{"schema_version":"1.0","updated_at":"2026-09-26","total":23,"limit":50,"offset":0,"sort":"-date","records":[{"id":"esen-petase-53816-2026","title":"Reactive ML dynamics for a solvated enzyme","summary":"eSEN-omol treats complete solvated enzymes; its largest listed throughput benchmark is a 53,816-atom PETase system.","approach":"classical","workload":"molecular-dynamics","comparison_group":null,"date":"2026-09-16","date_kind":"preprint","date_note":"Verified arXiv v2 revision date; first submission 2026-09-08. Computation date is not reported.","sizes":[{"value":53816,"unit":"atoms","label":"Solvated PETase acylation system","note":"Entire learned-potential system, not a QM active region."}],"resources":{"hardware":"NVIDIA H200 140 GB GPUs","cpu_cores":null,"gpus":32,"qpus":null,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Table S1: 0.97 million 1-fs steps/day on 32 GPUs. This allocation describes the throughput benchmark, not every production trajectory."},"accuracy":"Study compares enzyme reaction behavior with experiments and DFT; DFT checks use selected active-site clusters, not the entire solvated system.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["mlqmmm","mlpot"],"sources":[{"title":"Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential","url":"https://arxiv.org/abs/2609.09293v2","year":2026,"status":"Preprint; v2"}],"relations":[],"limitation":"Learned potential, not an explicit QM/MM partition. Claimed 1000× speedup is an approximate literature comparison, not matched timing."},{"id":"xlsdft-battery-interface-2026","title":"Electronic structure of an 11-million-atom interface","summary":"ML-potential dynamics supplies a Li/LGPS/Li structure for a large DFT electronic analysis.","approach":"classical","workload":"materials","comparison_group":null,"date":"2026-09-11","date_kind":"preprint","date_note":"arXiv submission; computation date unknown.","sizes":[{"value":11325600,"unit":"atoms","label":"Whole battery interface","note":"Exact count in Figure 6."}],"resources":{"hardware":"LineShine Armv9 LX2 processors","cpu_cores":null,"gpus":null,"qpus":null,"nodes":null,"wall_seconds":6936.2,"total_flops":null,"peak_flops_per_second":null,"precision":"Mostly FP64; FP16/FP32 auxiliaries","note":"Table III total includes PDOS analysis; 167 SCF iterations. Allocation not separately pinned here."},"accuracy":"Electronic trends compared with XPS depth profiles.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["dft","mlpot"],"sources":[{"title":"XLSDFT: Li/LGPS interface, Figure 6 and Table III","url":"https://arxiv.org/abs/2609.13115v1","year":2026,"status":"Preprint; v1"}],"relations":[],"limitation":"DFT postprocessing of an ML-generated configuration; not DFT at every MD step."},{"id":"xlsdft-silicon-200m-2026","title":"200-million-atom silicon DFT capability run","summary":"XLSDFT converges a silicon crystal with localized Kohn–Sham calculations on LineShine.","approach":"classical","workload":"materials","comparison_group":null,"date":"2026-09-11","date_kind":"preprint","date_note":"arXiv v1 submission; computation date unknown.","sizes":[{"value":200000000,"unit":"atoms","label":"Whole silicon crystal","note":"Rounded count reported for this capability run."}],"resources":{"hardware":"LineShine Armv9 LX2 processors","cpu_cores":null,"gpus":null,"qpus":null,"nodes":20480,"wall_seconds":2280.4,"total_flops":null,"peak_flops_per_second":null,"precision":"Mostly FP64; FP16 stored guesses and FP32 preconditioner","note":"Table III total includes initialization, I/O and energy evaluation; seven SCF iterations."},"accuracy":"Relative density residual threshold 5e-4.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"benchmark","method_ids":["dft"],"sources":[{"title":"Extreme-Scale Linear-Scaling Kohn-Sham DFT at 100 Million Atoms: Bridging Quantum Simulations and Experiments","url":"https://arxiv.org/abs/2609.13115v1","year":2026,"status":"Preprint; v1"}],"relations":[],"limitation":"Homogeneous-crystal benchmark with localization and GGA approximations; not exact many-electron dynamics."},{"id":"sqd-trpcage-fragments-2026","title":"Protein conformer energies from quantum fragments","summary":"Wavefunction embedding combines FCI and hardware SQD for two conformers of the 303-atom Trp-cage miniprotein.","approach":"hybrid","workload":"chemistry","comparison_group":null,"date":"2026-06-04","date_kind":"publication","date_note":"Final journal publication; first preprint appeared 2025-12-18. Computation date is not reported.","sizes":[{"value":303,"unit":"atoms","label":"Complete miniprotein","note":"Final paper's count; the preprint abstract rounded to 300."},{"value":33,"unit":"spatial-orbitals","label":"Largest embedded cluster","note":"Fragment plus bath; not the entire protein on one circuit."}],"resources":{"hardware":"IBM Heron-R2 ibm_fez and ibm_marrakesh; classical embedding and diagonalization","cpu_cores":null,"gpus":null,"qpus":2,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Two named QPUs were used across experiments, not as one coupled device. One million measurement outcomes per circuit."},"accuracy":"Conformer gap 55.43 kcal/mol versus 52.05 kcal/mol from unfragmented DLPNO-CCSD; this is a method comparison, not exact error.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["sqd","embwf","fci"],"sources":[{"title":"Molecular Quantum Computations on a Protein","url":"https://doi.org/10.1021/acs.jctc.6c00364","year":2026,"status":"Peer-reviewed; JCTC"}],"relations":[],"limitation":"Fragmented electronic energies for two structures; no whole-protein coherent circuit, folding dynamics, or demonstrated quantum speedup."},{"id":"dwave-tensor-networks-2026","title":"D-Wave response: 192-qubit correlation convergence","summary":"Lattice-specific tensor networks evaluated all 18,336 two-point correlations of a 192-qubit diamond model after a 7 ns quench.","approach":"classical","workload":"quantum-dynamics","comparison_group":"dwave-spin-glass-dynamics-2025","date":"2026-05-21","date_kind":"publication","date_note":"Science publication; initial preprint appeared 7 March 2025. This record uses the updated published study; individual run dates are unknown.","sizes":[{"value":192,"unit":"qubits","label":"Simulated qubits in correlation-convergence test"}],"resources":{"hardware":"CPU/GPU tensor-network implementation","cpu_cores":null,"gpus":null,"qpus":null,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Timing studies report 8-core Intel CPUs and an NVIDIA RTX A6000, but do not isolate a complete runtime/allocation for this 192-qubit all-correlator test."},"accuracy":"Increasing bond dimension and loop-correction order yields roughly 0.02 relative correlation disagreement. This is internal convergence evidence, not comparison with an exact 192-qubit state.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"model","method_ids":["tensor_networks"],"sources":[{"title":"Dynamics of disordered quantum systems with two- and three-dimensional tensor networks","url":"https://doi.org/10.1126/science.adx2728","year":2026,"status":"Published paper"}],"relations":[{"target":"dwave-spin-glass-2025","kind":"responds-to","note":"Revisits the annealing models with geometry-adapted networks and belief propagation."}],"limitation":"Accuracy depends on geometry, quench time and observable. The paper also runs 900-qubit models with error proxies; that larger size is not substituted for this convergence-tested result."},{"id":"dmrg-femoco-blackwell-emulation-2026","title":"FeMoco DMRG with emulated FP64","summary":"INT8-sliced arithmetic reproduces native-FP64 DMRG energies for FeMoco active spaces up to 113 electrons in 76 orbitals.","approach":"classical","workload":"chemistry","comparison_group":null,"date":"2026-04-20","date_kind":"publication","date_note":"Online publication date; computation date is not reported.","sizes":[{"value":113,"unit":"electrons","label":"FeMoco active electrons"},{"value":76,"unit":"spatial-orbitals","label":"FeMoco active spatial orbitals"}],"resources":{"hardware":"NVIDIA DGX B200; DGX H100 comparison","cpu_cores":null,"gpus":null,"qpus":null,"nodes":1,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":"Native FP64 versus Ozaki INT8-sliced FP64 emulation","note":"Each benchmark uses one DGX node. GPU count and complete-run cost are not inferred from the product name."},"accuracy":"MilliHartree agreement concerns arithmetic relative to native-FP64 DMRG, not error against an exact molecular solution.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["dmrg"],"sources":[{"title":"Mixed-Precision Ab Initio Tensor Network State Methods Adapted for NVIDIA Blackwell Technology via Emulated FP64 Arithmetic","url":"https://doi.org/10.1021/acs.jctc.6c00203","year":2026,"status":"Peer-reviewed; JCTC"}],"relations":[],"limitation":"Measured on B200, not B300 or NVFP4. Eager emulation can be slower; this is not a general acceleration claim."},{"id":"dmrg-fe4s4-b200-2026","title":"Fe–S classical reference on B200","summary":"Spin-adapted DMRG supplies a stronger classical reference for the Fe4S4 CAS(54,36) chemistry benchmark.","approach":"classical","workload":"chemistry","comparison_group":null,"date":"2026-03-30","date_kind":"preprint","date_note":"arXiv v1 submission date; computation date is not reported.","sizes":[{"value":54,"unit":"electrons","label":"Active electrons"},{"value":36,"unit":"spatial-orbitals","label":"Active spatial orbitals"}],"resources":{"hardware":"NVIDIA DGX B200","cpu_cores":null,"gpus":null,"qpus":null,"nodes":1,"wall_seconds":45360,"total_flops":null,"peak_flops_per_second":null,"precision":"Native FP64 for the timed reference; FP64 emulation also tested","note":"12.6 hours for the reported finite-bond calculation. The reported maximum 220 TFLOP/s is a performance rate, not a total operation count."},"accuracy":"Finite-bond energy −327.24466 Ha; two extrapolations give −327.2471 and −327.2469 Ha. Their agreement is not a rigorous error bound.","evidence":"mixed","claim_role":"classical-response","chemistry_relevance":"direct","method_ids":["dmrg"],"sources":[{"title":"Hunting for quantum advantage in electronic structure calculations is a highly non-trivial task","url":"https://arxiv.org/abs/2603.28648v1","year":2026,"status":"Preprint; v1"}],"relations":[{"target":"sqd-fe4s4-heron-fugaku-2025","kind":"compares-with","note":"Discusses the Fe4S4 active-space benchmark and cites SQD; this is not a matched end-to-end runtime comparison."}],"limitation":"Model-specific reference, not a universal refutation of quantum advantage. B300 appears only as a future opportunity."},{"id":"dwave-classical-evaluation-2025","title":"D-Wave reply: testing tensor-network error scaling","summary":"New annealer measurements challenged assumptions used to extrapolate belief-propagation tensor-network accuracy, including 128-qubit cubic-dimer inputs.","approach":"quantum","workload":"quantum-dynamics","comparison_group":"dwave-spin-glass-dynamics-2025","date":"2025-08-21","date_kind":"preprint","date_note":"First arXiv submission; no journal version identified in this source audit. Individual acquisition dates are unknown.","sizes":[{"value":128,"unit":"qubits","label":"Qubits in largest cubic-dimer comparison"}],"resources":{"hardware":"D-Wave Advantage2 prototype","cpu_cores":null,"gpus":null,"qpus":1,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Same prototype family as the earlier dynamics study; reported model quench duration is not a wall-clock computation time."},"accuracy":"At small sizes, comparison uses MPS references. At 128 qubits, inferred classical errors use the QPU as an approximate reference and assumptions about error independence and size dependence.","evidence":"mixed","claim_role":"demonstration","chemistry_relevance":"model","method_ids":["analog","tensor_networks"],"sources":[{"title":"Evaluating classical simulations with a quantum processor","url":"https://arxiv.org/abs/2508.15759","year":2025,"status":"Preprint"}],"relations":[{"target":"dwave-tensor-networks-2026","kind":"responds-to","note":"Responds to the 2025 preprint of the later 2026 publication; tests its accuracy-scaling predictions."},{"target":"dwave-spin-glass-2025","kind":"extends","note":"Adds targeted quantum/classical comparisons for the same family of annealing models."}],"limitation":"A bounded challenge to specific approximations and scaling claims, not proof that every classical approach fails. At the largest size the QPU reference is itself approximate."},{"id":"sqd-fe4s4-heron-fugaku-2025","title":"77-qubit Fe–S sample-based diagonalization","summary":"Heron samples and Fugaku diagonalization produce variational Fe4S4 energies beyond full exact diagonalization. The same study also treats nitrogen dissociation and Fe2S2.","approach":"hybrid","workload":"chemistry","comparison_group":null,"date":"2025-06-20","date_kind":"publication","date_note":"Science Advances publication date; computation date is not reported.","sizes":[{"value":77,"unit":"qubits","label":"Physical circuit qubits","note":"72 Jordan–Wigner data qubits plus auxiliary qubits; not 77 orbitals."},{"value":54,"unit":"electrons","label":"Fe4S4 active electrons"},{"value":36,"unit":"spatial-orbitals","label":"Fe4S4 active spatial orbitals"}],"resources":{"hardware":"IBM Heron ibm_torino and Fugaku","cpu_cores":null,"gpus":null,"qpus":1,"nodes":6400,"wall_seconds":2700,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Wall time is approximately 45 minutes of quantum sampling only. Largest classical processing uses 100 batches on 64 nodes each; one diagonalization takes about 1.5 hours."},"accuracy":"Variational upper bounds and energy-variance comparisons retain useful signal; classical methods still give lower Fe4S4 energies.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["sqd"],"sources":[{"title":"Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer","url":"https://doi.org/10.1126/sciadv.adu9991","year":2025,"status":"Peer-reviewed; Science Advances"},{"title":"Author manuscript and supplementary hardware details","url":"https://arxiv.org/abs/2405.05068v3","year":2025,"status":"Author manuscript; v3"}],"relations":[],"limitation":"Exceeding exact diagonalization does not establish advantage over approximate classical solvers. Sampling and classical costs are separately scoped."},{"id":"dwave-spin-glass-2025","title":"D-Wave: spin-glass annealing dynamics","summary":"A superconducting annealer sampled rapid spin-glass quenches on several lattice geometries. The largest diamond input used 567 operable qubits.","approach":"quantum","workload":"quantum-dynamics","comparison_group":"dwave-spin-glass-dynamics-2025","date":"2025-04-11","date_kind":"publication","date_note":"Science issue date; the work was publicly available as a March 2024 preprint. Individual acquisition dates are unknown.","sizes":[{"value":567,"unit":"qubits","label":"Operable qubits in largest diamond input","note":"567 active sites of a nominal 576-qubit diamond input; not the 5,000-qubit experiment cited as earlier work."}],"resources":{"hardware":"D-Wave Advantage2 prototype","cpu_cores":null,"gpus":null,"qpus":1,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"One QPU for the largest diamond data. Reported throughput is at least 1,000 samples/s, but nanosecond anneal duration is not end-to-end runtime."},"accuracy":"Small-system observables and sampling were compared with converged simulations. Large-system scaling tests do not provide an exact 567-qubit full-state reference.","evidence":"mixed","claim_role":"advantage-claim","chemistry_relevance":"model","method_ids":["analog"],"sources":[{"title":"Beyond-classical computation in quantum simulation","url":"https://doi.org/10.1126/science.ado6285","year":2025,"status":"Published paper"}],"relations":[],"limitation":"A disordered-spin dynamics model, not chemistry. Projected classical costs concern tested simulation families and extrapolated MPS resources, not all possible classical algorithms."},{"id":"dwave-variational-monte-carlo-2025","title":"D-Wave response: variational dynamics of 128 spins","summary":"Time-dependent variational Monte Carlo simulated diamond-lattice quenches with a Jastrow–Feenberg wavefunction on systems up to 128 spins.","approach":"classical","workload":"quantum-dynamics","comparison_group":"dwave-spin-glass-dynamics-2025","date":"2025-03-11","date_kind":"preprint","date_note":"First arXiv submission; no journal version identified in this source audit. Individual run dates are unknown.","sizes":[{"value":128,"unit":"spins","label":"Simulated spins"}],"resources":{"hardware":"GPU implementation; accelerator model not specified","cpu_cores":null,"gpus":4,"qpus":null,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"The study reports up to four GPUs and a few days for the largest measured systems. No exact wall time or extrapolated larger-system run is entered."},"accuracy":"Reported relative two-point correlation errors below 7% on the studied short-quench cases, alongside residual-energy and variational-error diagnostics.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"model","method_ids":["qmc"],"sources":[{"title":"Challenging the Quantum Advantage Frontier with Large-Scale Classical Simulations of Annealing Dynamics","url":"https://arxiv.org/abs/2503.08247","year":2025,"status":"Preprint"}],"relations":[{"target":"dwave-spin-glass-2025","kind":"responds-to","note":"Tests a different classical approximation on the diamond-lattice annealing models."}],"limitation":"Validated calculations stop at 128 spins; larger-size cost forecasts are estimates. The result does not reproduce every geometry, longer quench, or full output distribution of the QPU study."},{"id":"mp2-amyloid-perlmutter-2024","title":"Fragmented MP2 forces for an amyloid assembly","summary":"A four-strand 2BEG amyloid model runs at 3.4 seconds per AIMD timestep.","approach":"classical","workload":"molecular-dynamics","comparison_group":null,"date":"2024-10-29","date_kind":"preprint","date_note":"Author-manuscript submission; computation date unknown.","sizes":[{"value":1496,"unit":"atoms","label":"Whole assembly atoms"},{"value":5504,"unit":"electrons","label":"Whole assembly electrons"}],"resources":{"hardware":"Perlmutter NVIDIA A100 nodes","cpu_cores":null,"gpus":4096,"qpus":null,"nodes":1024,"wall_seconds":3.4,"total_flops":null,"peak_flops_per_second":null,"precision":"FP64","note":"Per-step wall time; demonstrated trajectory 100 fs with 1 fs steps."},"accuracy":"Distance-truncated MBE3/MP2 forces.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["aimd","fmo"],"sources":[{"title":"MP2 AIMD: amyloid latency demonstration, Section VII-A","url":"https://doi.org/10.1109/SC41406.2024.00015","year":2024,"status":"Peer-reviewed; SC24"}],"relations":[],"limitation":"Short force/dynamics demonstration, not an aggregation trajectory."},{"id":"mp2-urea-frontier-2024","title":"Two-million-electron fragmented MP2 dynamics","summary":"Frontier evaluates AIMD timesteps for 63,854 urea molecules with an MBE3/MP2 potential.","approach":"classical","workload":"molecular-dynamics","comparison_group":null,"date":"2024-10-29","date_kind":"preprint","date_note":"arXiv submission date; also published at SC24. Computation date is not reported.","sizes":[{"value":2043328,"unit":"electrons","label":"Whole urea system electrons"}],"resources":{"hardware":"Frontier AMD MI250X nodes","cpu_cores":null,"gpus":null,"qpus":null,"nodes":9400,"wall_seconds":1536,"total_flops":1.55e+21,"peak_flops_per_second":null,"precision":"FP64","note":"One timestep: 25.6 minutes and explicitly reported 1.55 zettaFLOPs. GEMM counters give a lower bound; reported throughput is 1006.7 PFLOP/s."},"accuracy":"MBE3/RI-MP2 with cc-pVDZ basis and distance cutoffs; not full-system canonical MP2.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["aimd","fmo"],"sources":[{"title":"Breaking the Million-Electron and 1 EFLOP/s Barriers: Biomolecular-Scale Ab Initio Molecular Dynamics Using MP2 Potentials","url":"https://doi.org/10.1109/SC41406.2024.00015","year":2024,"status":"Peer-reviewed; SC24"},{"title":"MP2 AIMD author manuscript, Section VII-C","url":"https://arxiv.org/abs/2410.21888","year":2024,"status":"Author manuscript"}],"relations":[],"limitation":"Several demonstration timesteps; not a long equilibrated trajectory."},{"id":"google-sycamore-67qubit-2024","title":"Sycamore: larger circuits and noise transitions","summary":"A later Sycamore experiment sampled 67-qubit circuits with 32 cycles and studied the noise regime supporting global quantum correlations.","approach":"quantum","workload":"random-circuit-sampling","comparison_group":"google-67qubit-rcs-2024","date":"2024-10-09","date_kind":"publication","date_note":"Nature online publication; an initial preprint appeared in April 2023. Individual run dates are unknown.","sizes":[{"value":67,"unit":"qubits","label":"Active circuit qubits"}],"resources":{"hardware":"Google Sycamore superconducting processor","cpu_cores":null,"gpus":null,"qpus":1,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Over 70 million bitstrings reported for one 32-cycle circuit; no single acquisition wall time is entered."},"accuracy":"About 0.1% full-circuit fidelity is inferred from an error model, with patched-circuit and Loschmidt-echo checks. Full 67-qubit XEB is not computed exactly.","evidence":"mixed","claim_role":"advantage-claim","chemistry_relevance":"benchmark","method_ids":[],"sources":[{"title":"Phase transitions in random circuit sampling","url":"https://doi.org/10.1038/s41586-024-07998-6","year":2024,"status":"Published paper"}],"relations":[{"target":"google-sycamore-2019","kind":"extends","note":"Increases qubit count and depth, with additional noise-regime diagnostics."},{"target":"google-sycamore-pan-2022","kind":"compares-with","note":"Uses improved tensor-contraction baselines informed by prior classical sampling work."}],"limitation":"This is a larger workload than the 2019 circuit, not a repeat of it. Estimated classical costs depend strongly on memory and bandwidth assumptions; no chemistry result follows."},{"id":"google-sycamore-zhao-2024","title":"Sycamore response: 1,432 GPUs and XEB postselection","summary":"A100 GPUs generated three million uncorrelated samples with XEB about 0.002 in 86.4 seconds. Postselection increases the score of lower-fidelity simulated amplitudes.","approach":"classical","workload":"random-circuit-sampling","comparison_group":"google-sycamore-rcs-2019","date":"2024-09-12","date_kind":"publication","date_note":"Journal advance publication; assigned to the 2025 volume. Authors say the work was completed in August 2023; exact run dates are unspecified.","sizes":[{"value":53,"unit":"qubits","label":"Simulated circuit qubits"}],"resources":{"hardware":"1,432 NVIDIA A100 GPUs, 80 GB per GPU","cpu_cores":null,"gpus":1432,"qpus":null,"nodes":null,"wall_seconds":86.4,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Measured 86.4 seconds for three million samples, compared with 600 seconds for the corresponding Sycamore sample count. No extrapolation to one million is entered."},"accuracy":"Postselected XEB about 0.002. The author preprint reports approximate-state fidelity 0.01823% (0.0001823); XEB enhancement is not an equivalent increase in state fidelity.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"benchmark","method_ids":["tensor_networks"],"sources":[{"title":"Leapfrogging Sycamore: harnessing 1432 GPUs for 7× faster quantum random circuit sampling","url":"https://doi.org/10.1093/nsr/nwae317","year":2025,"status":"Published paper; online 2024"},{"title":"Leapfrogging Sycamore: author preprint and numerical-fidelity details","url":"https://arxiv.org/abs/2406.18889","year":2024,"status":"Author preprint; published version linked above"}],"relations":[{"target":"google-sycamore-2019","kind":"responds-to","note":"Measures faster generation at a comparable XEB score for the 53-qubit, 20-cycle benchmark."},{"target":"google-sycamore-pan-2022","kind":"extends","note":"Combines approximate contraction with XEB-boosting postselection and multi-GPU optimization."}],"limitation":"Speed comparison is scoped to sample count and XEB. Postselected output need not match the noisy quantum distribution; it is not a general-purpose quantum simulation advantage."},{"id":"ibm-eagle-tindall-2024","title":"Eagle response: tensor networks on a laptop","summary":"A tensor network adapted to the heavy-hexagon lattice reproduced selected Eagle observables with much smaller classical resources.","approach":"classical","workload":"quantum-dynamics","comparison_group":"ibm-eagle-kicked-ising-2023","date":"2024-01-23","date_kind":"publication","date_note":"PRX Quantum publication; first preprint appeared 26 June 2023. Individual run dates are unknown.","sizes":[{"value":127,"unit":"qubits","label":"Simulated circuit qubits"}],"resources":{"hardware":"Laptop computer; model not specified","cpu_cores":null,"gpus":null,"qpus":null,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"For five-step circuits the paper reports under 10 seconds for magnetization and under 4 minutes for higher-weight observables. Bounds are not stored as exact runtimes."},"accuracy":"Five-step magnetization reached approximately 10^-14 absolute error against an exact benchmark. Deeper circuits rely on convergence tests rather than exact full-state certification.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"model","method_ids":["tensor_networks"],"sources":[{"title":"Efficient Tensor Network Simulation of IBM’s Eagle Kicked Ising Experiment","url":"https://doi.org/10.1103/PRXQuantum.5.010308","year":2024,"status":"Published paper"}],"relations":[{"target":"ibm-eagle-2023","kind":"responds-to","note":"Recomputes observables and circuit instances from the Eagle experiment."}],"limitation":"The laptop timing and near-machine precision concern selected shallow-circuit observables, not every deep circuit or a complete state-vector calculation."},{"id":"ibm-eagle-begusic-2024","title":"Eagle response: sparse Pauli dynamics","summary":"Sparse Pauli dynamics reproduced the measured Eagle observables in about ten seconds per point on one CPU core.","approach":"classical","workload":"quantum-dynamics","comparison_group":"ibm-eagle-kicked-ising-2023","date":"2024-01-17","date_kind":"publication","date_note":"Publication date printed in the Science Advances paper; the issue is dated 19 January. Individual run dates are unknown.","sizes":[{"value":127,"unit":"qubits","label":"Simulated circuit qubits"}],"resources":{"hardware":"Single core of a laptop CPU; model not specified","cpu_cores":1,"gpus":null,"qpus":null,"nodes":null,"wall_seconds":10,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Approximately 10 seconds is the mean per observable/angle point for the explicitly time-limited SPD data in Fig. 3, not the entire parameter sweep."},"accuracy":"The time-limited SPD results agree with experimental error-mitigated observables. Separate, slower SPD and tensor-network calculations supply the tighter convergence checks.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"model","method_ids":[],"sources":[{"title":"Fast and converged classical simulations of evidence for the utility of quantum computing before fault tolerance","url":"https://doi.org/10.1126/sciadv.adk4321","year":2024,"status":"Published paper"}],"relations":[{"target":"ibm-eagle-2023","kind":"responds-to","note":"Recomputes the reported kicked-Ising observables, including the 20-step central-spin measurement."},{"target":"ibm-eagle-tindall-2024","kind":"compares-with","note":"Discusses related belief-propagation tensor-network simulations of the same experiment."}],"limitation":"The 10-second mean must not be paired with the paper’s most expensive, highest-accuracy tensor-network results. This concerns spin observables, not chemistry energies."},{"id":"dmrg-pcluster-48-a100-2024","title":"Nitrogenase P-cluster on 48 GPUs","summary":"Distributed DMRG reached bond dimension 14,000 for the P-cluster's 114-electron, 73-orbital active space.","approach":"classical","workload":"chemistry","comparison_group":null,"date":"2024-01-10","date_kind":"publication","date_note":"Online publication date; computation date is not reported.","sizes":[{"value":114,"unit":"electrons","label":"Active electrons"},{"value":73,"unit":"spatial-orbitals","label":"Active spatial orbitals"}],"resources":{"hardware":"NVIDIA A100 80 GB SXM cluster","cpu_cores":null,"gpus":48,"qpus":null,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"The paper's abstract explicitly identifies the 48-GPU calculation. No complete-run runtime is extracted here."},"accuracy":"Bond dimension 14,000 characterizes wavefunction capacity; it is not an absolute energy-error certificate.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"direct","method_ids":["dmrg"],"sources":[{"title":"Distributed Multi-GPU Ab Initio Density Matrix Renormalization Group Algorithm with Applications to the P-Cluster of Nitrogenase","url":"https://doi.org/10.1021/acs.jctc.3c01228","year":2024,"status":"Peer-reviewed; JCTC"}],"relations":[],"limitation":"Electronic active-space calculation of a nitrogenase cluster, not dynamics of the complete enzyme."},{"id":"ibm-eagle-2023","title":"Eagle: 127-qubit Ising observables","summary":"Error mitigation recovered observables from kicked-Ising circuits beyond exact state-vector reach. The paper compared results with the classical approximations available in its study.","approach":"quantum","workload":"quantum-dynamics","comparison_group":"ibm-eagle-kicked-ising-2023","date":"2023-06-14","date_kind":"publication","date_note":"Nature online publication; individual experiment dates are not established here.","sizes":[{"value":127,"unit":"qubits","label":"Active superconducting qubits"}],"resources":{"hardware":"IBM Eagle, ibm_kyiv","cpu_cores":null,"gpus":null,"qpus":1,"nodes":null,"wall_seconds":null,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"One processor; aggregate wall time is omitted because observable circuits and error-mitigation workloads differ."},"accuracy":"Observable accuracy was checked against exact values where feasible and approximate simulations elsewhere; there is no certified fidelity for the entire 127-qubit state.","evidence":"measured","claim_role":"advantage-claim","chemistry_relevance":"model","method_ids":["trotter_digital"],"sources":[{"title":"Evidence for the utility of quantum computing before fault tolerance","url":"https://doi.org/10.1038/s41586-023-06096-3","year":2023,"status":"Published paper"}],"relations":[],"limitation":"A spin-model dynamics experiment, not molecular chemistry. Later classical methods reproduced the studied observables; qubit count alone does not establish advantage."},{"id":"google-sycamore-pan-2022","title":"Sycamore response: one million classical samples","summary":"Tensor-network contraction produced approximately one million independent samples of the 53-qubit, 20-cycle circuit in about 15 hours on 512 V100 GPUs.","approach":"classical","workload":"random-circuit-sampling","comparison_group":"google-sycamore-rcs-2019","date":"2022-08-22","date_kind":"publication","date_note":"Physical Review Letters publication; first preprint appeared 4 November 2021. Individual run dates are unknown.","sizes":[{"value":53,"unit":"qubits","label":"Simulated circuit qubits"}],"resources":{"hardware":"512 NVIDIA V100 GPUs","cpu_cores":null,"gpus":512,"qpus":null,"nodes":null,"wall_seconds":54000,"total_flops":null,"peak_flops_per_second":null,"precision":"complex64 (FP32 real and imaginary components)","note":"Approximately 15 hours (54,000 seconds) for the complete reported calculation producing 2^20 independent samples."},"accuracy":"Approximate-state fidelity about 0.0037; samples are drawn from its squared amplitudes. The paper validates the approximation rather than computing the entire exact state.","evidence":"measured","claim_role":"classical-response","chemistry_relevance":"benchmark","method_ids":["tensor_networks"],"sources":[{"title":"Solving the Sampling Problem of the Sycamore Quantum Circuits","url":"https://doi.org/10.1103/PhysRevLett.129.090502","year":2022,"status":"Published paper"}],"relations":[{"target":"google-sycamore-2019","kind":"responds-to","note":"Actually generates uncorrelated low-fidelity samples for the 2019 circuit."},{"target":"google-sycamore-ibm-estimate-2019","kind":"compares-with","note":"Contrasts completed approximate sampling with the earlier full-state storage proposal."}],"limitation":"This measured run is slower than Sycamore’s 200-second acquisition. The paper’s possible exascale speedup is an additional estimate, not this measurement."},{"id":"deepmd-copper-127m-2020","title":"127-million-atom learned-potential dynamics","summary":"Optimized DeePMD-kit runs a copper scaling benchmark across Summit with mixed precision.","approach":"classical","workload":"molecular-dynamics","comparison_group":null,"date":"2020-09-14","date_kind":"preprint","date_note":"Verified arXiv v3 revision date; initial preprint 2020-05-01, subsequently SC20. Computation date is not reported.","sizes":[{"value":127401984,"unit":"atoms","label":"Whole copper benchmark"}],"resources":{"hardware":"Summit NVIDIA V100 and IBM POWER9 nodes","cpu_cores":null,"gpus":27360,"qpus":null,"nodes":4560,"wall_seconds":0.034,"total_flops":null,"peak_flops_per_second":null,"precision":"MIX-16 mixed half precision","note":"Reported 34 ms per MD step for copper with MIX-16; weak-scaling tests use 500 steps. Setup and training are excluded from this latency."},"accuracy":"Learned forces approximate ab initio training data; mixed-precision validation is model-specific, not exact Schrödinger accuracy.","evidence":"measured","claim_role":"demonstration","chemistry_relevance":"benchmark","method_ids":["mlpot"],"sources":[{"title":"Pushing the Limit of Molecular Dynamics with Ab Initio Accuracy to 100 Million Atoms with Machine Learning","url":"https://doi.org/10.1109/SC41405.2020.00009","year":2020,"status":"Peer-reviewed; SC20"},{"title":"DeePMD-kit scaling author manuscript","url":"https://arxiv.org/abs/2005.00223v3","year":2020,"status":"Author manuscript; v3"}],"relations":[],"limitation":"Homogeneous copper performance benchmark; no on-the-fly electronic solve, arbitrary chemistry guarantee, or quantum-computer advantage comparison."},{"id":"google-sycamore-2019","title":"Sycamore: 53-qubit random-circuit sampling","summary":"Sycamore generated one million samples from a 53-qubit, 20-cycle random circuit in about 200 seconds. Its claimed separation used a contemporary classical runtime estimate.","approach":"quantum","workload":"random-circuit-sampling","comparison_group":"google-sycamore-rcs-2019","date":"2019-10-23","date_kind":"publication","date_note":"Nature publication; individual acquisition dates are not established here.","sizes":[{"value":53,"unit":"qubits","label":"Active circuit qubits"}],"resources":{"hardware":"Google Sycamore superconducting processor","cpu_cores":null,"gpus":null,"qpus":1,"nodes":null,"wall_seconds":200,"total_flops":null,"peak_flops_per_second":null,"precision":null,"note":"Approximately 200 seconds for one million circuit samples; excludes constructing and calibrating the processor."},"accuracy":"Linear cross-entropy benchmark (XEB) score about 0.002. This is a low-fidelity sampling benchmark, not a 99.8%-accurate quantum calculation.","evidence":"mixed","claim_role":"advantage-claim","chemistry_relevance":"benchmark","method_ids":[],"sources":[{"title":"Quantum supremacy using a programmable superconducting processor","url":"https://doi.org/10.1038/s41586-019-1666-5","year":2019,"status":"Published paper"}],"relations":[],"limitation":"Random-circuit sampling is not a molecular simulation. Later algorithms changed the classical comparison; the original runtime estimate is not a universal lower bound."},{"id":"google-sycamore-ibm-estimate-2019","title":"Sycamore response: a disk-backed simulation estimate","summary":"IBM researchers proposed storing the complete 53-qubit state on Summit’s secondary storage, estimating 2.55 days for the 20-cycle circuit.","approach":"classical","workload":"random-circuit-sampling","comparison_group":"google-sycamore-rcs-2019","date":"2019-10-21","date_kind":"preprint","date_note":"First arXiv submission; the proposed full simulation was not executed in this paper.","sizes":[{"value":53,"unit":"qubits","label":"Circuit qubits in resource estimate"}],"resources":{"hardware":"Proposed Summit configuration: IBM POWER9 nodes and secondary storage","cpu_cores":null,"gpus":null,"qpus":null,"nodes":4096,"wall_seconds":220320,"total_flops":null,"peak_flops_per_second":null,"precision":"FP64 in memory; complex FP32 on disk","note":"Estimated 2.55 days (220,320 seconds), including modeled communication and disk I/O. The 4,096-node scheme needs 64 PiB of amplitude storage. This is not a measured run."},"accuracy":"Full-state simulation proposal, subject to floating-point error; different output scope from a million low-fidelity samples.","evidence":"estimated","claim_role":"resource-estimate","chemistry_relevance":"benchmark","method_ids":[],"sources":[{"title":"Leveraging Secondary Storage to Simulate Deep 54-qubit Sycamore Circuits","url":"https://arxiv.org/abs/1910.09534","year":2019,"status":"Preprint"}],"relations":[{"target":"google-sycamore-2019","kind":"responds-to","note":"Re-estimates classical cost for the same 53-qubit, 20-cycle circuit using secondary storage."}],"limitation":"The title also covers 54 qubits; this record selects the 53-qubit estimate. Neither projected timing nor perfect-state intent is evidence of a completed sampling experiment."}]}