Yuwei Jin

Quantum mechanics provides cryptographic primitives whose security is grounded in hardness assumptions independent of those underlying classical cryptography. However, existing proposals require low-noise quantum communication and long-lived quantum memory, capabilities which remain challenging to realize in practice. In this work, we introduce a quantum digital signature scheme that operates with only classical communication, using the classical shadows of states produced by random circuits as public keys. We provide theoretical and numerical evidence supporting the conjectured hardness of learning the private key (the circuit) from the public key (the shadow). A key technical ingredient enabling our scheme is an improved state-certification primitive that achieves higher noise tolerance and lower sample complexity than prior methods. We realize this certification by designing a high-rate error-detecting code tailored to our random-circuit ensemble and experimentally generating shadows for 32-qubit states using circuits with ≥ 80 logical (≥ 582 physical) two-qubit gates, attaining 0.90±0.01 fidelity. With increased number of measurement samples, our hardware-demonstrated primitives realize a proof-of-principle quantum digital signature, demonstrating the near-term feasibility of our scheme.

Quantum algorithms for binary optimization problems have been subject of extensive study. However, the application of quantum algorithms to integer optimization problems remains comparatively unexplored. In this paper, we study the Quantum Approximate Optimization Algorithm (QAOA) applied to integer problems on graphs, with each integer variable encoded in a qudit. We derive a general iterative formula for depth-$p$ QAOA expectation on high-girth $d$-regular graphs of arbitrary size. The cost of evaluating the formula is exponential in the QAOA depth $p$ but does not depend on the graph size. Evaluating this formula for Max-$k$-Cut problem for $p\leq 4$, we identify pararegimes ($k=3$ with degree $d \leq 10$ and $k=4$ with $d \leq 40$) in which QAOA outperforms the Frieze-Jerrum semi-definite programming (SDP) algorithm, which provides the best worst-case guarantee on the approximation ratio. To strengthen the classical baseline, we introduce a new heuristic algorithm based on the degree-of-saturation which empirically outperforms both the Frieze-Jerrum algorithm and shallow-depth QAOA. Nevertheless, we provide numerical evidence that QAOA may overtake this heuristic at depth $p\leq 20$. Our results show that moving beyond binary to integer optimization problems can open up new avenues for quantum advantage.

Achieving computational advantage using NISQ devices on practically useful problems is a long standing challenge. We report two papers that experimentally demonstrate a concrete application, namely certified randomness generation, which could be useful for multi-party cryptographic protocols and improving imperfect physical sources of randomness. Both papers involve substantial theoretical contributions to the protocol. We devise a realistic protocol that maximizes practical hardness. The verifier first asks the server to prepare a quantum state using a random circuit and then sends a random measurement basis right before the result must be received. This is repeated for many rounds. We show complexity theoretic evidence for entropy generation and provide improved entropy bounds against adversaries with oracle access to the random circuits. We also construct an end-to-end application of randomness amplification of imperfect sources into nearly perfect randomness, notably achieving everlasting security which uplifts computational security to information theoretic security.