(Pseudo) Random Quantum States with Binary Phase
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Abstract (arXiv)
We prove a quantum information-theoretic conjecture due to Ji, Liu and Song (CRYPTO 2018) which suggested that a uniform superposition with random binary phase is statistically indistinguishable from a Haar random state. That is, any polynomial number of copies of the aforementioned state is within exponentially small trace distance from the same number of copies of a Haar random state. As a consequence, we get a provable elementary construction of pseudorandom quantum states from post-quantum pseudorandom functions. Generating pseduorandom quantum states is desirable for physical applications as well as for computational tasks such as quantum money. We observe that replacing the pseudorandom function with a (2t)-wise independent function (either in our construction or in previous work), results in an explicit construction for quantum state t-designs for all t. In fact, we show that the circuit complexity (in terms of both circuit size and depth) of constructing t-designs is bounded by that of (2t)-wise independent functions. Explicitly, while in prior literature t-designs required linear depth (for t > 2), this observation shows that polylogarithmic depth suffices for all t. We note that our constructions yield pseudorandom states and state designs with only real-valued amplitudes, which was not previously known. Furthermore, generating these states require quantum circuit of restricted form: applying one layer of Hadamard gates, followed by a sequence of Toffoli gates. This structure may be useful for efficiency and simplicity of implementation.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jun 2019.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 3; 2024: 3.
10 papers.
- 2023A survey on the complexity of learning quantum statescited by 32
- 2023Efficient Learning of Quantum States Prepared With Few Non-Clifford Gatescited by 7
- 2024Certifying almost all quantum states with few single-qubit measurementscited by 7
- 2022Low-Stabilizer-Complexity Quantum States Are Not Pseudorandomcited by 6
- 2024When can classical neural networks represent quantum states?cited by 6
- 2021Quantum Topological Data Analysis with Linear Depth and Exponential Speedupcited by 4
- 2022Topological data analysis on noisy quantum computerscited by 4
- 2024Testing classical properties from quantum datacited by 2
- 2023Quantum Solutions to the Privacy vs. Utility Tradeoffcited by 1
- 2022Unclonability and Quantum Cryptanalysis: From Foundations to Applicationscited by 0
- 2024Testing classical properties from quantum data19 Nov 2024
- 2024When can classical neural networks represent quantum states?30 Oct 2024
- 2024Certifying almost all quantum states with few single-qubit measurements10 Apr 2024
- 2023Quantum Solutions to the Privacy vs. Utility Tradeoff6 Jul 2023
- 2023A survey on the complexity of learning quantum states31 May 2023
- 2023Efficient Learning of Quantum States Prepared With Few Non-Clifford Gates22 May 2023
- 2022Unclonability and Quantum Cryptanalysis: From Foundations to Applications31 Oct 2022
- 2022Low-Stabilizer-Complexity Quantum States Are Not Pseudorandom29 Sep 2022
- 2022Topological data analysis on noisy quantum computers19 Sep 2022
- 2021Quantum Topological Data Analysis with Linear Depth and Exponential Speedup5 Aug 2021