Large scale multi-node simulations of $\\mathbb{Z}_2$ gauge theory\n quantum circuits using Google Cloud Platform
Bibliographic record
Abstract
Simulating quantum field theories on a quantum computer is one of the most\nexciting fundamental physics applications of quantum information science.\nDynamical time evolution of quantum fields is a challenge that is beyond the\ncapabilities of classical computing, but it can teach us important lessons\nabout the fundamental fabric of space and time. Whether we may answer\nscientific questions of interest using near-term quantum computing hardware is\nan open question that requires a detailed simulation study of quantum noise.\nHere we present a large scale simulation study powered by a multi-node\nimplementation of qsim using the Google Cloud Platform. We additionally employ\nnewly-developed GPU capabilities in qsim and show how Tensor Processing Units\n-- Application-specific Integrated Circuits (ASICs) specialized for Machine\nLearning -- may be used to dramatically speed up the simulation of large\nquantum circuits. We demonstrate the use of high performance cloud computing\nfor simulating $\\mathbb{Z}_2$ quantum field theories on system sizes up to 36\nqubits. We find this lattice size is not able to simulate our problem and\nobservable combination with sufficient accuracy, implying more challenging\nobservables of interest for this theory are likely beyond the reach of\nclassical computation using exact circuit simulation.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".