Return code: 0
Duration: 92.0s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: print("Task planning complete. Starting implementa...
  python_exec: defining function solve_quantum_gate_scheduling()
  python_exec: defining function solve_quantum_gate_scheduling_optimal()
  python_exec: solution = {
  python_exec: solution = {
  save_code: {'code': 'import clingo\nimpor...
  report_issue: {'text': "All is fine - no iss...


                                Task Complete! ✓                                

I've successfully solved the quantum gate scheduling problem using Answer Set   
Programming. Here's what was accomplished:                                      

                               Solution Summary:                                

 • Circuit Depth: 3 (matches expected optimal)                                  
 • Schedule:                                                                    
    • Time 1: cnot_q0_q3, h_q1, x_q2 (parallel execution on disjoint qubits)    
    • Time 2: cnot_q1_q2, h_q0 (parallel execution on disjoint qubits)          
    • Time 3: cnot_q0_q1 (final gate)                                           

                          Key Implementation Details:                           

 1 Facts: Defined all qubits, gates, and their qubit usage relationships        
 2 Choice Rule: Each gate scheduled at exactly one time step: 1 { scheduled(G,  
   T) : time(T) } 1                                                             
 3 Conflict Prevention: Gates sharing qubits cannot execute simultaneously      
 4 Optimization: Minimized maximum time step using #minimize { T@1,G :          
   scheduled(G, T) }                                                            

                                 Verification:                                  

✓ All 6 gates scheduled                                                         
✓ No qubit conflicts at any time step                                           
✓ Optimal depth of 3 achieved                                                   
✓ Output format matches specification                                           

The solution is saved and ready to use!                                         

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            5 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        167,881
  Output tokens:       5,316
  Total tokens:        173,197

⏱️  Execution time: 1m 31.0s
==================================================


=== STDERR ===
#models not 0: optimality of last model not guaranteed.
