Estimate the physical quantum resources needed to run a quantum algorithm.

Provide the algorithm as EXACTLY ONE of:

• algorithm_template: ID of a predefined algorithm (e.g. 'shor_2048', 'grover_aes128', 'chemistry_femo', 'qpe_generic'). Use list_algorithm_templates() for the full list.

• logical_counts: JSON string with algorithm counts, e.g.: '{"numQubits": 100, "tCount": 200, "rotationCount": 50}'

• qsharp_code: Q# source code string with a parameterless entry point operation.

Hardware parameters:

• qubit_model: Physical qubit technology. Default 'qubit_gate_ns_e3' (superconducting). Use list_qubit_models() for all options.

• qec_scheme: Error correction scheme. 'surface_code' (default) or 'floquet_code' (Majorana qubits only).

• error_budget: Acceptable failure probability (0-1). Default 0.001.

• qubit_model_overrides: JSON string to override specific qubit parameters while keeping a named model as the base. E.g. '{"twoQubitGateTime": "10 ns"}'. Valid keys: oneQubitGateTime, twoQubitGateTime, oneQubitMeasurementTime, oneQubitGateErrorRate, twoQubitGateErrorRate, tGateErrorRate, readoutErrorRate, idleErrorRate.

Optional QEC scheme overrides (override individual parameters of the named qec_scheme):

• qec_crossing_prefactor: float > 0. Error-suppression prefactor (default ~0.03).

• qec_error_correction_threshold: float in (0,1). Error correction threshold (default ~0.01).

• qec_logical_cycle_time: Formula string for logical cycle duration, e.g. '1000 ns' for a fixed 1 µs cycle (replicates Gidney-Ekerå assumption).

• qec_physical_qubits_per_logical: Formula string for qubits per logical qubit.

Optional constraints (use at most one of max_duration or max_physical_qubits):

• max_duration: e.g. '1 hour', '500 ms', '1 s'

• max_physical_qubits: integer upper bound on qubit count

• max_t_factories: limit T-factory copies (reduces qubits, increases runtime)

• logical_depth_factor: multiplier on circuit depth (default 1.0)

Returns: summary (physical_qubits, runtime, logical_qubits, code_distance, t_factory_copies) plus full details breakdown.

Compare resource estimates across multiple hardware configurations.

Provide the algorithm as exactly one of algorithm_template, logical_counts, or qsharp_code.

Hardware selection (choose one approach):

• compare_all_models=True: compare all compatible qubit models

• qubit_models=['qubit_gate_ns_e3', 'qubit_gate_us_e3']: compare specific models

• configurations: JSON string of full configs. Each config dict may include the standard qubit_model/qec_scheme/error_budget keys plus the new override keys: qubit_model_overrides (dict), qec_crossing_prefactor, qec_error_correction_threshold, qec_logical_cycle_time, qec_physical_qubits_per_logical. E.g.: '[{"qubit_model": "qubit_gate_ns_e3", "qec_logical_cycle_time": "1000 ns"}]'

• Default (none specified): compare all 4 gate-based models

Returns a side-by-side comparison table showing physical qubits, runtime, code distance, and T-factory copies for each configuration.

Generate the Pareto frontier: qubit-count vs. runtime tradeoff for an algorithm.

Provide the algorithm as exactly one of algorithm_template, logical_counts, or qsharp_code.

Returns a list of Pareto-optimal points. Each point represents a configuration where you cannot reduce qubit count without increasing runtime, or vice versa.

• First point: minimum qubit count (longest runtime)

• Last point: minimum runtime (most qubits)

Optional qubit/QEC overrides (same as estimate_resources):

• qubit_model_overrides: JSON string to override specific qubit parameters.

• qec_crossing_prefactor, qec_error_correction_threshold: float overrides.

• qec_logical_cycle_time, qec_physical_qubits_per_logical: formula string overrides.

Useful for understanding hardware requirements at different time budgets.

List all 6 predefined physical qubit models with gate times, error rates, and descriptions.

Returns information about:

• qubit_gate_ns_e3/e4: Superconducting or spin qubits (nanosecond gates)

• qubit_gate_us_e3/e4: Trapped-ion qubits (microsecond gates)

• qubit_maj_ns_e4/e6: Majorana/topological qubits

Use this to understand which qubit_model to select for estimate_resources().

List available Quantum Error Correction (QEC) schemes with compatibility notes.

Returns details on:

• surface_code: Works with all qubit models. The standard choice.

• floquet_code: Majorana qubits only. Better overhead for topological hardware.

Use this to understand which qec_scheme to select for estimate_resources().

List predefined quantum algorithm templates with logical resource counts.

Templates are sourced from published research papers and cover:

• cryptography: shor_2048, grover_aes128

• chemistry: chemistry_femo

• general: qpe_generic

Each template can be passed directly to estimate_resources(algorithm_template=). Returns logical resource counts (numQubits, cczCount, etc.) and source citations.

Explain resource estimation parameters and recommend configurations for a use case.

If use_case is provided, gives targeted guidance. Valid values:

• 'cryptography': guidance for quantum attacks on RSA, ECC, AES

• 'chemistry': guidance for molecular simulation and drug discovery

• 'optimization': guidance for combinatorial optimization

• 'general': full parameter reference guide

Returns parameter descriptions, recommended starting configurations, and relevant templates.

Estimate resources using fully custom physical qubit parameters.

Use this when modeling novel hardware not covered by the 6 predefined qubit models. All gate times accept strings like '50 ns', '1 μs', '100 ms'. instruction_set: 'GateBased' (default) or 'Majorana'.

Provide algorithm as exactly one of algorithm_template, logical_counts, or qsharp_code.