VLDB'25 vision paper "Quantum Data Management in the Noisy Intermediate-Scale Quantum Era"

This repository contains the research and associated works related to the VLDB 2025 vision paper, "Quantum Data Management in the NISQ Era".

It includes supplementary materials, detailed experimental settings, and comprehensive results, particularly in Appendices B3 and B4 of our technical report. This repository serves as a resource for researchers interested in the intersection of classical database technologies and quantum computing.

Citation

BibTeX

tex @article{hai2025qdmvision, title={Quantum Data Management in the Noisy Intermediate-Scale Quantum Era}, author={Hai, Rihan and Hung, Shih-Han and Coopmans, Tim and Littau, Tim and Geerts, Floris}, journal={Proceedings of the VLDB Endowment}, year={2025}, publisher={VLDB Endowment}, note={to appear} }

Reproducing the shown results

The results shown in the paper can either be reproduced using the benchmark notebook or by running the simulation script. Required packages for the Python installation can be installed using the command

pip install -r requirements.txt

Here is a list of the included dependencies

duckdb==1.1.3 jupyter_client==8.6.3 jupyter_core==5.7.2 numpy==2.2.1 opt-einsum==3.3.0 psycopg2-binary==2.9.10

To generate an SQL query from a quantum circuit provided as json you can use the circuit_sqlgen file. A circuit should be of the following format:

{ "number_of_qubits": 3, "gates": [ { "qubits": [0], "gate": "H" }, { "qubits": [0, 1], "gate": "CNOT" }, { "qubits": [1, 2], "gate": "CNOT" } ] }

This json quantum circuit would create the GHZ state for three qubits.

When running the simulation.py, make sure there is PostgreSQL running on your machine with the following settings:

Using PostgreSQL is optional and if not needed should be explicitly excluded. PostgreSQL should have the following configuaration:

• POSTGRES_DB: 'postgres'
• POSTGRES_USER: 'postgres'
• POSTGRES_PASSWORD: 'password'
• HostPort: "5432"

If you want to run a customized version of the simulations please refer to the API Documentation below.

To quickly do a test run without PostgreSQL and DuckDB run simulation.py with the argument sqlite for an extensive test or test for a very quick test run.

Simulation API

1. Functions

   • sqltonp
   • generateghzcircuit
   • generatewcircuit
   • generateqftcircuit
   • generateqpecircuit
   • generateghzqft
   • generatewqft

2. Classes

   • QuantumCircuit
   • Gate

Functions

sqltonp

Parameters:

• db_result (list): Database query result rows.
• expected_shape (tuple): Shape of the resulting tensor.
• complex_flag (bool, optional): If True, assumes complex values in the result.

Returns:

• np.ndarray: The constructed NumPy tensor.

generateghzcircuit

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.
• reverse (bool, optional): If True, the circuit will be reversed

Returns:

• dict: The constructed circuit as dict.

generatewcircuit

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.
• reverse (bool, optional): If True, the circuit will be reversed

Returns:

• dict: The constructed circuit as dict.

generateqftcircuit

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.
• reverse (bool, optional): If True, the circuit will be reversed

Returns:

• dict: The constructed circuit as dict.

generateqpecircuit

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.

Returns:

• dict: The constructed circuit as dict.

generateghzqft

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.

Returns:

• dict: The constructed circuit as dict.

generatewqft

Parameters:

• num_qubits (int): Number of Qubits in the GHZ state preparation.

Returns:

• dict: The constructed circuit as dict.

Classes

QuantumCircuit

Class for constructing and simulating quantum circuits.

Constructor:

python QuantumCircuit(num_qubits: int = None, circuit_dict: dict = None)

Attributes:

• num_qubits (int): Number of Qubits in the circuit
• gates (list): List of gates in the circuit
• tensor_uniques (dict): All unique tensors of the initial state and the gates in the circuit
• einsum (str): Einstein Summation Notation as str
• mps (MPS | None): If simulation method "MPS" is used this contains the corresponding MPS Object
• con (Connection | None): DB Connection if exists
• cur (Cursor | None): DB Cursor if exists
• dispatcher (dict): Gate dispatcher to map str gate names to corresponding gate method

Methods:

• to_query(complex=True): Converts the circuit to a SQL query.
• convert_to_einsum(): Converts the circuit to Einstein summation notation.
• run(contr_method='np'): Runs the circuit using the specified contraction method.
• benchmark_circuit_performance(n_runs): Benchmarks the circuit's performance.
• export_circuit_query(): Exports the circuit query.
• One-Qubit_Gate(qubit): possible gates: H, X, Y, Z
• Controlled-Gate(control_qubit, target_qubit): possible gates: CNOT, CY, CZ
• R(qubit, k): applies Phase Shift gate with parameter k
• RY(qubit, theta): applies Rotation around Y-axis of Bloch-Sphere with angle theta
• G(qubit, p): G(p) gate as described in Cruz et al.

Gate

Base class for quantum gates.

Constructor:

python Gate(qubits: list, tensor: numpy.ndarray, name: str = None, two_qubit_gate: bool = False)

Attributes:

• qubits (list): Qubits the gate acts on.
• tensor (np.ndarray): Matrix representation of the gate.
• two_qubit_gate (bool): Indicates if the gate is a two-qubit gate.
• gate_name (str): Name of the gate.