INDIVIDUAL MCP TOOL
list_qldpc_codes
Return the catalog of supported qLDPC codes (id, label, family, n, k, d, circuitLevelDistance, ancilla counts, roundsPerLogicalOp, threshold, prefactor [per block per syndrome cycle], logicalErrorExponent [= d_circ/2], source URLs).
Input schema
{}Risk classification
Inferred read-only · medium confidence · heuristic, not a guarantee.
- No write-capable action terms were found; this is not proof that invocation has no side effects.
Parent endpoint
CONNECT WITH APPROVAL
Client installation
Review this server and its permissions before adding it. Secret placeholders must be set locally.
Codex
~/.codex/config.toml
[mcp_servers.quantum-expectations]
url = "https://www.quantum-expectations.com/api/mcp"
enabled = true
Claude Code
.mcp.json
{
"mcpServers": {
"quantum-expectations": {
"type": "http",
"url": "https://www.quantum-expectations.com/api/mcp"
}
}
}
Claude Desktop
Settings → Connectors → Add custom connector
Name: quantum-expectations
Remote MCP URL: https://www.quantum-expectations.com/api/mcp
Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.
Cursor
.cursor/mcp.json
{
"mcpServers": {
"quantum-expectations": {
"url": "https://www.quantum-expectations.com/api/mcp"
}
}
}
Visual Studio Code
.vscode/mcp.json
Add to Visual Studio Code{
"servers": {
"quantum-expectations": {
"type": "http",
"url": "https://www.quantum-expectations.com/api/mcp"
}
}
}
Generic MCP
Client-specific MCP configuration
{
"name": "quantum-expectations",
"transport": "streamable-http",
"url": "https://www.quantum-expectations.com/api/mcp"
}
MCP Inspector
Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.
Related tools
compute_expectation— Given a quantum circuit (2-qubit error rate p, qubit count n, depth d), compute the effective error rate, success probability, and optional surface-code or qLDPC overhead.compute_required_error_rate— Inverse of compute_expectation.compute_fault_tolerant_resources— Given an algorithm stated as (numLogicalQubits, tCount) and a physical error rate (or hardwareId), derive the full surface-code + magic-state-distillation footprint from the general laws of the Litinski lattice-surgery cost model (no per-scenario constants): distillation factory choice, tile layout, required code distance, total physical qubits, and wall-clock time.compare_hardware_scenarios— Run the same circuit against multiple current SOTA hardware entries in one call so an agent can rank platforms without N sequential compute_expectation calls.list_current_quantum_computers— Return the representative-entry table of current SOTA quantum computers (id, hardware type, physical qubit count, 2-qubit error rate).list_hardware_timings— Return per-platform gate-cycle timings (2Q gate time, readout time, in SI seconds) plus the representative device and native 2Q gate name, with source URLs.compute_quantum_volume_rate— Compute the Quantum Volume Rate (QV/second): QVR = V_Q / (log2(V_Q) * t_2Q + t_meas).fit_historic_series— Fit a log-linear trend (ln(value) = slope * year + intercept) to one historic series — fidelity or qubit-count — for one hardware type.