# The Power of the Lorentz Quantum Computer

## TL;DR

This paper explores a new theoretical quantum computing model that can solve complex problems faster than traditional models.

## Contribution

The paper introduces the Lorentz quantum computer and proves its equivalence to the complexity class P♯P.

## Key findings

- LQC can solve NP-hard problems like maximum independent set in polynomial time.
- LQC can simulate quantum computing with postselection but not vice versa.
- The complexity class BLQP is shown to contain the entire polynomial hierarchy.

## Abstract

We analyze the power of the recently proposed Lorentz quantum computer (LQC), a theoretical model leveraging hyperbolic bits (hybits) governed by complex Lorentz transformations. We define the complexity class BLQP (bounded-error Lorentz quantum polynomial-time) and demonstrate its equivalence to the complexity class P♯P (the class of problems solvable by a deterministic polynomial-time Turing machine with access to a ♯P oracle). LQC algorithms are shown to solve NP-hard problems, such as the maximum independent set (MIS), in polynomial time, thereby placing NP and co-NP within BLQP. Furthermore, we establish that LQC can efficiently simulate quantum computing with postselection (PostBQP), while the reverse is not possible, highlighting LQC’s unique “super-postselection” capability. By proving BLQP =P♯P, we situate the entire polynomial hierarchy (PH) within BLQP and reveal profound connections between computational complexity and physical frameworks like Lorentz quantum mechanics. These results underscore LQC’s theoretical superiority over conventional quantum computing models and its potential to redefine boundaries in complexity theory.