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1. Quantum Data Sketches

   Zhang, Qin; Heidari, Mohsen (March 2025, 28th International Conference on Database Theory (ICDT 2025))

   Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress underscores the necessity for developing efficient and scalable quantum data management strategies. This goal faces immense challenges due to the exponential dimensionality of quantum data and its unique quantum properties such as no-cloning and measurement stochasticity. Specifically, classical storage and manipulation of an arbitrary n-qubit quantum state requires exponential space and time. Hence, there is a critical need to revisit foundational data management concepts and algorithms for quantum data. In this paper, we propose succinct quantum data sketches to support basic database operations such as search and selection. We view our work as an initial step towards the development of quantum data management model, opening up many possibilities for future research in this direction. 

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2. Quantum Data Sketches

   https://doi.org/10.4230/LIPICS.ICDT.2025.16  

   Zhang, Qin; Heidari, Mohsen (January 2025, Schloss Dagstuhl – Leibniz-Zentrum für Informatik)

   Roy, Sudeepa; Kara, Ahmet (Ed.)

   Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress underscores the necessity for developing efficient and scalable quantum data management strategies. This goal faces immense challenges due to the exponential dimensionality of quantum data and its unique quantum properties such as no-cloning and measurement stochasticity. Specifically, classical storage and manipulation of an arbitrary n-qubit quantum state requires exponential space and time. Hence, there is a critical need to revisit foundational data management concepts and algorithms for quantum data. In this paper, we propose succinct quantum data sketches to support basic database operations such as search and selection. We view our work as an initial step towards the development of quantum data management model, opening up many possibilities for future research in this direction. 

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3. Evaluating the evidence for exponential quantum advantage in ground-state quantum chemistry

   https://doi.org/10.1038/s41467-023-37587-6  

   Lee, Seunghoon; Lee, Joonho; Zhai, Huanchen; Tong, Yu; Dalzell, Alexander M.; Kumar, Ashutosh; Helms, Phillip; Gray, Johnnie; Cui, Zhi-Hao; Liu, Wenyuan; et al (April 2023, Nature Communications)

   Abstract Due to intense interest in the potential applications of quantum computing, it is critical to understand the basis for potential exponential quantum advantage in quantum chemistry. Here we gather the evidence for this case in the most common task in quantum chemistry, namely, ground-state energy estimation, for generic chemical problems where heuristic quantum state preparation might be assumed to be efficient. The availability of exponential quantum advantage then centers on whether features of the physical problem that enable efficient heuristic quantum state preparation also enable efficient solution by classical heuristics. Through numerical studies of quantum state preparation and empirical complexity analysis (including the error scaling) of classical heuristics, in both ab initio and model Hamiltonian settings, we conclude that evidence for such an exponential advantage across chemical space has yet to be found. While quantum computers may still prove useful for ground-state quantum chemistry through polynomial speedups, it may be prudent to assume exponential speedups are not generically available for this problem. 

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4. Quantum advantage in learning from experiments

   https://doi.org/10.1126/science.abn7293  

   Huang, Hsin-Yuan; Broughton, Michael; Cotler, Jordan; Chen, Sitan; Li, Jerry; Mohseni, Masoud; Neven, Hartmut; Babbush, Ryan; Kueng, Richard; Preskill, John; et al (June 2022, Science)

   Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors. 

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5. Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

   https://doi.org/10.1109/COMPSAC54236.2022.00097  

   Mohammad Masum, Mohammad Nazim (June 2022, IEEE Conference on Computers, Software & Applications)

   Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks. 

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