CMOS-Quantum Circuit Optimizer
As companies push for CMOS-compatible quantum technologies, the need for efficient hardware-mapping software grows. This challenge requires you to build an automated optimization pipeline that takes high-level quantum circuits and maps them to simulated CMOS-compatible qubit layouts. You will use DeepSeek-V3, via the Marvin framework, to interpret complex gate requirements and suggest optimal routing to minimize decoherence. The entire simulation and optimization sequence will be orchestrated using Prefect, allowing for parallel execution of circuit permutations and robust observability of the optimization process.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a quantum-classical hybrid optimization pipeline using DeepSeek-V3 for circuit mapping and Prefect for parallel simulation management.
How work is evaluated
The evaluation focuses on the optimizer's ability to reduce gate depth while maintaining the logical integrity of the quantum circuit.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Master the integration of Marvin decorators to turn DeepSeek-V3 into a structured circuit transpiler
Deploy Prefect Work Pools to distribute quantum simulation tasks across multiple compute nodes
Build a cost-function evaluator that checks the gate-depth and error-rate of mapped circuits
Implement a Prefect flow that dynamically adjusts simulation parameters based on LLM feedback
Orchestrate the benchmarking of CMOS-compatible gate sets against standard superconducting qubit models
Utilize DeepSeek-V3's reasoning capabilities to suggest architectural improvements for CMOS quantum chips
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
The evaluation focuses on the optimizer's ability to reduce gate depth while maintaining the logical integrity of the quantum circuit.
Challenge input
OpenQASM string
Agent execution
No agent tool is listed for this challenge.
Evaluated output
Optimized OpenQASM string + Gate Depth Integer
- The output QASM must be valid and parseable by standard simulators.
- Depth Reduction Ratio target: 1.2
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
No tool records are attached.
Evaluation contract
- The output QASM must be valid and parseable by standard simulators.
Metrics
- Depth Reduction Ratio ยท Target 1.2
Workflow tasks
- Task 1
Input: OpenQASM string
Output: Optimized OpenQASM string + Gate Depth Integer
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.