Dense Evolution

Run a real Velocity-Verlet MD trajectory driven by Hellmann-Feynman forces at every step, for a catalog molecule. Args: params (MdTrajectoryInput): name (short id or full catalog name), n_steps, dt_fs, mapping, recompute_electronic_state (true = true ab-initio MD, capped at 30 steps; false = fixed electronic state, capped at 200 steps). Returns: str: JSON with the trajectory (positions/energies per step). "Error: ..." if n_steps is out of range for the chosen mode.

Run real coherence-L1-predictive density-matrix ZNE: the same Monte-Carlo density-matrix construction as dense_evolution_mitigate_density_matrix, but extrapolated via a signal that covers phase-type noise (phaseflip, dephasing) the classical-JSD signal behind that other tool is structurally blind to -- it only ever reads the density matrix's diagonal. Use this one for phase-type noise models, the JSD one for amplitude/bitflip-type noise. Args: params (MitigateDensityMatrixInput): qasm, noise_model, noise_p, seed. Returns: str: JSON with n_qubits, noise_factors, fidelity_raw, fidelity…

Run real density-matrix ZNE: Monte-Carlo density-matrix estimate at 1x/2x/3x noise_p, extrapolated and projected onto the nearest physical state, graded by Uhlmann fidelity against the true ideal state. Args: params (MitigateDensityMatrixInput): qasm, noise_model, noise_p, seed. Returns: str: JSON with n_qubits, noise_factors, fidelity_raw, fidelity_corrected.

Run real Zero-Noise Extrapolation: measure a Pauli expectation value at several noise scales under a real Kraus noise channel, then extrapolate back to zero noise. Args: params (MitigateZneInput): qasm, pauli_string, noise_model, noise_p, seed, extrapolation_method. Returns: str: JSON with n_qubits, ideal_expectation, noise_factors, noisy_expectations, zne_extrapolated, extrapolation_method.

Compute H_mix = weight_a*H_a + weight_b*H_b for two catalog molecules that share the same qubit count (same electron space), and diagonalize all three (H_a, H_b, H_mix) for their ground-state energies. Mixing molecules with different qubit counts is physically meaningless and is rejected with a clear error. Args: params (MixMoleculesInput): name_a, name_b (short id or full catalog name), weight_a, weight_b, mapping. Returns: str: JSON with n_qubits, energy_a, energy_b, energy_mixed (all in Hartree).

Compute the exact ground-state energy of a catalog molecule via real Hartree-Fock + Jordan-Wigner/Bravyi-Kitaev Hamiltonian construction and exact dense diagonalization. Args: params (MoleculeEnergyInput): name (short id or full catalog name), mapping. Returns: str: JSON with n_qubits, symbols, geometry, charge, ground_state_energy_hartree. "Error: ..." with a 404-style message if `name` is not in the catalog (call dense_evolution_list_molecules to see valid names/ids).

Run real Hartree-Fock SCF (dense_evolution.native_hf, the fallback path for elements outside PennyLane's bundled STO-3G table, e.g. Silicon) and run Dense-Armor's Hampel/Tukey anomaly filters over the real per-iteration energy trace, instead of trusting the final `converged` flag alone. Needs the `armor` extra (pip install dense-evolution[armor]) -- returns an actionable "Error: ..." if it isn't installed. Args: params (NativeHfDiagnosticsInput): symbols, geometry, charge, active_electrons, active_orbitals. len(symbols) must equal len(geometry). Returns: str: JSON with n_qubi…

A noise strength that oscillates instead of scaling smoothly with a ZNE-style scale factor -- for checking whether a mitigation technique that assumes smooth noise-vs-scale still works when that assumption breaks down. Args: params (OscillatingNoiseInput): base_p, freq, amp, factors. Returns: str: JSON with base_p, freq, amp, factors, p_eff.

Compute real Hellmann-Feynman nuclear forces (F = -d<psi|H(R)|psi>/dR via PennyLane autodiff, not finite differences) on a catalog molecule's real Hartree-Fock ground state. Args: params (QmmmForcesInput): name (short id or full catalog name), mapping. Returns: str: JSON with per-atom force vectors and related energetics.

Build an ephemeral index over the given documents and search it for `query` in one call -- ground an agent's own answer in a document collection, not a quantum measurement result. Nothing persists server-side: each call builds and searches its own index from scratch, so pass the same `documents` again for a second query rather than expecting a prior index to still exist. Two modes: semantic (default) pools TF-IDF and dense-bi-encoder candidates then re-scores with a cross-encoder if rerank=True -- catches paraphrases plain TF-IDF misses. exact=True instead does a substring/regex search over r…

Run real OpenQASM on dense_evolution's DenseSVSimulator (or the MPS backend for large circuits) and return measurement counts, probabilities, and statevector amplitudes. Above the dense backend's safe qubit ceiling (see dense_evolution_system_limits), automatically switches to an MPS top-k-states approximation instead of failing. Large statevectors/probability arrays are truncated to their most significant entries (see 'shown' vs 'total_*' fields) to keep the response usable in an agent's context -- the full histogram is always returned in 'counts' since it's naturally bounded by `shots`. Ar…

Run real VQE (Adam gradient descent with adjoint differentiation) against a molecule's Jordan-Wigner Hamiltonian, from a fresh random start every call -- not a cached/precomputed result. Can take a while for 'uccsd' ansatz or high maxiter; consider dense_evolution_health's RAM figures and start with a small maxiter/n_layers to gauge cost first. Args: params (RunVqeInput): either `name` (short id or full catalog name) or `symbols`+`geometry` (custom) must be given, plus ansatz_type, n_layers, maxiter, step_size, beta1, beta2, seed. Returns: str: JSON with the optimized…

Get the maximum qubit count this machine can currently simulate with a dense statevector, computed from actual free RAM right now (not a fixed constant) -- call before requesting a large `dense_evolution_run_circuit`. Returns: str: JSON describing the current safe qubit ceiling for the dense backend.

Run a real predictive-healing pass over a noisy (n_steps, dim) vector sequence -- e.g. VQE convergence telemetry or an MD trajectory. Per step, a Phi-Trigger (dense_evolution.healing) decides whether the change from a local baseline looks like genuine dynamics (kept as-is) or static noise (replaced by the local median). NaN/Inf entries are sanitized first regardless of that decision. Args: params (VectorHealingInput): vectors, radius_baseline. Returns: str: JSON with healed_vectors, fallback_triggered, adaptive_radius_used, reconstruction_error.

Sweep `t1_values` for the traversable-wormhole-inspired teleportation protocol, running both +mu_magnitude and -mu_magnitude at every point -- one call instead of 2*len(t1_values) separate dense_evolution_wormhole_teleportation calls. Returns each point's mutual information for both signs plus their difference (mu<0 minus mu>0 in this project's own convention), the standard readout for the protocol's qualitative signature: a smooth peak in that difference across the sweep (known peak for seed=61/n_majorana=8/k_terms=10/ t0=0.3: around t1≈0.6-0.7). Unlike dense_evolution_energy_scan, points ru…

Find a binary-sparse-SYK random-instance seed suitable for the traversable-wormhole-inspired teleportation protocol (`dense_evolution_wormhole_teleportation`). A uniformly-random seed does NOT reliably show the protocol's sign-dependent teleportation signal -- verified directly across many seeds (some give a clean peak, some the wrong sign for most of a sweep, some are flat noise). arXiv:2604.10090 didn't use an arbitrary instance either: they picked one "selected for favorable commutation properties" among their chosen terms. This tool reproduces that same selection criterion -- screening `n…

Run one point of the real traversable-wormhole-inspired quantum teleportation protocol (Gao-Jafferis-Wall theory, arXiv:2604.10090) on a binary sparse SYK model: two coupled chaotic Hamiltonians (L,R), a message injected into L via a reference-qubit pair (P,Q), a real bilinear L-R coupling exp(i*mu*V), and a readout that is NOT a single-qubit expectation value (which the no-signaling theorem forbids from ever showing this signal) but the mutual information between the reference qubit P and a qubit read out from R. Returns a single mutual-information value for the given mu -- the physically me…