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April 15, 2018

# qramutils: gather statistics for your QRAM

Generally, with the term QRAM people are referring to an oracle, or generically to a unitary, that gets called with the purpose of creating a state in a quantum circuit. This state represents some (classical) data that you want to process later in your algorithm. More formally, QRAM allows you to perform operations like: $\ket{i}\ket{0} \to \ket{i}\ket{x_i}$ for $x_i \in \mathbb{R}$ for some $i \in [n]$. This model can be used to create states proportional to classical vectors, and allowing us to perform queries: $\ket{i}\ket{0} \to \ket{i}\ket{x(i)}$ for $x(i) \in \mathbb{R}^d$ for some $i \in [n]$

Querying the QRAM is assumed to be done efficiently. The running time is expected to be polylogarithmic in the matrix dimensions, but eventually the time complexity might polynomial in other parameters. As an example, in QRAM described in Kerenidis and Prakash (2017)Kerenidis and Prakash (2016)Prakash (2014) the authors stores a matrix decomposition such that the running time of a query might depend on the Frobenius norm, or a parametrized function, which is specific to their implementation. In this model, the best parametrization of the decomposition might depend on the dataset. This means that in practice, you might need to estimate these parameters, and therefore I’ve decided to write a library for this. Specifically, given a matrix $A$ to store in QRAM, you have to find the value $p \in \left(0, 1 \right)$ such that it minimize the function: $\mu_p(A) = \sqrt{ s_{2p}(A) s_{2(1-p)}(A^T)}$ where $s_p(A) := max_{i \in [m]} |A|_F^p$ is the maximum $l_p$ norm to the power of $p$ of the row vectors.

Being able to estimate parameters of a dataset might happen also with other model of access to the data. For instance, other algorithms such HHL uses Hamiltonian simulation, which has an access model that makes the complexity of the algorithm depend on the sparsity.

So far qramutils analyze a given numpy matrix for the following parameters:

• The sparsity.

• The conditioning number.

• The Frobenius norm (of the rescaled matrix such that $0< \sigma_i < 1$).

• The best parameter $p$ for the matrix decomposition described above.

• Some boring and common plotting.

Here you can find the repository.

This code might be improved in many directions! For instance, I’d like to integrate in the library the code for plotting the parameters for various PCA dimensions and/or degree of polynomial expansion, integrate options for dataset normalization, scaling, and maybe expand the type of accepted input data, and so on..

Ideally, for other kind of matrices there hopefully might be other kind matrix decompositions available and therefore there might be the need to estimate other parameters in the future. This is where I’ll add that code for that. :)

This is an example of usage on the MNIST dataset:

\$ pipenv run python3 examples/mnist_QRAM.py --help
usage: mnist_QRAM.py [-h] [--db DB] [--generateplot] [--analize]
[--pca-dim PCADIM] [--polyexp POLYEXP]
[--loglevel {DEBUG,INFO}]

Analyze a dataset and model QRAM parameters

optional arguments:
-h, --help            show this help message and exit
--db DB               path of the mnist database
--generateplot        run experiment with various dimension
--analize             Run all the analysis of the matrix
--pca-dim PCADIM      pca dimension
--polyexp POLYEXP     degree of polynomial expansion
--loglevel {DEBUG,INFO}
set log level


This is the output, assuming you have a folder called data that holds the MNIST dataset.

pipenv run python3 examples/mnist_QRAM.py --db data --analize --loglevel INFO
04-01 22:23 INFO     Calculating parameters for default configuration: PCA dim 39, polyexp 2
04-01 22:24 INFO     Matrix dimension (60000, 819)
04-01 22:24 INFO     Sparsity (0=dense 1=empty): 0.0
04-01 22:24 INFO     The Frobenius norm: 4.6413604982930385
04-01 22:26 INFO     best p 0.8501000000000001
04-01 22:26 INFO     Best p value: 0.8501000000000001
04-01 22:26 INFO     The \mu value is: 4.6413604982930385
04-01 22:26 INFO     Qubits needed to index+data register: 26.


If you want to use the library in your source code:

    libq = qramutils.QramUtils(X, logging_handler=logging)

logging.info("Matrix dimension {}".format(X.shape))

sparsity = libq.sparsity()
logging.info("Sparsity (0=dense 1=empty): {}".format(sparsity))

frob_norm = libq.frobenius()
logging.info("The Frobenius norm: {}".format(frob_norm))

best_p, min_sqrt_p = libq.find_p()
logging.info("Best p value: {}".format(best_p))

logging.info("The \\mu value is: {}".format(min(frob_norm, min_sqrt_p)))

qubits_used = libq.find_qubits()
logging.info("Qubits needed to index+data register: {} ".format(qubits_used))


To install, you just need to do the following:

pipenv run python3 setup.py sdist


And then, your package will be ready to be installed as:

pipenv install dist/qramutils-0.1.0.tar.gz

Kerenidis, Iordanis, and Anupam Prakash. 2016. “Quantum Recommendation Systems.” *ArXiv Preprint ArXiv:1603.08675*.
———. 2017. “Quantum Gradient Descent for Linear Systems and Least Squares.” *ArXiv Preprint ArXiv:1704.04992*.
Prakash, Anupam. 2014. *Quantum Algorithms for Linear Algebra and Machine Learning*. University of California, Berkeley.