 4-7x lower noise than unencoded versions with at most 3% runs discarded after postselection.
Optimized QED intervals plus steady-state extraction enable PEC+QED to deliver 2-11x lower error than PEC alone on Iceberg codes for QAOA.
New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.
citing papers explorer
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Probing the Critical Point (CritPt) of AI Reasoning: a Frontier Physics Research Benchmark
CritPt benchmark shows state-of-the-art LLMs reach only 5.7% average accuracy on full-scale unpublished physics research tasks, rising to about 10% with coding tools.
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Error detection without post-selection in adaptive quantum circuits
Error detection is integrated into adaptive quantum circuits for non-equilibrium phase transition simulations by mapping errors to resets, achieving post-selection-free logical simulations near break-even on current hardware.
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Fault-tolerant quantum computation with a neutral atom processor
A 256-atom neutral ytterbium processor demonstrates fault-tolerant entanglement of 24 logical qubits and runs Bernstein-Vazirani on 28 logical qubits with better-than-physical error rates using erasure conversion.
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Demonstration of logical qubits and repeated error correction with better-than-physical error rates
Logical error rates in [[7,1,3]] and [[12,2,4]] codes are suppressed 9.8-800 times below physical rates on trapped-ion hardware, with repeated correction cycles approaching the error rate of two physical CNOTs.
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Protection of Exponential Operation using Stabilizer Codes in the Early Fault Tolerance Era
A new encoding scheme for exp(-iθP) into stabilizer codes like [[n,n-2,2]] and [[5,1,3]] achieves 4-7x lower noise than unencoded versions with at most 3% runs discarded after postselection.
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Co-Designing Error Mitigation and Error Detection for Logical Qubits
Optimized QED intervals plus steady-state extraction enable PEC+QED to deliver 2-11x lower error than PEC alone on Iceberg codes for QAOA.
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Lower overhead fault-tolerant building blocks for noisy quantum computers
New combinatorial proofs and circuit designs for quantum error correction reduce physical qubit overhead by up to 10x and time overhead by 2-6x for codes including Steane, Golay, and surface codes.
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Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor
Logical quantum kernels outperform physical ones when solving differential equations on a neutral-atom processor, with gains traced to noise error detection in the logical encoding.