Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Harvard Data Science Review

Assume a Quantum Data Set

Mária Kieferová, Yuval R. Sanders

Published Jan 27, 2022• 3 citations
Publisher

Abstract

Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of the challenges of encoding nonquantum data for use by quantum computers.

DOI
10.1162/99608f92.69c5328d

Related papers

Encoding patterns for quantum algorithms

Manuela Weigold, Johanna Barzen, Frank Leymann +1 more

Abstract As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, quantum algorithms with a theoretical speed‐up often assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented by the state of the quantum computer and (ii) the data itself. In some cases, loading the data requires at least exponential time that destroys a potential speed‐up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. In this work, we, therefore, present six patterns that describe how data is handled by quantum computers.

IET Quantum Communication•Dec 1, 2021•101 citations

Expanding Data Encoding Patterns For Quantum Algorithms

Manuela Weigold, Johanna Barzen, Frank Leymann +1 more

As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, often algorithms which a theoretical speed-up assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented and (ii) the data itself. In some cases, loading the data requires at least exponential time which destroys a potential speed-up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. Therefore, understanding the consequences of a particular data encoding is crucial. To capture knowledge about different encodings, we present two data encoding patterns that extend our previous collection of encoding patterns [1].

IEEE International Conference on Software Architecture Companion (ICSA-C)•Mar 1, 2021•58 citations

Revisiting dequantization and quantum advantage in learning tasks

Jordan S. Cotler, Hsin-Yuan Huang, Jarrod R. McClean

It has been shown that the apparent advantage of some quantum machine learning algorithms may be efficiently replicated using classical algorithms with suitable data access -- a process known as dequantization. Existing works on dequantization compare quantum algorithms which take copies of an n-qubit quantum state $|x\rangle = \sum_{i} x_i |i\rangle$ as input to classical algorithms which have sample and query (SQ) access to the vector $x$. In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs. Because classical algorithms are a subset of quantum algorithms, this demonstrates that SQ access can sometimes be significantly more powerful than quantum state inputs. Our findings suggest that the absence of exponential quantum advantage in some learning tasks may be due to SQ access being too powerful relative to quantum state inputs. If we compare quantum algorithms with quantum state inputs to classical algorithms with access to measurement data on quantum states, the landscape of quantum advantage can be dramatically different. We remark that when the quantum states are constructed from exponential-size classical data, comparing SQ access and quantum state inputs is appropriate since both require exponential time to prepare.

arXiv:2112.00811•Dec 1, 2021•13 citations

Quantum computing for data science

Barry C. Sanders

Abstract I provide a perspective on the development of quantum computing for data science, including a dive into state-of-the-art for both hardware and algorithms and the potential for quantum machine learning.

arXiv:2302.08666•Journal of Physics Conference Series•Feb 1, 2023•5 citations

Parallel quantum trajectories via forking for sampling without redundancy

Daniel K Park, Ilya Sinayskiy, Mark Fingerhuth +2 more

Abstract The computational cost of preparing a quantum state can be substantial depending on the structure of data to be encoded. Many quantum algorithms require repeated sampling to find the answer, mandating reconstruction of the same input state for every execution of an algorithm. Thus, the advantage of quantum computation can diminish due to redundant state initialization. We present a framework based on quantum forking that bypasses this fundamental issue and expedites a family of tasks that require sampling from independent quantum processes. Quantum forking propagates an input state to multiple quantum trajectories in superposition, and a weighted power sum of individual results from each trajectories is obtained in one measurement via quantum interference. The significance of our work is demonstrated via applications to implementing non-unitary quantum channels, studying entanglement and benchmarking quantum control. A proof-of-principle experiment is implemented on the IBM and Rigetti quantum cloud platforms.

arXiv:1902.07959•New Journal of Physics•Jul 26, 2019•18 citations

Circuit-Based Quantum Random Access Memory for Classical Data

Daniel K. Park, Francesco Petruccione, June‐Koo Kevin Rhee

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.

arXiv:1901.02362•Scientific Reports•Mar 8, 2019•151 citations
Data-processing algorithms often require that the data is prepared in appropriate structures that are readily accessible or can be prepared on demand. Quantum computers derive their power from storing and manipulating quantum superpositions and could potentially speed up data science tasks. However, they often require input in the form of a quantum state that encodes a nonquantum data set. Here we describe some of the challenges of encoding nonquantum data for use by quantum computers.
Abstract As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, quantum algorithms with a theoretical speed‐up often assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented by the state of the quantum computer and (ii) the data itself. In some cases, loading the data requires at least exponential time that destroys a potential speed‐up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. In this work, we, therefore, present six patterns that describe how data is handled by quantum computers.
As quantum computers are based on the laws of quantum mechanics, they are capable of solving certain problems faster than their classical counterparts. However, often algorithms which a theoretical speed-up assume that data can be loaded efficiently. In general, the runtime complexity of the loading routine depends on (i) the data encoding that defines how the data is represented and (ii) the data itself. In some cases, loading the data requires at least exponential time which destroys a potential speed-up. And especially for the first generation of devices that are currently available, the resources (qubits and operations) needed to encode the data are limited. Therefore, understanding the consequences of a particular data encoding is crucial. To capture knowledge about different encodings, we present two data encoding patterns that extend our previous collection of encoding patterns [1].
It has been shown that the apparent advantage of some quantum machine learning algorithms may be efficiently replicated using classical algorithms with suitable data access -- a process known as dequantization. Existing works on dequantization compare quantum algorithms which take copies of an n-qubit quantum state ∣x⟩=∑ixi∣i⟩|x\rangle = \sum_{i} x_i |i\rangle∣x⟩=∑i​xi​∣i⟩ as input to classical algorithms which have sample and query (SQ) access to the vector xxx. In this note, we prove that classical algorithms with SQ access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs. Because classical algorithms are a subset of quantum algorithms, this demonstrates that SQ access can sometimes be significantly more powerful than quantum state inputs. Our findings suggest that the absence of exponential quantum advantage in some learning tasks may be due to SQ access being too powerful relative to quantum state inputs. If we compare quantum algorithms with quantum state inputs to classical algorithms with access to measurement data on quantum states, the landscape of quantum advantage can be dramatically different. We remark that when the quantum states are constructed from exponential-size classical data, comparing SQ access and quantum state inputs is appropriate since both require exponential time to prepare.
Abstract The computational cost of preparing a quantum state can be substantial depending on the structure of data to be encoded. Many quantum algorithms require repeated sampling to find the answer, mandating reconstruction of the same input state for every execution of an algorithm. Thus, the advantage of quantum computation can diminish due to redundant state initialization. We present a framework based on quantum forking that bypasses this fundamental issue and expedites a family of tasks that require sampling from independent quantum processes. Quantum forking propagates an input state to multiple quantum trajectories in superposition, and a weighted power sum of individual results from each trajectories is obtained in one measurement via quantum interference. The significance of our work is demonstrated via applications to implementing non-unitary quantum channels, studying entanglement and benchmarking quantum control. A proof-of-principle experiment is implemented on the IBM and Rigetti quantum cloud platforms.
A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.