Variational quantum solutions to the Shortest Vector Problem

Variational quantum solutions to the Shortest Vector Problem

Source Node: 1990464

Martin R. Albrecht1, Miloš Prokop2, Yixin Shen3, and Petros Wallden4

1King’s College London and SandboxAQ. email:
2University of Edinburgh. email:
3Royal Holloway, University of London. email:
4University of Edinburgh. email:

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A fundamental computational problem is to find a shortest non-zero vector in Euclidean lattices, a problem known as the Shortest Vector Problem (SVP). This problem is believed to be hard even on quantum computers and thus plays a pivotal role in post-quantum cryptography. In this work we explore how (efficiently) Noisy Intermediate Scale Quantum (NISQ) devices may be used to solve SVP. Specifically, we map the problem to that of finding the ground state of a suitable Hamiltonian. In particular, (i) we establish new bounds for lattice enumeration, this allows us to obtain new bounds (resp. estimates) for the number of qubits required per dimension for any lattices (resp. random q-ary lattices) to solve SVP; (ii) we exclude the zero vector from the optimization space by proposing (a) a different classical optimisation loop or alternatively (b) a new mapping to the Hamiltonian. These improvements allow us to solve SVP in dimension up to 28 in a quantum emulation, significantly more than what was previously achieved, even for special cases. Finally, we extrapolate the size of NISQ devices that is required to be able to solve instances of lattices that are hard even for the best classical algorithms and find that with approximately $10^3$ noisy qubits such instances can be tackled.

Quantum computers potentially offer speed-ups in solving many problems. Existing encryption schemes, used in everyday life, are based on the assumption that factoring a large number is hard. However, a quantum algorithm due to Shor, can solve this problem using a large fault-tolerant quantum computer efficiently. This would compromise the security of all these communications. Because of this threat, alternative encryption schemes are being considered, where their security relies on assumptions about the hardness of other mathematical problems that are believed to be hard for quantum computers. The most prominent such problem is the Shortest-Vector-Problem (SVP). While SVP is strongly believed to be hard for quantum computers, it is not clear how hard it is, or else, what is the best that a quantum computer (fault-tolerant or not) can do in solving this problem.

In our work we examine how to solve the SVP using quantum computers and algorithms that can be run on exiting or near-term, imperfect, devices. We significantly reduce the number of qubits required to run such algorithms by providing bounds on the coefficients of the shortest-vector and by excluding the zero vector in a more efficient way. We are able to solve SVP for dimension 28 in a simulation, and we extrapolate that the classical record for SVP can be tackled using quantum computers with around one thousand qubits. We note, however, that our results do not invalidate any security claims made by post-quantum candidates as part of current standardisation efforts to replace cryptographic algorithms.

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Cited by

[1] Yifeng Rocky Zhu, David Joseph, Cong Ling, and Florian Mintert, “Iterative Quantum Optimization with Adaptive Problem Hamiltonian”, arXiv:2204.13432, (2022).

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