Python software and data for the publication "Mitigating the barren plateau problem in linear optics" by Matthew D. Horner. To run this Python code, first install Aegiq's Lightworks and the fermion sampling extension. pip install lightworks pip install lightworks[fermionic] Notebooks This contains notebooks for various figures. Run each notebook to generate the data. Figure 2 - Barren plateau scaling of bosonic and fermionic linear optics. Figure 5 - Measurement-induced dual-valued phase shifter (DVPS). Figure 8 - False herald probasbility calculation for the DVPS Figure 9 - Cost landscape comparison. Figure 10 - Solving a random QUBO problem using gradient descent or Rotosolve for either bosonic or fermionic statistics. Figure 12 - Calculation of the Fourier modes in the QUBO cost landscape. Raw data This also contains raw data for Figure 2 - The barren plateau scaling figures stored in three .npz files Figure 5 - A set of pre-computed DVPS unitaries in a single .npz file Figure 9 - The raw cost landscape data in a single .npz file. Figure 12 - All subfigure data in 12 .txt files. This data is produced by the notebooks so is not necessary, but is included to save time. Scripts The scripts lightworks_optimiser_classes.py lightworks_qubo.py contain code for optimising via gradient descent and Rotosolve. These are called by the notebooks.
Python software and data for the publication "Mitigating the barren plateau problem in linear optics" by Matthew D. Horner. To run this Python code, first install Aegiq's Lightworks and the fermion sampling extension. pip install lightworks pip install lightworks[fermionic] Notebooks This contains notebooks for various figures. Run each notebook to generate the data. Figure 2 - Barren plateau scaling of bosonic and fermionic linear optics. Figure 5 - Measurement-induced dual-valued phase shifter (DVPS). Figure 8 - False herald probasbility calculation for the DVPS Figure 9 - Cost landscape comparison. Figure 10 - Solving a random QUBO problem using gradient descent or Rotosolve for either bosonic or fermionic statistics. Figure 12 - Calculation of the Fourier modes in the QUBO cost landscape. Raw data This also contains raw data for Figure 2 - The barren plateau scaling figures stored in three .npz files Figure 5 - A set of pre-computed DVPS unitaries in a single .npz file Figure 9 - The raw cost landscape data in a single .npz file. Figure 12 - All subfigure data in 12 .txt files. This data is produced by the notebooks so is not necessary, but is included to save time. Scripts The scripts lightworks_optimiser_classes.py lightworks_qubo.py contain code for optimising via gradient descent and Rotosolve. These are called by the notebooks.
We prove that variational quantum algorithms using discrete variable boson samplers suffer from barren plateaus and that by changing to fermionic statistics, which is classically efficient to simulate, the barren plateau scaling is less severe. Moreover, when using fermionic statistics the cost landscape boils down to one unit frequency Fourier mode per variational parameter, irrespective of circuit design, ansatz or variational problem, allowing for more efficient parameter shift rules. This simplified cost landscape allows for the gradient-free Rotosolve algorithm to be used to gain a considerable speed-up over the original bosonic algorithm. Motivated by this, we propose a non-linear parametrised phase shifter, the dual-valued phase shifter, that generates these single-harmonic cost landscapes for bosonic statistics. We propose three ways to achieve this by using either non-linear optics, measurement-induced non-linearities, or entangled resource states simulating fermionic statistics. The latter two require linear optics only, allowing for implementation with widely-available technology today. Despite this, the classical simulability of free fermions and the fact the fermionic algorithm can defeat the bosonic algorithm casts doubt on whether the quantum advantage of boson sampling results in any practical advantage in this setting.
Overview of data provided in support of Towards near-term quantum simulation of materials Contents of this folder: `analyse_materials_results.py`: script used to generate the summary tables and figures presented in the manuscript. `towards_quantum_simulation_data`: raw data used and produced when studying various 3D materials. `towards_quantum_simulation_analysis`: tables (in `.tex` format) and figures (as PDFs) presented in the manuscript. The contents of `towards_quantum_simulation_analysis` were generated by `analyse_materials_results.py`, i.e. the user need not run the script to produce the output files. Users can generate these analyses directly by running ```python analyse_materials_results.py```, though note the following packages will first need to be installed: `pandas >= 1.2.3` `numpy >= 1.23` `seaborn >= 0.11.1` `lfig >= 0.1.3` `matplotlib > 3.7.0` Included data Within `towards_quantum_simulation_data`, there are subfolders for each of the materials described in the manuscript, i.e. `SrVO3`, `GaAs`, `H3S`, `Si`, `Li2CuO2`. Within each material's folder are further subfolders for the `hamiltonian` and `encoding` used to represent the material, as well as subfolders for each of the `algorithms` studied. `hamiltonian`: files which specify the Hamiltonian for the material under study. There are a number of files `interactions.json` Hamiltonian terms in terms of Majorana monomials. `map_majorana_to_mode.json` keys are Majorana indices; values are the mode index to which they are associated. `map_mode_to_group.json` keys are mode indices; values are the group (or site) index to which they are associated. `map_group_to_position.json` keys are group indices; values are the corresponding 3D Cartesian coordinates of the lattice used to represent the material. `stage_data.json` contains key/value pairs of any other fields of interest. `encoding` files which specify the fermionic encoding which is customised for the material under study. `encoding.json` which details the edges of the hybrid compact encoding described in Section VI of the supplementary material. `precompiler.json` contains all the information which permits the encoding construction, including the Hamiltonian terms (`_interactions`) which match those in `hamiltonian/interactions.json`. The same mappings as present in the Hamiltonian data(a.g. `map_group_to_position`). `stage_data.json` contains key/value pairs of any other fields of interest. `algorithms` contains subfolders for each of the algorithms desribed in the manuscript Those explored for circuits depths: `TDSSplitTermsPriorityCircuitDepth` (TDS in the manuscript) `TDSSplitTermsPriorityCircuitDepthNoSwapNetwork` (TDS\*) `VQESplitTerms` (VQE) `VQESplitTermsNoSwapNetwork` (VQE*) each of which contain the files, inside the `circuitry` folder: `circuit_terms.csv`, which lists each individual term, together with their Pauli string and rotation angle, required to construct the corresponding quantum circuit `circuit_layers_to_implement.csv` groups the same terms into layers to achieve parallelism in the circuit. `stage_data.json` contains key/value pairs of any other fields of interest. and those used to compose measurement layers, as outlined in Section VII D of the supplementary material: `MeasurementCommutativity` `MeasurementNaiveQubitwise` `MeasurementNonCrossing` each of which contain the files, inside the `compilation` folder: `layers.csv` lists the terms which may be measured simultaneously to achieve the measurement strategies shown in Table S14. `stage_data.json` contains key/value pairs of any other fields of interest. CSV files In `towards_quantum_simulation_data`, there are unified CSV files containing the results of applying the procedures described in the manuscript to the target materials. `circuit_costs.csv`: results of running the circuit compiler described in the manuscript. `measurements.csv`: results of running the measurement compiler described in the manuscript. These CSVs are used in the analysis script `analyse_materials_results.py` to produce the figures and tables presented in the manuscript.
This archive contains the numerical data and scripts used to generate the figures for the paper “Energy-selective quantum search with Ising Hamiltonian phase oracles”. The main numerical dataset is used for the scaling analysis of the correlation-induced resonance displacement φ₀ in the zero-field random Ising ensemble. The simulations use dimensionless units with σ_J = 1 and the Hamiltonian H(s) = 2 sum_{i<j} J_ij s_i s_j, where the couplings are independent Gaussian random variables, J_ij ~ N(0, 1), and h_i = 0. For each random Ising instance, the archive stores the truncated characteristic-function coefficients g_m = 2^{-n} Tr exp(-i m T H), m = 0, ..., 15. These coefficients are used to extract the local displacement φ₀ from the zero of the imaginary part of the response function C(φ) = 2 sum_{m=0}^{15} g_m (-1)^m exp(i m φ) - 1. The quantity plotted in the paper is the disorder-averaged second moment <φ₀²> and the scaled combination n⁶<φ₀²>. The Monte Carlo average is over independent disorder realizations only. For each realization, the trace over spin configurations is evaluated deterministically. The data cover even system sizes n = 18, 20, 22, ..., 36. For the largest systems, the computation splits the spins into two equal halves, performs GPU block contractions, and uses the zero-field spin-flip symmetry H(s)=H(-s) to reduce the sum by a factor of two. Archive contents - scripts/01_generate_phi0_coefficients.py Generates the characteristic-function coefficient files g_m for the random Ising ensemble. - scripts/02_analyze_phi0_scaling.py Computes φ₀, bootstrap confidence intervals, the finite-n covariance-sum comparison, and the scaling plots. - scripts/03_make_gaussian_amplification.py Generates the Gaussian amplification figure used as Fig. 1 in the paper. - data/phi0_coefficients/ Directory for the NumPy .npz files containing the characteristic-function coefficients. The production files have names of the form Nq_<n>_K_<K>.npz. The .npz files are saved without pickle objects. They can be read with standard NumPy using np.load. Typical usage To regenerate the Gaussian amplification figure: python scripts/03_make_gaussian_amplification.py --output-dir output/fig1_gaussian To analyze the φ₀ scaling data: python scripts/02_analyze_phi0_scaling.py \ --data-dir data/phi0_coefficients \ --output-dir output/phi0_scaling To run a short test calculation of the characteristic-function coefficients: python scripts/01_generate_phi0_coefficients.py \ --smoke-test \ --output-dir output/smoke_test_coefficients To regenerate the full production dataset on a CUDA-capable GPU: python scripts/01_generate_phi0_coefficients.py \ --production \ --output-dir data/phi0_coefficients The production calculations were performed using CuPy on an NVIDIA GPU. The analysis and plotting scripts use NumPy, pandas, and Matplotlib.
Shortcuts to adiabaticity with a quantum control field — Code & data========================================================== This repository contains a Jupyter notebook and supporting data files (csv) to reproduce the numerical results and figures in our manuscript (arXiv 2508.06304), which investigates adiabatic shortcuts mediated by a quantum control field.The code implements a Landau–Zener two-level system coupled to a spectator qubit, time evolution of the full system (LZ qubit + spectator) via QuTiP, parameter sweeps (parallelized with joblib), and plotting utilities for the manuscript figures. Related publication----------------------------This code is complementary to the preprint: https://arxiv.org/abs/2508.06304. Requirements---------------------The simulations were developed for and tested on Python 3.9+. Recommended packages and minimum versions: - Python >= 3.9- numpy >= 1.22- scipy >= 1.8- matplotlib >= 3.5- qutip >= 4.6 (install a recent stable qutip; some installations require `pip install qutip` or `conda` channels)- joblib >= 1.2- tqdm >= 4.60- pandas (optional, for CSV handling)- jupyterlab / notebook (for interactive use) Running the notebook (interactive) Open the notebook with JupyterLab or Jupyter Notebook. Execute cells sequentially in the order indicated by the notebook outline.The sections “Definitions” and “Figure imports and setup” must be executed before generating any data or figures, as they define all operators, and helper functions. The notebook is organized into separate sections for each figure of the manuscript, allowing you to re-run only the figure(s) of interest. Note: Figure 4 (Density plot) is configured to load precomputed data files to reduce computational cost. To reproduce Figure 4 without recomputing the full dataset, ensure the required .csv files are downloaded and located in the same directory as the notebook. To explore different parameter regimes: Modify the relevant parameter definitions (e.g., anneal_window, g, epsilon, omega_c, x0, x0_vals, q_vals). To include dissipation of the spectator (ancilla) qubit, set the parameter kappa> 0. For questions about reproducing the figures or about parameter choices used in the paper, contact the corresponding authors listed in the preprint. If you reuse this data/code, please cite the associated preprint and this code archive.
We provide the numerical codes to produce the data contained in "Competition between pair and single-particle superfluidity in bosonic quasi-flat bands: A Gaussian state approach", https://arxiv.org/abs/2605.26231, as well as notebooks to plot them. v1 contains a minimal version of Python and Julia codes. v2 provides the final version of the plots, explicitly stores data used in the plots and avoids Julia notebooks (in order to avoid compatibility issues). The LaTeX script allows to generate the manuscipt. Some of the Python codes rely on the external library QuSpin.py.
This record contains code, notebooks, data, and figure scripts associated with the project. The description below lists the contents of the upload and their roles. strongsym.zip: Zip archive used for the Zenodo upload. It contains the simul/, finsize_data/, and dependencies/ directories. simul/ : Simulation and analysis notebooks and scripts. MF_*.ipynb - Mean-field analysis notebooks. finite_size_spins.ipynb - Finite-size dynamics for 2 level system alt_finite_size_spins.ipynb - Alternative finite-size with a stress on Liouvullian spectrum analysis in the two level system. liouv_spec_spin1.ipynb -Finite-size spectrum analysis for three level model. find_symmetry.ipynb - Symbolic symmetry-finding notebook. finsize_data/ : Data files used to generate figures. fig_script.ipynb: Notebook to generate the figures overview_sketch.ipynb: Notebook to generate schmatics and diagrams (no raw data) dependencies/ : Environment specification files, for both python (.yml) and julia (.toml) Usage: Unzip strongsym.zip, set up environments using the files in dependencies/, and run the notebooks in simul/. Data required for figures is in finsize_data/; figure scripts are at the repository root. For a more detailed walkthrough and documentation, see the GitHub repository: https://github.com/MarcNairn/spin_strong_symmetries_phase