The Path to Powerful Quantum Computers

E. V. Krishnamurthy

This paper studies the computational power of quantum computers to explore as to whether they can recognize properties which are in nondeterministic polynomial-time class (NP) and beyond. To study the computational power, we use the Feynman's path integral (FPI) formulation of quantum mechanics. From a computational point of view the Feynman's path integral computes a quantum dynamical analogue of the k-ary relation computed by an Alternating Turing machine (ATM) using AND-OR Parallelism. Hence, if we can find a suitable mapping function between an instance of a mathematical problem and the corresponding interference problem, using suitable potential functions for which FPI can be integrated exactly, the computational power of a quantum computer can be bounded to that of an alternating Turing machine that can solve problems in NP (e.g, factorization problem) and in polynomial space. Unfortunately, FPI is exactly integrable only for a few problems (e.g., the harmonic oscillator) involving quadratic potentials; otherwise, they may be only approximately computable or noncomputable. This means we cannot in general solve all quantum dynamical problems exactly except for those special cases of quadratic potentials, e.g., harmonic oscillator. Since there is a one to one correspondence between the quantum mechanical problems that can be analytically solved and the path integrals that can be exactly evaluated, we can say that the noncomputability of FPI implies quantum unsolvability. This is the analogue of classical unsolvability. The Feynman's path graph can be considered as a semantic parse graph for the quantum mechanical sentence. It provides a semantic valuation function of the terminal sentence based on probability amplitudes to disambiguate a given quantum description and obtain an interpretation in a linear time. In Feynman's path integral, the kernels are partially ordered over time (different alternate paths acting concurrently at the same time) and multiplied. The semantic valuation is computable only if the FPI is computable. Thus both the expressive power and complexity aspects quantum computing are mirrored by the exact and efficient integrability of FPI.

Anders Søndberg Sørensen

Sara Campbell

Prudhvi Naayini, Chiranjeevi Bura, Anil Kumar Jonnalagadda

The rapid evolution of computational technologies has led to the emergence of quantum computing as a powerful supplement to traditional distributed computing. The increasing complexity of machine learning workloads is pushing the limits of classical computing. This paper explores the synergistic potential of combining distributed and quantum computing to overcome these limitations and unlock new frontiers in artificial intelligence. We investigate how quantum algorithms can enhance the training of complex machine learning models within a distributed framework, enabling more accurate and efficient learning through quantum-accelerated data analysis. While challenges remain in hybrid quantum-classical integration and quantum hardware limitations, this convergence offers a promising path toward realizing the full potential of quantum machine learning. This paper highlights the path toward unprecedented computational power in AI.

Daniel J. Brod, Andrew M. Childs

Matchgates are a restricted set of two-qubit gates known to be classically simulable when acting on nearest-neighbor qubits on a path, but universal for quantum computation when the qubits are arranged on certain other graphs. Here we characterize the power of matchgates acting on arbitrary graphs. Specifically, we show that they are universal on any connected graph other than a path or a cycle, and that they are classically simulable on a cycle. We also prove the same dichotomy for the XY interaction, a proper subset of matchgates related to some implementations of quantum computing.