Tight Bounds on the Spooky Pebble Game: Recycling Qubits with Measurements

TL;DR

This paper establishes tight bounds for the spooky pebble game, demonstrating how interleaved measurements can significantly reduce qubit requirements in quantum simulations of classical computations, with implications for quantum space-time trade-offs.

Contribution

It provides asymptotically tight trade-offs for the spooky pebble game, extending understanding of quantum space-time complexity beyond reversible pebbling.

Findings

Tight asymptotic bounds for the spooky pebble game on a line.

Quantum simulation of classical algorithms with reduced qubit usage.

PSPACE-hardness of approximating pebble requirements on general DAGs.

Abstract

Pebble games are popular models for analyzing time-space trade-offs. In particular, the reversible pebble game is often applied in quantum algorithms like Grover's search to efficiently simulate classical computation on inputs in superposition. However, the reversible pebble game cannot harness the additional computational power granted by irreversible intermediate measurements. The spooky pebble game, which models interleaved measurements and adaptive phase corrections, reduces the number of qubits beyond what reversible approaches can achieve. While the spooky pebble game does not reduce the total space (bits plus qubits) complexity of the simulation, it reduces the amount of space that must be stored in qubits. We prove asymptotically tight trade-offs for the spooky pebble game on a line with any pebble bound, giving a tight time-qubit tradeoff for simulating arbitrary classical sequential computation with the spooky pebble game. For example, for all $\epsilon \in (0,1]$, any classical computation requiring time $T$ and space $S$ can be implemented on a quantum computer using only $O(T/ \epsilon)$ gates and $O(T^{\epsilon}S^{1-\epsilon})$ qubits. This improves on the best known bound for the reversible pebble game with that number of qubits, which uses $O(2^{1/\epsilon} T)$ gates. We also consider the spooky pebble game on more general directed acyclic graphs (DAGs), capturing fine-grained data dependency in computation. We show that for an arbitrary DAG even approximating the number of required pebbles in the spooky pebble game is PSPACE-hard. Despite this, we are able to construct a time-efficient strategy for pebbling binary trees that uses the minimum number of pebbles.