Posté par: Robert B. Lisek Il y a 1 mois

(0 comments)

Presented by : Robert B. Lisek

Biography

Quantum Fields Synthesis represents a novel approach to sound generation using quantum physics, particularly quantum field theory, as fundamental building blocks. While classical synthesis describes sound as mechanical waves requiring a medium to propagate, QFS replaces traditional oscillators with quantum fields, each defined by a quantized wave function and its dynamics.

This system produces sound where complex wave functions, probability amplitudes, energy transitions, and spinor interactions generate unique audio experiences. The dynamics of quantum states are precisely controlled via Dirac and Klein-Gordon models.

The project employs a rigorous quantum sound synthesis framework, utilizing IBM's real quantum computing hardware and quantum neural networks to scale up the system. This enables control over the behavior of a swarm of software agents representing quantum states and their time dynamics, using wave-function measurements, energy levels, spins, and operators for superposition and entanglement.

QFS results in new sound  textures with unique timbral properties, spectral components, and non-classical patterns. The frequency spectra depend directly on the quantum states, spin dynamics define changes in sound timbre, energy jumps trigger transitions and leaps, and eigenstates modify the sound texture.

import numpy as np
from qiskit import QuantumCircuit, Aer, execute
from qiskit.quantum_info import Statevector
from qiskit.circuit.library import PauliEvolutionGate
from qiskit.opflow import PauliSumOp, Pauli
# Function quantum field
def simulate_field(num_qubits, dt, num_steps, m, lambda_coupling):
    # Define Hamiltonian terms
    hamiltonian_terms = []
    for i in range(num_qubits):
        hamiltonian_terms.append((0.5, Pauli(f"Z{i}")))  # Kinetic term
        if i < num_qubits - 1:
            hamiltonian_terms.append((0.5, Pauli(f"X{i} X{i+1}")))  # Potential term
        hamiltonian_terms.append((0.5 * m**2, Pauli(f"X{i}")))  # Mass term
        hamiltonian_terms.append((lambda_coupling, Pauli(f"X{i} X{i} X{i} X{i}")))  # Interaction term
    hamiltonian = PauliSumOp.from_list(hamiltonian_terms)
    # Time evolution
    qc = QuantumCircuit(num_qubits)
    initial_state = np.zeros(2**num_qubits)
    initial_state[2**(num_qubits//2)] = 1.0
    qc.initialize(initial_state, range(num_qubits))
    for step in range(num_steps):
        evolution_gate = PauliEvolutionGate(hamiltonian, time=dt)
        qc.append(evolution_gate, range(num_qubits))

Partager sur Twitter Partager sur Facebook

• ← Artists & Engineers: How We Communicate – Behind the Scenes of Sound Art (Installation/Theatre) with Multichannel Audio by Miyu Hosoi
• Bridging Audio, Visual, and AI Domains using the Elixir language, by Thibaut Barrère →