RAPID QUANTUM PROTOTYPING (RQP)

Quantum Monte Carlo RQP Lab

Derivatives pricing and expectation-estimation benchmark using configurable payoff scenarios, classical Monte Carlo baselines, quantum readout methods, amplitude-estimation variants, convergence charts, logs, and resource diagnostics.

Input Workflow

Access, workbook, scenario, methods, budgets, async run controls, and artifacts.

1. Access

License key

Access statusUnchecked

Key flowExisting RQP key/access pattern

Separate QMC keyNo

QMC enablednot checked

QMC remainingnot checked

Inspect endpointConfigured

2. Workbook Input

WorkbookNo workbook selected

Sizen/a

File loadingWaiting for file

InspectNot inspected

ValidationNot validated

Scenario listNot refreshed

Workbook inspect, validation, and scenario listing run automatically when a file is selected.

3. Scenario And Run Mode

ScenarioRun modeResponse level

4. Method Selection

Classical MCClassical Monte Carlo baseline using the same payoff evaluator.Objective shotsDirect objective-qubit sampling at Grover power k=0.Low-depth AEMLAE-style estimator using the safe Grover schedule.IQAE-style adaptive AESimplified IQAE-style adaptive estimator, not full rigorous IQAE.

5. Classical Settings

Sample scheduleDemo seeds

6. Quantum Settings

Shot schedule

Safe Grover schedule0,1,2,4

Max Grover power4

IQAE max iterationsIQAE shots per iteration

7. Run Controls

The selected run mode above controls whether this submits a quick single scenario run or a full convergence benchmark.

API URLConfigured

Job IDNo job submitted

Statusidle

Phaseidle

Progressn/a

ResultNot available

8. Review / Artifacts

Results View

Filter result panes while keeping the QMC input workflow visible.

Client Log

03:47:58 PMclientQMC page opened. Inspect/validate/list and async worker job flow use the configured backend URL.

Backend Log

03:47:58 PMidleNo backend job has been started from this QMC page.

File Overview

Workbook

Not loaded

Inspect

Not inspected

Validation

Not validated

Job status

idle

Job phase

idle

Result

Not available

Scenario Overview

Scenario	Type	Payoff	Underlyings	Qubits	Suitability	Validation
EU_CALL_OTM_001	single_asset_expectation	european_call_out_of_money	1	6 uncertainty + 1 objective	ae_suitable	sample_default
DIGITAL_RARE_001	event_probability	rare_cash_or_nothing	1	6 uncertainty + 1 objective	ae_suitable	sample_default
TERMINAL_BARRIER_RARE_001	terminal_event_probability	rare_barrier_digital	1	6 uncertainty + 1 objective	ae_suitable	sample_default
DIGITAL_CASH_001	event_probability	cash_or_nothing	1	6 uncertainty + 1 objective	high_amplitude_control	sample_default
CAPPED_CALL_001	bounded_expectation	capped_call	1	6 uncertainty + 1 objective	high_amplitude_control	sample_default
TERMINAL_BARRIER_DIGITAL_001	terminal_event_probability	barrier_digital	1	6 uncertainty + 1 objective	high_amplitude_control	sample_default
EU_CALL_001	single_asset_expectation	european_call	1	6 uncertainty + 1 objective	reference	sample_default
BASKET_CALL_2A_001	multi_asset_expectation	basket_call	2	8 uncertainty + 1 objective	reference	sample_default

Selected Scenario

Scenario

EU_CALL_OTM_001

Suitability

ae_suitable

Safe schedule

0,1,2,4

Effective methods

classical_mc, objective_shots, low_depth_ae, iqae

Low-amplitude option scenario for Grover-amplified estimator diagnostics.

Result Summary

Reference option value (disc.)

available after run

Best option estimate (disc.)

available after run

Best method

available after run

Raw expected payoff

available after single run

Scaled amplitude

available after run

Discount factor

n/a

CI width

available after run

Total shots

500,1000,2000,5000

Effective oracle calls

available after run

Max Grover power

Runtime

available after run

Scenario Conclusion

Scenario-specific method conclusions appear after a completed convergence benchmark.

QMC Estimation Pipeline

Load distribution

Probability mass over terminal states.

Add payoff

Payoff or event indicator applied to each state.

Derive expectation value

MC, objective shots, and AE estimate the expected value.

Reported option values are financial post-processing of the estimated bounded expectation: unscale where needed and apply the discount factor. Discounting is not shown as a separate quantum/circuit step.

1. Load Distribution / 2. Add Payoff

Distribution/payoff plots will appear when the backend result includes scenario_diagnostics.distribution with probability, payoff, and contribution fields.

Convergence Charts

Charts will be available after a completed convergence benchmark.

Convergence Comparison

No rows available yet.

Simulator runtime, classical samples, quantum shots, and effective oracle calls are separate resource views. This QMC page does not claim current quantum runtime advantage.

Resource Metrics

Classical samples

500,1000,2000,5000

Quantum shots

500,1000,2000,5000

Seeds

IQAE iterations

Job ID

not submitted

Progress

n/a

Response level

full

Methods

classical_mc, objective_shots, low_depth_ae, iqae

No rows available yet.

Memory Diagnostics

Memory diagnostics will appear when the backend result includes worker memory telemetry or circuit-resource qubit counts.

1-3 Circuit Resource Estimate

Circuit resource tables will appear when the backend result includes circuit_resources for distribution loading, payoff/objective loading, Grover iteration, and AE schedules.

Diagnostics And Warnings

Wilson CI is surfaced by the backend for objective-shot zero/all-success cases.
Low-depth AE likelihood diagnostics appear in the completed result payload.
Simplified IQAE-style adaptive AE iteration diagnostics appear in the completed result payload.
Rare-event classical MC error can be non-monotonic at low sample counts.
State preparation and controlled rotations are simulator-oriented caveats.

Artifacts

Artifact	Status
JSON result	available after completed run
CSV convergence export	available after convergence result
PDF report	available after completed run
Qiskit notebook	available after completed run
PennyLane notebook	available after completed run
Workbook/result bundle	future

Adapter Envelope Preview

{
  "ok": true,
  "module": "qmc-rqp",
  "mode": "idle",
  "scenario_id": "EU_CALL_OTM_001",
  "result": {},
  "warnings": [
    "QMC frontend page. Calculations are submitted through the async worker job flow."
  ],
  "diagnostics": {
    "frontend_phase": "async_backend_testing",
    "route": "/qmc-rqp-internal",
    "adapter_contract": "qmc-rqp-platform-api"
  },
  "errors": [],
  "effective_settings": {
    "response_level": "full"
  }
}