Quantum Simulations

Active Simulations

Running

4 Queued

Success Rate

95.8%

+2.1%

Last 24 hours

Avg. Runtime

3.2m

Per Simulation

Optimized

Quantum Hardware Status

Backend	Qubits	Error Rate	Status
IBM Qiskit Aer	40	0.15%	Available
AWS Braket	35	0.18%	High Load
Local Simulator	30	0.01%	Available

Available Simulation Types

Post-Quantum Cryptography

Simulate quantum-powered DDoS and test PQC defenses

Quantum Neural Networks

Anomaly detection in encrypted traffic using QNNs

Hybrid Quantum-Classical

Test quantum preprocessing with classical ML models

Laboratory Environment

Infrastructure

IBM Quantum System One 127 Qubits
Classical Processing Unit 128-core AMD EPYC
Quantum Network Testbed 5-node QKD

Software Stack

Qiskit v0.45.0 Optimized
PennyLane v0.32.0 QML Ready
Post-Quantum Libraries NIST Round 4

Attack Workflows

Initial Access Phase

Quantum Side-Channel Analysis

Error rate measurement

Timing analysis

Execution Phase

Shor's Algorithm Implementation

Key factorization

Circuit optimization

Active Simulation

Running

Simulation Type:

Quantum Neural Network Training

Progress:

75%

Real-time simulation progress showing accuracy improvements over a 60-minute window. Starting at 65% baseline, optimization at 40-minute mark led to peak accuracy of 85%. Demonstrates stable performance in quantum attack simulations.

Simulation Parameters

Quantum Circuit Depth	42 layers	Entanglement Map	Full mesh
Error Correction	Surface code	Noise Model	Gaussian
Optimization Level	Level 3	Backend	IBM Q System

Resource Usage

Real-time monitoring of quantum computing resource allocation. Currently at 75% utilization with dynamic scaling enabled. Reserved 25% capacity ensures rapid response to security incidents while maintaining optimal performance levels.

Simulation Type Distribution

Distribution of current simulation workloads: Quantum (42%), Classical (35%), and Hybrid (23%). The increased focus on quantum simulations reflects enhanced capabilities in quantum threat detection and mitigation strategies, showing a 15% improvement in early threat detection.

Simulation Queue

Shor's Algorithm Test
Queued 5m ago

Pending
Grover's Search
Queued 12m ago

Pending

Completed Simulations

Simulation Name	Type	Duration	Completion Date	Status
Quantum Encryption Test ID: QSim-001	Encryption	45 minutes	3 hours ago	Completed
Shor's Algorithm Defense ID: QSim-002	Cryptanalysis	2 hours	12 hours ago	Completed
QKD Network Stress Test ID: QSim-003	Network	6 hours	24 hours ago	Analyzing
Post-Quantum Algorithm Test ID: QSim-004	Algorithm	3 hours	36 hours ago	Completed
Quantum-Classical Hybrid Test ID: QSim-005	Hybrid	4 hours	48 hours ago	Verified

Simulation Performance Comparison

Comparison between traditional and quantum-enhanced simulation capabilities. Quantum integration shows significant improvements across all metrics, with notable gains in scenario coverage (+41.5%) and processing speed (+31.9%).

Resource Optimization

Resource utilization trends showing significant optimization after hybrid implementation, with 45% reduction in overall usage.

Workload Distribution

Task distribution across system components showing balanced allocation and improved efficiency in hybrid setup.

Technical Appendix

Circuit Specifications

// Quantum Circuit Details
circuit = QuantumCircuit(8)
circuit.h([0,1,2,3])
circuit.cx([0,1], [4,5])
circuit.barrier()
circuit.measure_all()

Error Mitigation

Readout Error: M1 = 0.014 ± 0.002 Compensated
Gate Error: T1 = 95μs ± 5μs Monitored
Decoherence: T2 = 142μs ± 8μs Tracked

References

[1]	Smith et al. (2024) "Quantum Error Mitigation in Security Simulations"
[2]	Johnson et al. (2024) "Circuit Optimization for Security Applications"