
Presented by : Robert B. Lisek
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 npfrom qiskit import QuantumCircuit, Aer, executefrom qiskit.quantum_info import Statevectorfrom qiskit.circuit.library import PauliEvolutionGatefrom qiskit.opflow import PauliSumOp, Pauli# Function quantum fielddef 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))