A comprehensive simulation toolkit for quantum computation, providing state-of-the-art modeling of quantum circuits, algorithms, and visualizations.
1import numpy as np 2 3class QuantumState: 4 def __init__(self, name: str, num_qubits: int = 1): 5 self.name = name 6 self.num_qubits = num_qubits 7 self.state = self.initialize_state() 8 9 def initialize_state(self) -> np.ndarray: 10 # Initialize to |0> state 11 state = np.zeros(2**self.num_qubits, dtype=np.complex128) 12 state[0] = 1 13 return state 14 15 def apply_gate(self, gate, target_qubit, control_qubit=None): 16 # Apply quantum gate to the state 17 if gate == QuantumGate.HADAMARD: 18 self._apply_hadamard(target_qubit) 19 elif gate == QuantumGate.CNOT: 20 self._apply_cnot(control_qubit, target_qubit)
Our quantum computing framework provides powerful tools for simulation, algorithm development, and visualization of quantum phenomena.
Comprehensive quantum state representation with support for superposition, entanglement, and measurement operations on systems of arbitrary qubit size.
Design complex quantum circuits with a rich set of gates including Hadamard, CNOT, Pauli, and Phase, with high-performance simulation of execution.
Implement and test quantum algorithms including Grover's Search and Quantum Fourier Transform with optimized components for accuracy and performance.
Rich visualization tools for quantum circuits, Bloch spheres, and probability distributions to better understand quantum states and operations.
Efficient scheduling and resource allocation with adaptive prioritization to optimize quantum task execution in complex computational workflows.
Built-in quantum error correction codes and noise models to simulate and mitigate errors in quantum calculations for more robust computations.
Our framework supports a comprehensive set of quantum gates that can be combined to create powerful quantum circuits.
Creates superposition by transforming |0⟩ to (|0⟩ + |1⟩)/√2 and |1⟩ to (|0⟩ - |1⟩)/√2
Quantum NOT gate - flips the state of a qubit from |0⟩ to |1⟩ and vice versa
Controlled-NOT gate - flips the target qubit if the control qubit is |1⟩
Introduces a phase shift of π for the |1⟩ state, leaving |0⟩ unchanged
π/4 phase rotation gate - introduces a phase shift of π/4
Exchanges the quantum states of two qubits
Leverage the power of quantum computing with these state-of-the-art algorithm implementations.
Our implementation of Grover's algorithm provides quadratic speedup for unstructured search problems, finding a marked item among N items in O(√N) steps instead of O(N).
class GroverSearch(QuantumAlgorithm): def __init__(self, n_qubits, target_state): self.n_qubits = n_qubits self.target_state = target_state self.circuit = QuantumCircuit(n_qubits) def run(self): # Initialize superposition for qubit in range(self.n_qubits): self.circuit.add_gate( QuantumGate.HADAMARD, qubit) # Optimal iterations iterations = max(1, int( np.pi/4 * np.sqrt(2**self.n_qubits))) for _ in range(iterations): self._oracle() self._diffusion() return self.circuit.measure()
Our Quantum Fourier Transform (QFT) implementation efficiently transforms quantum states from position to momentum representation, a fundamental building block for many quantum algorithms.
class QuantumFourierTransform(QuantumAlgorithm): def __init__(self, n_qubits): self.n_qubits = n_qubits self.circuit = QuantumCircuit(n_qubits) def run(self): # Apply Hadamard and controlled phase gates for i in range(self.n_qubits): self.circuit.add_gate( QuantumGate.HADAMARD, i) for j in range(i + 1, self.n_qubits): # Apply controlled phase rotation self.circuit.add_gate( QuantumGate.CNOT, j, i) self.circuit.add_gate( QuantumGate.PHASE, j) self.circuit.add_gate( QuantumGate.CNOT, j, i) # Swap qubits to complete QFT self._swap_qubits() return self.circuit.run()
Compare the performance of classical vs. quantum algorithms for common computational tasks.
View detailed benchmarksHigh-performance quantum simulation with advanced noise modeling and error correction.
Efficiently simulate quantum systems with up to 30+ qubits using optimized linear algebra operations.
Simulate realistic quantum noise including depolarizing, amplitude damping, and phase flip errors.
Implement quantum error correction with bit flip codes, phase flip codes, and the Shor code.
Compile circuits for specific architectures with gate decomposition and optimization techniques.
Gain insights into quantum systems with our comprehensive visualization suite.
Perfect entanglement: Measuring one qubit instantly determines the state of the other.
Efficiently manage quantum computing resources with advanced scheduling and prioritization.
Dynamically adjust task priorities based on historical performance and current resource availability.
Efficiently handle complex task dependencies with visualization and critical path analysis.
Track resource utilization and execution times with detailed logging and visualization.
Parallelize quantum tasks with efficient thread management and resource allocation.
Comprehensive guides and examples to help you get started with the Quantum Framework.
Complete documentation of all classes, methods, and parameters in the Quantum Framework.
Step-by-step tutorials and comprehensive guides for beginners and experienced users.
Open-source code repository, issue tracking, and community resources.
Download the Quantum Framework and explore the possibilities of quantum computing simulation.
pip install quantum-framework
Clone from GitHub repository
Comprehensive guides and API reference
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