Shot-Based Quantum Encoding: A Data-Loading Paradigm for Quantum Neural Networks

Shot-Based Quantum Encoding is a new data-loading paradigm for Quantum Neural Networks (QNNs) designed to overcome the efficiency and hardware compatibility issues of traditional methods. It aims to enable robust data input for QNNs on current noisy quantum hardware by addressing limitations like inefficient Hilbert-space utilization and excessive circuit depth.

intermediate15 min5 steps

The play

Identify the QML Data Loading Bottleneck
Understand that efficient data loading is a critical challenge for practical near-term Quantum Machine Learning (QML) applications due to the unique constraints of quantum hardware.
Evaluate Traditional Encoding Limitations
Recognize the inherent limitations of conventional quantum data encoding methods (e.g., angle, amplitude, basis encoding), which often underutilize quantum hardware's Hilbert-space capacity or require impractically deep circuits.
Acknowledge NISQ Hardware Constraints
Factor in the strict coherence budget of Noisy Intermediate-Scale Quantum (NISQ) hardware, which makes deep quantum circuits impractical and necessitates more hardware-compatible encoding strategies.
Explore the Shot-Based Encoding Paradigm
Investigate Shot-Based Quantum Encoding as a novel approach that proposes to mitigate these issues, offering a more compatible solution for current quantum hardware by rethinking how classical data is mapped to quantum states through measurement shots.
Assess Impact on QNN Design
Consider how adopting a paradigm like Shot-Based Quantum Encoding could significantly influence the design, feasibility, and performance of Quantum Neural Networks, enabling more effective and robust data input for quantum AI applications.

Starter code

from qiskit import QuantumCircuit, transpile
from qiskit_aer import AerSimulator
import numpy as np

# This example demonstrates a basic angle encoding, a traditional method.
# Shot-Based Quantum Encoding offers an alternative approach to this data loading step.

# Create a quantum circuit with 2 qubits
qc = QuantumCircuit(2, 2)

# Simulate angle encoding for a 2-dimensional classical vector [x, y]
data_vector = np.array([0.5, 0.8])

# Map data_vector components to qubit rotation angles
qc.ry(np.arcsin(data_vector[0]), 0) # Encode data_vector[0] on qubit 0
qc.ry(np.arcsin(data_vector[1]), 1) # Encode data_vector[1] on qubit 1

# Add a simple entanglement layer (e.g., for a QNN)
qc.cx(0, 1)

# Measure the qubits
qc.measure([0, 1], [0, 1])

# Simulate the circuit
simulator = AerSimulator()
compiled_circuit = transpile(qc, simulator)
job = simulator.run(compiled_circuit, shots=1024)
result = job.result()
counts = result.get_counts(qc)
print("Measurement Counts:", counts)

Source

Paperarxiv.org